Enterprise Risk Management 2015 PDF

Abiliti: Future Systems


Throughout eternity, all that is of like form comes around again – everything that is the same must return in its own everlasting

cycle.....

• Marcus Aurelius – Emperor of Rome •

Many Economists and Economic Planners have arrived at the same conclusion - that most organisations have not yet widely adopted

sophisticated Business Intelligence and Analytics systems – let alone integrated BI / Analytics and “Big Data” outputs into their core Strategic

Planning and Financial Management processes.....


• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service Providers and Future Management Strategy Consulting firms for Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media

• Graham Harris Founder and MD @ Abiliti: Future Systems

– Email: (Office) – Telephone: (Mobile)

• Nigel Tebbutt 奈杰尔泰巴德

– Future Business Models & Emerging Technologies @ Abiliti: Future Systems – Telephone: +44 (0) 7832 182595 (Mobile) – +44 (0) 121 445 5689 (Office) – Email: [email protected] (Private)

• Ifor Ffowcs-Williams CEO, Cluster Navigators Ltd & Author, “Cluster Development” – Address : Nelson 7010, New Zealand (Office)

– Email : [email protected]

Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©

Cluster Theory - Expert Commentary: -

mailto:[email protected]





Slow is smooth, smooth is fast.....

.....advances in “Big Data” have lead to a revolution in

Economic Modelling and Enterprise Risk Management –

but it takes both human ingenuity, and time, for Strategic

Economic and Risk Models to develop and mature.....

Financial Technology – Business Categories

Fin Tech – Business Disciplines

Economic Analysis & Econometrics Regime: –

• Economic Planning, Analytics & Optimisation •

• Business Cycles, Patterns and Trends •

• Quantitative and Qualitative Techniques •

• Economic Modelling & Long-range Forecasting •

• Ghost in the Machine - Future Management •

Business Planning and Strategy Regime: –

• Corporate Planning and Financial Analysis •

• Horizon Scanning, Monitoring and Tracking •

• Eltville Model • Three Horizons Framework •

• The “Thinking about the Future” Framework •

Business Programme Management Regime: –

• Organisational Change Framework •

• Business Transformation Framework •

• Project / Programme Management Framework •

Enterprise & Solution Architecture Regime: –

• Business Architecture / Modelling Framework •

• Technology Architecture / Modelling Framework •

Fin Tech – Operational Regimes

Corporate Responsibility Regimes: –

• Business Principles Regime •

• Enterprise Governance Regime •

• Reporting and Controls Regime •

• Enterprise Risk Management Regime •

• Enterprise Performance Management Regime •

Enterprise Risk Frameworks: –

• Systemic Risk • Outsights •

• Operational Risk • COSO •

• Trade Risk (micro-economic) •

• Market Risk (macro-economic) •

Liquidity Risk Frameworks – Capital Adequacy Rules

• Basle II – Banking • Solvency II – Insurance •

Insurance Risk Frameworks: –

• Actuarial Science • Underwriting / Reinsurance Risk •

• Security Risk • Reputational Risk • Data Science •

Reporting and Controls Frameworks: –

• Accounting Standards • GAAP • IFRS •

Enterprise and Business Architecture is a part of Abiliti: Financial Technology (Fin Tech) Training: -

Financial Technology – System Categories

Fin Tech – Core Processing

Retail Banking

• Deposits

• Accounts

• Payments

• Securities • Wealth Management •

Financial Markets

• Trade Desk • Automatic Trading •

• Enterprise Risk Management

• Quantitative (Technical) Analysis

• Financial Market Data Management

• Regulatory and Statutory Compliance

Corporate Banking

• Corporate Finance

• Investment Services

• Asset Portfolio Management

• Merger and Acquisition Services

• Shareholder Registration and Administration

Fin Tech – Shared Services

Enterprise Support Systems (ESS): -

• Planning, Forecasting and Strategic Management

• Enterprise Performance Management

• Human Resources and Talent Management

• Finance & Accounting • Treasury & Settlements

• Enterprise Governance, Reporting and Controls

Business Support Systems (BSS)

• Customer Relationship Management •

• Social Media • BI / Analytics • “Big Data” •

• Mobile Devices and Smart Apps Platforms •

• Multi-channel Digital Self-service Platforms •

Operational Support Systems (OSS)

• Cloud Services

• Desktop Services

• Network Management

• Software Versioning and Control

• Software Distribution Management

Systems and Solution Architecture forms part of Abiliti: Financial Technology (Fin Tech) Training: -

At the very Periphery of Corporate Vision and Awareness…..

• The Cosmology Revolution – new and exciting advances in Astrophysics and Cosmology (String Theory and Wave Mechanics) is leading Physicists towards new questions and answers concerning the make-up of stellar clusters and galaxies, stellar populations in different types of galaxy, and the relationships between high-stellar populations and local clusters. What are the implications for galactic star-formation histories and relative stellar formation times – overall, resolved and unresolved – and their consequent impact on the evolution of life itself ?.

• The Quantum Revolution – The quantum revolution could turn many ideas of science fiction into science fact - from meta-materials with mind-boggling properties such as invisibility, limitless quantum energy via room temperature superconductors an onwards and upwards to Arthur C Clarke's space elevator. Some scientists even forecast that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules to produce everything from almost anything. How ultimately will we use this gift? Will we have the wisdom to match our mastery of matter like Solomon? Or will we abuse our technology strength and finally bring down the temple around our ears like Samson?

• The Nano-Revolution – To meet the challenges in an ever more resource-limited world, innovation and technology must play an increasing role. Nanotechnology, the engineering of matter at the atomic scale to create materials with unique properties and capabilities, will play a significant part in ensuring that risks to critical water resources for future cities are addressed. Nanotechnology “has the potential to be a key element in providing effective, environmentally sustainable solutions for supplying potable water for human use and clean water for agricultural and industrial uses.”


• The Energy Revolution • Oil Shale • Kerogen • Tar Sands • Methane Hydrate • The

Hydrogen Economy • Nuclear Fusion • Every year we consume the quantity of Fossil

Fuel energy which took nature 3 million tears to create. Unsustainable fossil fuel energy

dependency based on Carbon will eventually be replaced by the Hydrogen Economy

and Nuclear Fusion. The conquest of hydrogen technology, the science required to

support a Hydrogen Economy (to free up humanity from energy dependency) and

Nuclear Fusion (to free up explorers from gravity dependency) is the final frontier which,

when crossed, will enable inter-stellar voyages of exploitation across our Galaxy.

• Nuclear Fusion requires the creation and sustained maintenance of the enormous

pressures and temperatures to be found at the Sun’s core This is a most challenging

technology that scientists here on Earth are only now just beginning to explore and

evaluate its extraordinary opportunities. To initiate Nuclear Fusion requires creating the

same conditions right here on Earth that are found the very centre of the Sun. This

means replicating the environment needed to support quantum nuclear processes which

take place at huger temperatures and immense pressures in the Solar core – conditions

extreme enough to overcome the immense nuclear forces which resist the collision and

fusion of two deuterium atoms (heavy hydrogen – one proton and one neutron) to form a

single Helium atom – accompanied by the release of a vast amount of Nuclear energy.


• Renewable Resources • Solar Power • Tidal Power • Hydro-electricity • Wind Power • The Hydrogen Economy • Nuclear Fusion • Any natural resource is a renewable resource if it is replenished by natural processes at a rate compatible with or faster than its rate of consumption by human activity or other natural uses or attrition. Some renewable resources - solar radiation, tides, hydroelectricity, wind – can also classified as perpetual resources, in that they can never be consumed at a rate which is in excess of their long-term availability due to natural processes of perpetual renewal. The term renewable resource also carries the implication of prolonged or perpetual sustainability for the absorption, processing or re-cycling of waste products via natural ecological and environmental processes.

• For the purposes of Nuclear Fission, Thorium may in future replaced enriched Uranium-235. Thorium is much more abundant, far easier to mine, extract and process and far less dangerous than Uranium. Thorium is used extensively in Biomedical procedures, and its radioactive decay products are much more benign.

• Sustainability is a characteristic of a process or mechanism that can be maintained indefinitely at a certain constant level or state – without showing any long-term degradation, decline or collapse.. This concept, in its environmental usage, refers to the potential longevity of vital human ecological support systems - such as the biosphere, ecology, the environment the and man-made systems of industry, agronomy, agriculture, forestry, fisheries - and the planet's climate and natural processes and cycles upon which they all depend.


• Trans-humanism – advocates the ethical use of technology to extend current human form and function - supporting the use of future science and technology to enhance the human genome capabilities and capacities in order to overcome undesirable and unnecessary aspects of the present human condition.

• The Intelligence Revolution – Artificial Intelligence will revolutionise homes, workplaces and lifestyles. Augmented Reality will create new virtual worlds – such as the interior of Volcanoes or Nuclear Reactors, the bottom of the Ocean or the surface of the Moon, Venus or Mars - so realistic they will rival the physical world. Robots with human-level intelligence may finally become a reality, and at the ultimate stage of mastery, we'll even be able to merge human capacities with machine intelligence and attributes – via the man-machine interface.

• The Biotech Revolution – Genome mapping and Genetic Engineering is now bringing doctors and scientists towards first discovery, and then understanding, control, and finally mastery of human health and wellbeing. Digital Healthcare and Genetic Medicine will allow doctors and scientists to positively manage successful patient outcomes – even over diseases previously considered fatal. Genetics and biotechnology promise a future of unprecedented health, wellbeing and longevity. DNA screening could diagnose and gene therapy prevent or cure many diseases. Thanks to laboratory-grown tissues and organs, the human body could be repaired as easily as a car, with spare parts readily available to order. Ultimately, the ageing process itself could ultimately be slowed or even halted.


• Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and dystopian views of the emerging world future state, in which climate, the environment, ecology and even geology are dominated by the indirect impact of human activity and the direct impact of human manipulation: –

1. Human Impact is now the major factor in climate change, environmental and

ecological degradation.

2. Environmental Degradation - man now moves more rock and earth than do all of the natural geological processes

3. Ecological Degradation – biological extinction rate - is currently greater than that of the Permian-Triassic boundary (PTB) extinction event

4. Food, Energy, Water (FEW) Crisis – increasing scarcity of Natural Resources

• Society’s growth-associated impacts on its own ecological and environmental support systems, for example intensive agriculture causing exhaustion of natural resources by the Mayan and Khmer cultures, de-forestation and over-grazing causing catastrophic ecological damage and resulting in climatic change – further examples are the Easter Island culture, the de-population of upland moors and highlands in Britain from the Iron Age onwards – including the Iron Age retreat from northern and southern English uplands, the Scottish Highland Clearances and replacement of subsistence crofting by deer and grouse for hunting and sheep for wool on major Scottish Highland Estates and the current sub-Saharan de-forestation and subsequent desertification by semi-nomadic pastoralists

Ghost in the Machine: Haunted by Randomness

“Time present and time past Are both perhaps present in time future, And time future contained in time past

. . . all time is eternally present”

• Time, Eternity, and Immortality in T. S. Eliot's Four Quartets •

Ghost in the Machine.....


• The purpose of a Futures Study Training Module is based on the overarching need to

enable and prepare clients to anticipate, prepare for and manage the future - by guiding them

towards an understanding of how the future might unfold. This involves planning, organising

and running Futures Studies Projects and presenting the results via Workshops, Seminars

and CxO Forums. This means working with key client executives responsible for Stakeholder

Relationships, Communications and Benefits Realisation Strategies - helping to influence and

shape organisational change and driving technology innovation to enable rapid business

transformation, ultimately to facilitate the achievement of stakeholder’s desired Business

Outcomes – plus the scoping, envisioning and designing the Future Systems to support

client objectives – by integrating BI / Analytics and “Big Data” Futures Study and Strategy

Analysis outputs into their core Corporate Planning and Financial Management processes.....

– CxO Forums – executive briefings on new and emerging technologies and trends

– Workshops – discovery workshops to explore future Scenario Planning & Analysis

– Seminars – presents in detail the key Futures Study findings and extrapolations.

– Special Interest Groups (SIGs) – for stakeholder Subject Matter Experts (SMEs)


• This Futures Study Training Module – is designed to provide cross-functional support to those client stakeholders who are charged by their organisations with thinking about the future – corporate planners, disaster and contingency management and enterprise risk research, planning, strategy, analysis and management along with those IT Professionals responsible for Strategic Enterprise Management (SEM) Frameworks and Systems. The Futures Study course consists of the following components : -

– Classroom Training – Slide Pack, Handouts, Background Documents, Tests and Exercises.

– Workshop Facilitation – driving and mentoring Futures Studies Workshops.

– Advisory Consulting – advise and inform your Futures Study Programme.

– CxO Forums – executive briefings on new and emerging technologies and trends

– Future Discovery – discovery workshops to explore future Scenario Planning & Analysis

– Seminars – presents in detail the key Futures Study findings and extrapolations.

– Special Interest Groups (SIGs) – for stakeholder Subject Matter Experts (SMEs)

– Resources – access to Think Tanks, NGOs, Government Departments and Academia.

– Gateway to Higher Education – Graduate Courses in Futures Studies @ University of Oxford – Said Business School and Smith School of Economics and the Environment (SSEE)


• This Slide Pack forms part of a Futures Study Training Module - the purpose of which is to provide

cross-functional support to those client stakeholders who are charged by their organisations with

thinking about the future – corporate planners, disaster and contingency management and enterprise

risk research, planning, strategy, analysis and management along with IT Professionals responsible for

architecting, designing and supporting Strategic Enterprise Management Frameworks and Systems: -

– Finance, Corporate Planners and Strategists – authorise and direct the Futures Study.

– Enterprise Risk Managers, Disaster & Contingency Planners – plan & lead Futures Studies.

– Product Innovation, Research & Development – advise and inform the Futures Study.

– Marketing and Product Engineering – review and mentor the Futures Research Study.

– Economists, Data Scientists and Researchers – undertakes the detailed Research Tasks.

– Research Aggregator – examines hundreds of related Academic Papers, “Big Data” & other

relevant global internet content - looking for hidden or missed findings and extrapolations.

– Author – compiles, documents, edits and publishes the Futures Study Research Findings.

– Business Analysts / Enterprise Architects – provide the link into Business Transformation.

– Technical Designers / Solution Architects – provide the link into Technology Refreshment.

Executive Summary: - The Management of Uncertainty

Mechanical Processes –

Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures

Wave Mechanics (String Theory) – integrates the behaviour of every size and type of object

The Management of Uncertainty

• It has long been recognized that one of the most important competitive factors for any

organization to master is the management of uncertainty. Uncertainty is the major

intangible factor contributing towards the risk of failure in every process, at every level,

in every type of business. The way that we think about the future must mirror how the

future actually unfolds. As we have learned from recent experience, the future is not a

straightforward extrapolation of simple, single-domain trends. We now have to consider

ways in which the possibility of random, chaotic and radically disruptive events may be

factored into enterprise threat assessment and risk management frameworks and

incorporated into decision-making structures and processes.

• Managers and organisations often aim to “stay focused” and maintain a narrow

perspective in dealing with key business issues, challenges and targets. A

concentration of focus may risk overlooking Weak Signals indicating potential issues

and events, agents and catalysts of change. Such Weak Signals – along with their

resultant Wild Card and Black Swan Events - represent early warning of radically

disruptive future global transformations – which are even now taking shape at the very

periphery of corporate awareness, perception and vision – or just beyond.


• There are many kinds of Stochastic or Random processes that impact on every area

of Nature and Human Activity. Randomness can be found in Science and Technology

and in Humanities and the Arts. Random events are taking place almost everywhere

we look – for example from Complex Systems and Chaos Theory to Cosmology and

the distribution and flow of energy and matter in the Universe, from Brownian motion

and quantum theory to fractal branching and linear transformations. There are further

examples – atmospheric turbulence in Weather Systems and Climatology, and system

dependence influencing complex orbital and solar cycles. Other examples include

sequences of Random Events, Weak Signals, Wild Cards and Black Swan Events

occurring in every aspect of Nature and Human Activity – from the Environment and

Ecology - to Politics, Economics and Human Behaviour and in the outcomes of current

and historic wars, campaigns, battles and skirmishes - and much, much more.

• These Stochastic or Random processes are agents of change that may precipitate

global impact-level events which either threaten the very survival of the organisation -

or present novel and unexpected opportunities for expansion and growth. The ability to

include Weak Signals and peripheral vision into the strategy and planning process may

therefore be critical in contributing towards the continued growth, success, wellbeing

and survival of both individuals and organisations at the micro-level – as well as cities,

states and federations at the macro-level - as witnessed in the rise and fall of empires.


Random Processes

• Random Processes may influence any natural and human phenomena, such as: -

– the history of an object

– the outcome of an event

– the execution of a process

• Randomness may be somewhat difficult to demonstrate, as true Randomness in chaotic

system behaviour is not always readily or easily distinguishable from any of the “noise”

that we may find in Complex Systems – such as foreground and background wave

harmonics, resonance and interference. Complex Systems may be influenced by both

internal and external factors which remain hidden – either unrecognised or unknown.

These hidden and unknown factors may exist far beyond our ability to detect them – but

nevertheless, still exert influence. The existence of weak internal or external forces acting

on systems may not be visible to the observer – these subliminal temporal forces can

influence Complex System behaviour in such a way that the presence of imperceptibly tiny

inputs, acting on a system, amplified in effect over many system cycles - are ultimately

able to create massive observable changes to outcomes in complex system behaviour.


• Uncertainty is the outcome of the disruptive effect that chaos and randomness

introduces into our daily lives. Research into stochastic (random) processes looks

towards how we might anticipate, prepare for and manage the chaos and uncertainty

which acts on complex systems – including natural systems such as Cosmology and

Climate, as well as human systems such as Politics and the Economy – so that we may

anticipate future change and prepare for it…..

1. Classical Mechanics - Any apparent randomness is as a result of Unknown Forces

2. Thermodynamics - Randomness, chaos and uncertainty is directly a result of Entropy

3. Biology - Any apparent randomness is as a result of Unknown Forces

4. Chemistry - Any apparent randomness is as a result of Unknown Forces

5. Atomic Theory - All events are utterly and unerringly predictable (Dirac Equation)

6. Quantum Mechanics - Every event is both symmetrical and random (Hawking Paradox)

7. Geology - Any randomness or asymmetry is a result of Unknown Forces

8. Astronomy - Any randomness or asymmetry is a result of Unknown Forces

9. Cosmology - Any randomness or asymmetry is as a result of Dark Matter, Energy, Flow

10. Relativity Theory - Randomness or asymmetry may be a result of Quantum effects

11. Wave Mechanics - Any randomness and asymmetry is as a result of Unknown Forces


Domain Scope / Scale Randomness Pioneers

Classical Mechanics

(Newtonian Physics)

Everyday objects Any apparent randomness is as

a result of Unknown Forces

Sir Isaac Newton

Thermodynamics Energy Systems -

Entropy, Enthalpy

Newcomen, Trevithick,

Watt, Stephenson

Biology Evolution Darwin, Banks, Huxley,

Krebs, Crick, Watson

Chemistry Molecules Lavoisier, Priestley

Atomic Theory Atoms Events are truly and intrinsically,

utterly and unerringly totally

predictable (Dirac Equation).

Max Plank, Niels Bohr,

Bragg, Paul Dirac,

Richard Feynman

Quantum Mechanics Sub-atomic particles Each and every Quantum event

is truly and intrinsically fully

random and symmetrical

(Hawking Paradox)

Erwin Schrodinger ,

Werner Heisenberg,

Albert Einstein,

Hermann Minkowsky


Domain Scope / Scale Randomness Pioneers

Geology The Earth, Planets,

Planetoids, Asteroids,

Meteors / Meteorites

Any apparent randomness is as

a result of Unknown Forces

Hutton, Lyell, Wagner

Astronomy Common, Observable

Celestial Objects

Any apparent randomness or

asymmetry may be as a result

of Quantum effects or other

Unknown Forces acting early in

the history of Space-Time

Galileo, Copernicus,

Kepler, Lovell, Hubble

Cosmology Super-massive

Celestial Objects

Hoyle, Ryall, Rees,

Penrose, Bell-Burnell

Relativity Theory The Universe

Any apparent randomness or

asymmetry is as a result of

Unknown Forces / Dimensions

Albert Einstein,

Hermann Minkowski,

Stephen Hawking

Wave Mechanics

(String Theory or

Quantum Dynamics)

The Universe,

Membranes and

Hyperspace

Michael Green,

Michio Kaku


• Classical Mechanics (Newtonian Physics)

– Classical Mechanics (Newtonian Physics) governs the behaviour of everyday objects

– any apparent randomness is as a result of unimaginably small, unobservable and

unmeasurable Unknown Forces - either internal or external - acting upon a System.

• Thermodynamics

– governs the flow of energy and the transformation (change in state) of systems

– randomness, chaos and uncertainty is the result of the effects of Enthalpy and Entropy

• Chemistry

– Chemistry (Transformation) governs the change in state of atoms and molecules



• Biology

– Biology (Ecology ) governs Evolution - the life and death of all living Organisms




• Atomic Theory

– governs the behaviour of unimaginably small objects (atoms and sub-atomic particles)

– all events are truly and intrinsically, utterly and unerringly predictable (Dirac Equation).

• Quantum Mechanics

– governs the behaviour of unimaginably tiny objects (fundamental sub-atomic particles)

– all events are truly and intrinsically both symmetrical and random (Hawking Paradox).

• Geology

– Geology governs the behaviour of local Solar System Objects (such as The Earth, Planets,

Planetoids, Asteroids, Meteors / Meteorites) which populate the Solar System


unmeasurable Unknown Forces - either internal or external - acting upon a System

• Astronomy

– Astronomy governs the behaviour of Common, Observable Celestial Objects (such as

Asteroids, Planets, Stars and Stellar Clusters) which populate and structure Galaxies

– any apparent randomness or asymmetry is as a result of Quantum Effects, Unknown

Forces or Unknown Dimensions acting very early in the history of Universal Space-Time


• Cosmology

– Cosmology governs the behaviour of impossibly super-massive cosmic building blocks

(such as Galaxies and Galactic Clusters) which populate and structure the Universe

– any apparent randomness or asymmetry is due to the influence of Quantum Effects,

Unknown Forces (Dark Matter, Dark Flow and Dark Energy) or Unknown Dimensions

• Relativity Theory

– Relativity Theory governs the behaviour of impossibly super-massive cosmic structures

(such as Galaxies and Galactic Clusters) which populate and structure the Universe


Forces or Unknown Dimensions acting very early in the history of Universal Space-Time

• Wave Mechanics (String Theory or Quantum Dynamics)

– Wave Mechanics integrates the behaviour of every size and type of physical object


Forces or Unknown Dimensions acting on the Universe, Membranes or in Hyperspace

• 4D Geospatial Analytics is the

profiling and analysis of large

aggregated datasets in order to

determine a ‘natural’ structure of

groupings provides an important

technique for many statistical and

analytic applications.

• Environmental and Demographic

Geospatial Cluster Analysis - on the

basis of profile similarities or

geographic distribution - is a statistical

method whereby no prior assumptions

are made concerning the number of

groups or group hierarchies and

internal structure. Geo-spatial and

geodemographic techniques are

frequently used in order to profile and

segment populations by ‘natural’

groupings - such as common

behavioural traits, Clinical Trial,

Morbidity or Actuarial outcomes - along

with many other shared characteristics

and common factors.....


• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of

Geospatial “Big Data” – Geospatial Analytics simultaneously within a Time (history) and Space

(geographic) context. The problems encountered in exploring and analysing vast volumes of

spatial–temporal information in today's data-rich landscape – are becoming increasingly

difficult to manage effectively. In order to overcome the problem of data volume and scale in a

Time (history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method of

Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) framework which is communicated via data visualisation and animation techniques

used to support geo-visual “Big Data” analytics - thus improving the accessibility, exploration

and analysis of the huge amounts of time-variant geo-spatial data, such as the history of an

object or location, or the outcome of a process (evolution of the universe). Temporal Wave

combines the strengths of both linear timeline and cyclical wave-form analysis . Both linear

and cyclic trends in space-time data may be represented in combination with other graphic

representations typical for location–space and attribute–space data-types. The Temporal

Wave can be used in various roles as a time–space data reference system, as a time–space

continuum representation tool, and as time–space interaction tool– and so is able to represent

data within both a Time (history) and Space (geographic) context simultaneously – therefore

pan across Space-time layers or even zoom between different levels of detail or granularity.



• Time Present is always in some way inextricably woven into both Time Past and Time Future –

with the potential, therefore, to give us notice of future random events – subliminal indications

of future events before they actually occur. Chaos Theory suggests that even the most tiny of

inputs, so minute as to be undetectable, may ultimately be amplified over many system cycles

– to grow in influence and effect to trigger dramatic changes in future outcomes. So any given

item of Information or Data (Global Content) may contain faint traces which hold hints or clues

about the outcomes of linked Clusters of Past, Present and Future Events.

• Every item of Global Content that we find in the Present is somehow connected with both the

Past and the Future. Space-Time is a Dimension – which flows in a single direction, as does a

River. Space-Time, like water diverted along an alternative river channel, does not flow

uniformly – outside of the main channel there could well be “submerged objects” (random

events) that disturb the passage of time, and may possess the potential capability of creating

unforeseen eddies, whirlpools and currents in the flow of Time (disorder and uncertainty) –

which in turn posses the capacity to generate ripples, and waves (chaos and disruption) – thus

changing the course of the Space-Time continuum. “Weak Signals” are “Ghosts in the

Machine” of these subliminal temporal interactions – with the capability to contain information

about future “Wild card” or “Black Swan” random events.


• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a sequence of waves linked and integrated in ascending order of magnitude, which have a common source or origin - either a single Random Event instance or arising from a linked series of chaotic and disruptive Random Events - an Event Storm. These Random Events propagate through the space-time continuum as a related and integrated series of waves with an ascending order of magnitude and impact – the first wave to arrive is the fastest travelling,- Weak Signals - something like a faint echo of a Random Event which may in turn be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which may indicate the unfolding a further increase in magnitude and intensity which finally arrives catastrophically - something like a tsunami (Black Swan Event).

Sequence of Events - Emerging Waves Stage View of Wave Series Development

1. Random Event 1. Discovery

2. Weak Signals 1.1 Establishment

3. Strong Signals 1.2 Development

4. Wild Cards 2. Growth

5. Black Swan Event 3. Plateau

4. Decline

5. Collapse

5.1 Renewal

5.2 Replacement


• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic

models currently available to us today - seem able to deal with the concept or impact of

Random Events (uncertainty). We therefore need to consider and factor in further novel

and disruptive (systemic) approaches which offer us the possibility to manage uncertainty.

We can do this by searching for, detecting and identifying Weak Signals – which are tiny,

unexpected variations or disturbances in system outputs – surprises – predicating the

possible existence of hidden data relationships which are masked or concealed within the

general background system “noise”. Weak Signals are caused by the presence of small

unrecognised or unknown forces acting on the system. Weak Signals in turn may indicate

the possible future appearance of emerging chaotic, and radically disruptive Wild Card or

Black Swan events beginning to form on the detectable Horizon – or even just beyond.

• Random Events must then be factored into Complex Systems Modelling. Complex

Systems interact with unseen forces – which in turn act to inject disorder, randomness,

uncertainty, chaos and disruption. The Global Economy, and other Complex Adaptive

Systems, may in future be considered and modelled successfully as a very large set of

multiple interacting Ordered (Constrained) Complex Systems - each individual System

loosely coupled with all of the others, and every System with its own clear set of rules and

an ordered (restricted) number of elements and classes, relationships and types.

Enterprise Risk Management



Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures



Introduction

• Enterprise Risk Management (ERM) has a wide spectrum of scope

and definitions. The generally agreed concept is that ERM is now

much wider than traditional risk management and covers all of the

risks within an enterprise (public and private sector). Traditional risk

management focuses on identifying risks, measuring and monitoring

risks and designing strategies to limit losses to agreed limits.

• ERM recognises that businesses take risks in order to make a profit

for their owners and therefore considers the upside of taking risks, and

attempts to strike a balance between too much risk and not enough

risk compared to the enterprise’s strategic direction. Risk is managed

holistically in a fully integrated framework, across all different risk

types and the different functions/companies within the organisation.

Risk

“The bear that

you can see in

front of you –

is never the

same bear as

the one which

takes your life

away.....”

Inuit Proverb

Risk

Advances in Data

Science and “Big

Data” have lead to

a revolution in

macro and micro

Econometrics

Modelling, Threat

Analysis and

Enterprise Risk

Management .....

– but it takes both

human ingenuity,

time and effort for

Austrian (Real)

Economic and

Enterprise Risk

Models to develop

and mature.....

Section 1 – Introduction to Enterprise Risk Management

• This Section describes the fundamentals of Enterprise Risk Management Threat Analysis. The

underlying premise of Enterprise Risk Management is that every enterprise exists to provide value for

its stakeholders. All entities face uncertainty, which leads to risk. The challenge for management is to

determine how much uncertainty or risk to accept, as it strives to protect and grow stakeholder value : -

• AUDIENCE – Finance, Corporate Planners and Strategists – authorise and direct the Risk Study.

– Enterprise Risk Managers, Disaster & Contingency Planners – plan and lead the Risk Study.

– Product Innovation, Research & Development – advise and inform the Risk Research Study.

– Marketing and Product Engineering – review and mentor the Risk Research Study.

– Economists, Data Scientists and Researchers – undertakes detailed Risk Research Tasks.

– Research Aggregator – “Big Data”: - examines hundreds of related Academic Papers and other

global internet content - looking for hidden or missed findings and extrapolations – Data Science.

– Author – compiles, documents, edits and publishes the Risk Research Study Findings.




Enterprise Risk Management – Key Issues

• The underlying premise of Enterprise Risk Management is that every enterprise exists to provide sustainable value for its stakeholders.

• All entities face random events and uncertainty, and the challenge for management is to determine how much uncertainty they are willing to accept as the Enterprise strives to grow stakeholder value.

• Randomness and uncertainty presents both risk and opportunity, with the potential to either erode or enhance short-term stakeholder value.

• Enterprise Risk Management enables leadership to deal effectively with random events and uncertainty along with its associated risk and opportunity – enhancing the capacity of the Enterprise to achieve sustainable growth and conserve long-term stakeholder value


• The underlying premise of Enterprise Risk Management is that every enterprise exists

to generate value for its stakeholders. All entities face uncertainty, which leads to risk.

The challenge for management is to determine its risk appetite - how much uncertainty

to accept as it strives to protect and grow stakeholder value. Uncertainty presents both

threats and opportunities – with the potential to either erode or enhance stakeholder

value. Enterprise Risk Management enables leadership to deal effectively with

randomness and uncertainty along with its associated risk and opportunity – thus

enhancing capacity to build sustainable growth and long-term stakeholder value.

• Enterprise Risk Management value is maximised when leadership and management teams sets policy, strategy and objectives to strike an optimal balance between growth and return on investment - with their related goals and risks - deploying resources efficiently and effectively in pursuit of the enterprise’s desired future outcomes.

• These capabilities inherent in enterprise risk management help the leadership team to achieve the enterprise’s performance and profitability targets whilst preventing the loss, attrition or devaluation of enterprise resources – and in so doing, protecting and preserving corporate assets. Enterprise Risk Management helps to ensure effective reporting and compliance with laws and regulations, and helps avoid damage to the enterprise’s reputation - and any consequential losses. In sum, enterprise risk management helps an enterprise to realise its corporate plans and business strategies - avoiding pitfalls and surprises along the way.


• Risk Events – Threats and Opportunities. Risk Events can have negative impact, positive impact, or both. Events with a negative impact represent risks, which can prevent value creation or erode existing value. Events with positive impact may offset negative impacts or represent opportunities. Opportunities are the possibility that an event will occur and positively affect the achievement of objectives, supporting value creation or preservation. Management channels opportunities back to its strategy or objective-setting processes, formulating plans to seize those opportunities.

• Enterprise Risk Management deals with risks and opportunities affecting the process of value creation or preservation – and is described as follows: -

– Enterprise Risk Management is a process, implemented by an enterprise’s board of directors, leadership, management and other personnel, and is applied both in a strategy setting and in every operational activity across the entire enterprise. Enterprise Risk Management is designed to identify potential threat events that may affect the enterprise, to manage those threats within its risk appetite and tolerances – and to provide reasonable comfort and assurance towards the achievement of operational and strategic enterprise objectives.

• This Enterprise Risk Management definition is purposefully broad. It captures key concepts fundamental to how companies and other organizations manage risk, providing a basis for application across organizations, industries, and sectors. It focuses directly on achievement of objectives established by a particular enterprise and provides a basis for defining enterprise risk management effectiveness.


• This definition reflects fundamental Enterprise Risk Management concepts: -

– A process set or group, ongoing and flowing through an entire enterprise

– Implemented by people at every level within an organisation

– Supported by technology - Enterprise Risk Management Systems

– Developed in a strategy setting, planning, forecasting and implemented by operational management

– Applied across the whole enterprise, at every segment and unit, and includes taking an enterprise level portfolio view of risk

– Designed to identify potential events that, if they occur, will affect the enterprise and to manage risk within its risk appetite

– Able to provide reasonable and acceptable Risk Management assurance to an enterprise’s senior management and board of directors

– Geared to the achievement of performance objectives in many separate but related categories

• This definition is purposefully broad. It captures key concepts fundamental to how companies and other organizations manage risk, providing a basis for application across organizations, industries, and sectors. It focuses directly on achievement of objectives established by a particular enterprise and provides a basis for defining your own organisations specific Enterprise Risk Management Framework.

Primary Risk Functions

• The Primary Risk Functions in large corporations that may participate in an Enterprise Risk Management programme typically include the following: -

– Strategic planning and forecasting - identifies competitive opportunities and external threats, along with strategic initiatives to exploit or address them

– Disaster and contingency planning - identifies business continuity issues

– Research and Development - understands core value propositions to ensure that future product / service development falls within corporate requirements

– Marketing and Product Engineering - understands the target customer to ensure product / service alignment within customer expectations and needs

– Finance and Accounting - identifies business performance management issues

– Actuarial Services - ensures the proper insurance cover for the organisation

– Treasury - ensures cash-flow is sufficient to meet business needs, whilst managing risk related to commodity pricing, interest and foreign exchange


– Financial Compliance – follows GAAP / IFRS recommendations and directs Sarbanes-Oxley Section 302 and 404 assessments, in addition to Basle II / Solvency II compliance - which identifies financial reporting and disclosure risks.

– Legal Services - manages litigation and analyses emerging government policy, legislation and regulation that may have future impact upon the organisation

– Regulatory and Statutory Compliance – provides governance and controls, monitors compliance with standards and initiates money laundering and fraud investigations - as well as dealing with Reputational Risk issues

– Quality Assurance - verifies operational quality assurance targets are achieved

– Operations Management – ensures that day-to-day operational performance is on target and that any operational issues are surfaced for resolution

Primary Risk Functions (continued)


– Credit Management - ensures that any credit facilities provided to customers is appropriate in respect of their Credit History and ability to repay the advance

– Customer Services – manages the customer experience / journey and ensures that problems are handled promptly and reported to operations for resolution

– Information Technology – follows Clinger-Cohen guidelines for due diligence in IT Procurement, implements Business Intelligence, “Big Data” Intelligent Agents / Alerts, Digital Dashboards and Reporting for Risk Controls and maintains Risk Event Identification / Incident Capture Systems for Risk Monitoring / Reporting

– Internal audit - evaluates Risk Event Identification / Incident Capture and Risk Controls; directs non-compliance / fraud investigation, monitoring and reporting

– Risk Management – maintains the Enterprise Risk Management Framework , audits and evaluates the effectiveness of each of the above risk functions and recommends any required improvements

Primary Risk Functions (continued)


• What is Risk Management ?

• Enterprise Risk Management is a structured approach to managing uncertainty through foresight and planning. Any risk is related to a specific threat (or group of related threats) managed through a sequence of activities using various resources: -

– Risk Research – evaluating / understanding the problem / opportunity domain

– Risk Identification – identifying applicable threats, risk groups, types & events

– Risk Prioritisation – ordering and prioritising relevant threats by risk probability

and magnitude

– Risk Assessment – comparing and balancing the individual threat posed by

each risk item in the ordered and prioritised risk register

– Risk Management Strategies – methods for transferring, avoiding, reducing or

accepting the risk

– Risk Planning – assessing the overall level of threat contained within the

consolidated risk register

– Risk Mitigation – reducing uncertainty through the application of strategic

foresight and future management planning processes


• Risk Management Strategies may include the following: -

– Transferring the risk to another party

– Avoiding the risk

– Reducing the negative effect of the risk

– Accepting part or all of the consequences of a particular risk .

• In an ideal Risk Management Scenario, a prioritisation process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - and risks with lower probability of occurrence and lower consequential losses are then handled in descending order

• In practice this prioritisation process can be very challenging. Comparing and balancing the overall threat of risks with a high probability of occurrence but lower loss - versus risks with higher potential loss but lower probability of occurrence - may lead to misleading results.....

Intangible Risk Management

• Intangible Risk Management hypothesises a different type of threat - a risk that has

a 100% probability of occurring but is ignored by the organization due to an inability

to recognise an unavoidable threat, or the failure to identify an intangible risk: -

– Process-engagement Risk may pose a threat when processes are ineffective,

incomplete or broken and operational procedures are misapplied (or not

applied).

– Knowledge Risk may materialise when insufficient knowledge is available in a

threat domain, or a deficient level of knowledge is applied to a threat situation,.

– Relationship Risk may occur when group dynamics are disrupted, morale

breaks down, or communication, collaboration and team-working become

ineffective.

• Intangible Risk Management allows risk managers to create immediate value from

the identification and reduction of hidden risks that reduce productivity.

• Such Intangible Risks may reduce the productivity of knowledge workers, decrease

cost effectiveness, erode profitability and service and quality whilst compromising

reputation, brand value, market share and earnings.

Opportunity Cost Management

• Risk Management Strategies also face operational difficulties in providing sufficient enterprise resources or allocating those resources appropriately. This is the concept of Opportunity Cost and may constitute: -

– Resources denied to risk management that could have been deployed more profitably on managing and avoiding risk.

– Resources over-expended on risk management that could have been spent elsewhere in the business on more profitable applications.

• Ideal Risk Management Scenarios minimizes spending whilst maximizing the reduction of the organisational impact and negative effects of such risks.

– Prioritisation ranks those risks with the greatest potential loss and / or the

greatest probability of occurrence -to be treated first

– Those Risks with lower probability of occurrence and lower consequential losses

are then handled in descending order

– Risk Management seeks to balance and optimise the overall threat impact of

risks with a high probability of occurrence but lower loss -versus risks with

greater potential loss but lower probability of occurrence

Town Flood Risk Example

A Trigger A

Risk

Event

B

Trigger B

Risk Event

FLOOD

Upstream

Dam Bursts Flood

Defences Fail

B Risk

Event

Infrastructure Destroyed

Property Damaged

B Risk

Event Loss of Life

B Risk

Event Personal Injury

Mitigation Factor

Mitigation Factor

Mitigation Factor

Mitigation Factor

Engineering

Services

Emergency Services

Rescue Response

Paramedic

Response


• Aligning risk appetite and risk management strategy – Management considers the enterprise’s capability to absorb risk (risk appetite) in evaluating strategic alternatives, setting related objectives, and developing mechanisms to manage related risk groups.

• Enhancing risk response decisions – Enterprise Risk Management provides the rigor to identify and select among alternative risk scenarios and responses –identification and assessment of threats, risk avoidance, risk reduction, risk sharing and risk acceptance.

• Reducing operational surprises and losses – Entities gain enhanced capability to identify potential threat events and establish threat responses - reducing their exposure to surprises and “black swan” events and their associated unplanned costs or losses.

• Identifying and managing multiple and cross-enterprise risks – Every enterprise faces a myriad of risks affecting different parts of the organization, and Enterprise Risk Management facilitates effective response to the interrelated impacts, and integrated management of multiple threat scenarios and exposure to groups of related risks.

• Seizing opportunities – By considering and mitigating a full range of potential threat events, management is well positioned to identify and proactively realise opportunities.

• Improving deployment of capital – Obtaining robust risk exposure information allows management to effectively assess overall capital needs and enhance capital allocation.

Risk Clusters and Connectivity

1

2

3

4

5

7

8

6

The above is an illustration of risk relationships - how risk events might be connected. A detailed and

intimate understanding of risk clusters and the connection between risks may help us to understand: -

• What is the relationship between Risks 1 and 8, and what impact do they have on Risks 2 - 7 ?

• Risks 2 - 5 and Risks 6 and 7 occur in clusters – what are the factors influencing these clusters ?

Answering questions such as these allows us to plan our risk management approach and mitigation

strategy – and to decide how to better focus our resources and effort on risk and fraud management.

Claimant 1

Risk Event

Claimant 2 Residence

Vehicle

Risk

Cluster


• Aggregated risk includes coincident, related, connected and interconnected risk: -

• Coincident - two or more risks appear simultaneously in the same domain – but

they arise from different triggers (unrelated causal events)

• Related - two more risks materialise in the same domain sharing common risk

features or characteristics (may share a possible hidden common trigger or cause

– and so are candidates for further analysis and investigation)

• Connected - two more risks materialise in the same domain due to the same

trigger (common cause)

• Interconnected - two more risks materialise together in a risk cluster or event

series - the previous (prior) risk event triggering the subsequent (next) risk event

• Aggregated risks may result in a significant cumulative impact - and are therefore

frequently identified incorrectly as Wild-card or Black Swan Events - rather than just

simply as risk clusters or event “storms”.....

Aggregated Risk

A Trigger A

Coincident Risk

B Trigger B

Risk Event

Risk Event

C Trigger

Related Risk

D Trigger

Risk Event

Risk Event

E

Trigger

Connected Risk

Risk Event

Risk Event F

G Trigger

Inter-connected Risk

Risk Event

Risk Event

H

Trigger D

USA Sub-Prime Mortgage Crisis

Trigger F

CDO Toxic Asset Crisis

K

E Trigger

K Sovereign

Debt Crisis

B Trigger

I

Money

Supply

Shock

C Trigger

H

Financial

Services

Sector

Collapse

D Trigger

G

L

A Trigger

J

Credit

Crisis

Global

Recession

Black Swan Events

Definition of a “Black Swan” Event

• A “Black Swan” Event is an event or

occurrence that deviates beyond what is

normally expected of any given situation

and that would be extremely difficult to

predict. The term “Black Swan” was

popularised by Nassim Nicholas Taleb, a

finance professor and former Investment

Fund Manager and Wall Street trader.

• Black Swan Events – are unforeseen,

sudden and extreme change events or

Global-level transformations in either the

military, political, social, economic or

environmental landscape. Black Swan

Events are a complete surprise when

they occur and all feature an inordinately

low probability of occurrence - coupled

with an extraordinarily high impact when

they do happen (Nassim Taleb). “Black Swan” Event Cluster or “Storm”

Risk Management Frameworks

Throughout eternity, all that is of like form comes around again –

everything that is the same must return again in its own

everlasting cycle.....


Section 3 – Risk Management Framework Design

• This Section describes how to design an Enterprise Risk Management Framework – a set of

processes, data, systems and technology designed to manage, control and be resilient to the impact of

every type of risk event and which facilitate rapid and agile business transformation in order to deliver

the client stakeholders desired future organisational structure and target business operating model : -












Risk Management Framework Design – Key Issues

• Enterprise Risk Management Frameworks are a set of processes, data, systems

and technology which help to manage and control every type of risk event.

• Enterprise Risk Management Frameworks facilitate rapid and agile business

transformation in order to deliver the clients desired future organisational structure

and target business operating model which are resilient to the impact of risk

• Enterprise Risk Management Frameworks therefore ensure Critical Success

factors such as enterprise governance, reporting and controls, disaster planning and

recovery management, business continuity, statutory and regulatory compliance

• The Enterprise Risk Management Framework can easily be implemented using

Amphora Symphony supported by SAP modules - SAP HANA, Business Objects,

EPM, GRC, SEM, TRM. There are also Oracle and Microsoft options.....

Threat Analysis, Hazard Research and Risk Management

The Nature of Uncertainty – Randomness

Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems randomness is as a result of Unknown Forces.....

Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects – any apparent randomness is as a result of Unknown Forces.....

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic objects – all events are truly and intrinsically both symmetrical and random.....

Relativity Theory – governs the behaviour of impossibly super-massive cosmic objects – any apparent randomness or asymmetry is as a result of Quantum Dynamics.....

Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness and asymmetry is a result of Unknown Forces and Quantum Dynamics.....


Standard (Integrated) Risk Framework

• Systemic (external) Risk – Future Management Frameworks – Outsights / Eltville Model

• Operational (internal) Risk – CLAS, SOX / COBIT

• Market (macro-economic) Risk – COSO, Basle II / Solvency II, BoE / FSA

• Trade (micro-economic) Risk – COSO, SOX / COBIT, GAAP / IFRS

Event Risk

• Event Risk is the threat of loss from unexpected events. Event Risk measurement systems seek to quantify the

actual or potential (realised or unrealised) exposure of the total asset portfolio to unexpected Wild Card or Black

Swan Events. Event Risk may arise from Systemic (external) sources – such as Natural Disaster, Geo-political

Crisis, or the collapse of Local, Regional or Global Markets or the failure of Sovereign Nation States - or Operational

(internal) sources – such as Rogue Trading or the failure of Compliance or Disclosure systems and processes.

Market Risk

• Market Risk is the threat of loss from movements in the level or volatility of Market Prices – such as interest rates,

foreign currencies, equities and commodities. Market Risk measurement systems seek to recognise the actual or

potential (realised or unrealised) exposure of the total asset portfolio as a result of money supply or commodity price

shocks (sudden changes in the balance between supply and demand) and changes in market sentiment affecting

the attractiveness, desirability or value of the asset portfolio – as well as changes in the level of market intervention

(government legislation or market regulation).

Trade Risk

• Trade Risk is the threat of loss from erosion in the attractiveness, desirability or value of specific traded instruments

between individual counterparties – including contracts for foreign currencies, equities and commodities. Trade Risk

measurement systems seek to quantify the actual or potential (realised or unrealised) value of specific contracts or

traded instruments, Trade Risk does not cover Incremental Risk Capital Charge (IRC) due to Toxic Asset lock-in.

Risk Types

Operational Risk Types

Internal Risk Group

Employee

Third Party

B A

Human Risk

Process Risk

3rd Party Risk

G

Systemic Risk Types

External Risk Group

B

Security Risk

F

Legal Risk

D

C

Technology Risk

- Liquidity Risk

Economic Risk

E

Compliance Risk

F D

H

E

A

G C

Disaster /

Catastrophe Risk

Sponsorship Risk

Stakeholders

Political Risk

Social Risk

Environment Risk

Security

Risk

Terrorism / Piracy Risk

- Credit Risk

D

Competitor Risk

J

F

Wild-card

Event Risk

Black Swan

Event Risk


Credit Risk

• Credit Risk is the threat of loss from changes in the status or liquidity of individual external debtors – changes in their

ability to service debts due to movement in their credit status, capitalisation, liquidity or solvency – or their exposure

to consequential losses due to statutory, regulatory or legal action. Credit Risk measurement systems seek to

quantify the actual or potential (realised / unrealised) ability of a Creditor to fulfil their contractual obligations.

Liquidity Risk – Solvency II and Basle II

• Liquidity Risk is the threat of loss from changes in the status or liquidity of an organisation –changes in their ability to

service debts due to internal movement in their credit status, capitalisation, liquidity or solvency – or their exposure to

consequential losses due to external statutory, regulatory or legal action. Liquidity Risk measurement systems seek to

quantify actual or potential (realised / unrealised) ability of a Bank or Insurer to meet provided / exposed liabilities.

• Basle II and Solvency II are Rules-based, Quantitative Risk Frameworks. The overhaul of the capital adequacy and

solvency rules is now well under way for European Financial Services - Banking and insurance - Life and Pensions,

General Insurers, Underwriters and Re-insurers -. Key drivers for Basle II and Solvency II include the following: -

• Key drivers for Basle II and Solvency II: -

• – EC directive around capital adequacy of Financial Services Companies

• – Critical requirement to bolster capital and strengthen balance sheets

• – Need to have reporting systems in place to demonstrate compliance

• – Deadline is Q4 2010 – so aggressive timeline for implementation

• – Fines and imprisonment for non-compliance or non-disclosure

• – Major insurance companies will invest £100m + in Compliance Programmes

• – Strategy, Business Process, Architecture and Technology changes

• – Specialisations include compliance, risk, finance, actuarial science

Risk Types

Trade Risk Types

Traded Instrument

Trader

Counterparty

B A

Fraud Risk

Insurance Risk

Counterparty Risk

D

Market Risk Types

Commodity

B

Market

Sentiment

Quantity Risk

E

Price Risk

G

C Exchange Rate Risk

- Credit Risk

- Liquidity Risk

Market Participants

F

Contract Risk

G D

I

F

H C

Currency

Risk

Commodity Risk

Financial Risk

Regulatory Risk

Wild-card

Event Risk

Black Swan

Event Risk

E

Interest

Rate Risk

A Money

Markets

Compliance Risk

Supervisors

H

Statutory Risk

Legislative Regulators

Price-shock

Risk


• Systemic Risk (external threats) - Eltville Model, Future Management Framework, Outsights

– Political Risk – Political Science, Futures Studies and Strategic Foresight

– Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting

– Social Risk – Population Growth and Migration, Futures Studies and Strategic Foresight

– Environmental Risk – Climate Change, Environmental Analysis, Modelling and Forecasting

– Event Risk – exposure to unexpected local, regional or global events

• Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals

• Black Swan Events – Scenario Planning and Impact Analysis – Future Management

• Market Risk (macro-economic threats) - COSO, Basle II / Solvency II, BoE / FSA

– Financial Risk – Traded Instrument Product Analysis, Valuation and Financial Management

– Currency Risk – FX Curves and Exchange-rate Forecasting

– Commodity Risk – Price Curves and Supply-Demand Forecasting

– Money Supply Risk – Interest Rate Curves and Money-market Forecasting

• Trade Risk (micro-economic threats) - COSO, Basle II / Solvency II, BoE / FSA

– Credit Risk – Credit Rating, Balanced Scorecard, Debtor Forecasting and Analysis

– Contract Risk – Asset Valuation, Credit Default Propensity Modelling

– Liquidity Risk – Solvency and Capital Adequacy Rules (Solvency II / Basle II)

– Insurance Risk – Underwriting Due Diligence and Compliance

– Actuarial Risk – Geo-demographic profiling and Morbidity Analysis

– Counter-Party Risk – Counter-Party Threat Analysis and Risk Management

– Fraud Risk (Rogue Trading) – Real-time Analytics at Point-of-Contract-Execution

Risk Types

Clinical Risk Types

Clinical Risk Group

Employee

Patient

B

A

Human Risk Process

Risk

D

Morbidity Risk Types

Morbidity Risk Group

C

Legal Risk

F

3rd Party Risk

G

C

Technology Risk

Trauma Risk

E

Morbidity Risk

H E

J

G

A

I D

Immunological System Risk

Sponsorship

Stakeholders Disease

Risk

Shock Risk

Cardiovascular

System Risk

Pulmonary System Risk

Toxicity Risk

Organ Failure Risk

- Airways

- Conscious

- Bleeding

Triage Risk

- Performance

- Finance

- Standards

Compliance Risk

H

Patient Risk

Neurological

System Risk F

B

Predation Risk


• Operational Risk (internal / external operational threats) - CLAS, SOX / COBIT

– Legal Risk – Contractual Law Due Diligence and Compliance

– Statutory Risk – Legislative Due Diligence and Compliance

– Regulatory Risk – Regulatory Due Diligence and Compliance

– Competitor Risk – Competitor Analysis, Defection Detection and Churn Management

– Reputational Risk – Internet Content Scanning, Intervention and Threat Management

• Business Operations Risk (internal business threats)

– Process Risk – Business Strategy / Architecture, Enterprise Target Operating Model (eTOM) / Business

Process Management (BPM) Verification /Validation

– Stakeholder Risk – Benefits Realisation Strategy and Communications Management

– Information Risk – Information Strategy and Architecture, Data Quality Management

– Disclosure Risk – Enterprise Governance, Reporting and Controls (SOX / COBIT)

• Digital Communications and Technology Risk (internal technology threats)

– Technology Risk – Technology Strategy and Architecture

– Security Risk – Security Principles, Policies, Architecture and Models (CLAS)

– Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

Enterprise Risk Management Framework Development

Qui ne risque rien n'a rien…..

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Enterprise Risk Management Framework Design

Changement est vieux comme le monde….. changement est aussi vieux que le temps.

Section 2 – Risk Management Framework Design

• This Section describes how to design an Enterprise Risk Management Framework – a set of


every type of risk event and which facilitate rapid and agile business transformation in order to deliver












COSO Enterprise Risk Management Framework

• The COSO Enterprise Risk Management Framework has eight components and four objectives categories. The eight components are: -

1. Internal Environment

2. Objective Setting

3. Event Identification

4. Risk Assessment

5. Risk Response

6. Control Activities

7. Information and Communication

8. Monitoring

• The four objectives categories - additional components highlighted are: -

1. Strategy - high-level goals, aligned with and supporting the organization's mission

2. Operations - effective and efficient use of resources

3. Financial Reporting - reliability of operational and financial reporting

4. Compliance - compliance with applicable laws and regulations

Achievement of Objectives • Within the context of an enterprise’s established mission or vision,

management establishes strategic objectives, selects strategy, and sets aligned objectives cascading through the enterprise. This enterprise risk management framework is geared to achieving an enterprise’s objectives, set forth in four categories: -

– Strategic – high-level goals, aligned with and supporting its mission

– Operations – effective and efficient use of its resources

– Reporting – reliability of reporting

– Governance – compliance with applicable laws and regulations.

• This categorization of enterprise objectives allows a focus on separate aspects of enterprise risk management. These distinct but overlapping categories – a particular objective can fall into more than one category – address different enterprise needs and may be the direct responsibility of different executives. This categorization also allows distinctions between what can be expected from each category of objectives. Another category, safeguarding of resources, used by some entities, also is described


Enterprise Risk Management Components • Enterprise Risk Management consists of eight interrelated components. These are

derived from the way that management runs an enterprise and are integrated with the management process. These components are: -

1. Internal Environment – The internal environment encompasses the tone of an organization, and sets the basis for how risk is viewed and addressed by an entity’s people, including risk management philosophy and risk appetite, integrity and ethical values, and the environment in which they operate: -

2. Objective Setting – Objectives must exist before management can identify potential events affecting their achievement. Enterprise risk management ensures that management has in place a process to set objectives and that the chosen objectives support and align with the entity’s mission and are consistent with its risk appetite.

3. Event Identification – Internal and external events affecting achievement of an entity’s objectives must be identified, distinguishing between risks and opportunities. Opportunities are channelled back to management’s strategy or objective-setting processes.

Enterprise Risk Management Components

Enterprise Risk Management Components (continued): -

4. Risk Assessment – Risks are analyzed, considering likelihood and impact, as a basis for determining how they should be managed. Risks are assessed on an inherent and a residual basis.

5. Risk Response – Management selects risk responses – avoiding, accepting, reducing, or sharing risk – developing a set of actions to align risks with the entity’s risk tolerances and risk appetite.

6. Control Activities – Policies and procedures are established and implemented to help ensure the risk responses are effectively carried out.

7. Information and Communication – Relevant information is identified, captured, and communicated in a form and timeframe that enable people to carry out their responsibilities. Effective communication also occurs in a broader sense, flowing down, across, and up the entity.

8. Monitoring – The entirety of enterprise risk management is monitored and modifications made as necessary. Monitoring is accomplished through ongoing management activities, separate evaluations, or both.

Enterprise Risk Management Components

Relationship between Risk Objectives and Risk Components • Enterprise risk management is not a strictly a serial process - where one

component affects only the next. It is a multidirectional, iterative process in which almost any component can and does influence every other component.

• There is a direct relationship between objectives, which are what an entity strives to achieve, and enterprise risk management components, which represent what is needed to achieve them.

• The four objectives categories – strategic, operations, reporting and compliance – are represented by the vertical columns, the eight components by horizontal rows, and an entity’s organisational units by the third dimension.

• This depiction portrays the ability to focus on the entirety of a business entity’s Enterprise Risk Management, or by objectives category, component, entity organisation unit, or any subset, dimension, viewpoint or view thereof.

• The relationship of risk objectives and components is depicted as a three-dimensional matrix - drawn in the form of a cube.


COSO - Relationship between Risk Objectives and Risk Components

• The relationship of the enterprise structure, risk objectives and risk components may be depicted as a three-dimensional matrix – which is often drawn in the form of a cube: -

COSO - Risk Objectives and Risk Components

COSO - Organisation Dimensions

• Organisation Components – Internal Environment

– Objective Setting

– Event Identification

– Talent Acquisition

– Talent Management

– Control Activities

– Information and Communication

– Monitoring

• Organisation – Business Structure – Enterprise

– Division

– Segment

– Strategic Business Unit

• Organisation – Legal Structure – Enterprise

– Group

– Company

– Subsidiary

• Organisation Dimensions – Organisational Structure and Development

– Jobs and Descriptions

– Roles and Responsibilities

– Human Resources Management

– Enterprise Performance Management

• Organisation Categories – Strategic Management

– Operational Management

– Financial Management

– Governance, Reporting and Controls

– Statutory and Regulatory Compliance

• Risk Components Threat Environments Objective Setting Event Identification Threat Assessment Threat Response Control Activities Information and Communication Monitoring

• Risk Dimensions • Risk Categories

• Risk Components

• Organisation Units

• Risk Management Process

• Risk Categories • Strategic

• Finance, Planning, Foresight

• Operational

• People, Process, Technology

• Reporting

• Enterprise Governance, Reporting and Controls

• Compliance

• Statutory / Regulatory / Standards Compliance

• Risk Management Processes Threat Analysis Risk Identification Risk Prioritization Risk Assessment Risk Management Strategies Risk Planning Risk Mitigation Risk Communication and Event Reporting Risk Monitoring and Control

COSO – Enterprise Risk Dimensions

COSO – Categories Of Risk Categories Of Risk. The risks faced by an enterprise should be classified in

relation to its unique business activities. There are a number of commonly used risk categories which help to group risks according to the various structural aspects of enterprise and their business unit activities: -

The following are examples of some frequently used Risk Categories: -

– Trade Risk (micro-economic) • Fraud Risk

• Price Risk

• Quantity Risk

• Contract Risk

• Insurance Risk

• Counterparty Risk

• Exchange Rate Risk

– Market Risk (macro-economic) • Commodity Risk

• Price Shock (Market Sentiment) Risk

• Currency Risk

• Interest Rate (Money Supply) Risk

• Regulatory / Statutory Risk

– Operational Risk (internal)

• Credit Risk

• Liquidity Risk

• Stakeholder Risk

• Reputational Risk

• Governance, Reporting and Controls

• Statutory and Regulatory Compliance

– Systemic Risk (external)

• Political Risk

• Economic Risk

• Sociological Risk

• Environmental Risk

• Security Risk (War, Piracy, Terrorism)


Establishing the Risk Context Establishing the Risk Context involves implementing the following steps: -

1. Plan the Risk Framework approach to enterprise risk management : -

– Determine the scope of the risk management study

– Confirm the identity and objectives of stakeholders

– Select the basis upon which risks will be evaluated

– Map out risk management strategies, process and procedures

– Manage risk management constraints – time, scope, knowledge, resources.

2. Research the internal and external threats posed by any given risk domain

3. Identify all of the risk categories / groups in the risk domain subject to interest

4. Evaluating and Prioritising of all the types of risk apparent in the risk domain

5. Define a Risk Framework for describing and documenting E2E enterprise risk management approach, policies, strategies, procedures, methods & techniques

6. Design an Analysis Matrix - internal / external threats, risk categories / groups.

7. Mitigation of Risks - risk management mitigation strategies – avoid / minimise.

8. Deliver the Risk Framework – deploying risk management techniques and methods along with human, organisational, process and technology resources.

Risk Identification After establishing the context, the next step in the process of managing risk is to identify individual potential Threat Scenarios. Risks are threat events that, when triggered, cause problems. Hence, risk identification can start with the source of problems, or with the problem itself.

1. Source analysis Risk sources may be internal or external to the system that is the target of risk management. Examples of risk sources are: stakeholders of a project, employees of a company or the weather over an airport.

2. Problem analysis Risks events are related to identifiable threat scenarios. For example: the threat of losing money, the threat of abuse of privacy information or the threat of accidents and casualties. The threats may exist with various entities, most important with shareholders, customers and legislative bodies such as the government.

When either source or problem is known, then the events that a source may trigger or the events that can lead to a problem can be investigated. For example: stakeholders withdrawing during a project may endanger funding of the project; privacy information may be stolen by employees even within a closed network; large birds striking a Boeing 747 during takeoff may cause the engine to fail, a lightning strike might cause onboard instrumentation to fail…..


Risk Analysis Risk Domain

A

Threat A

Threat B C

Risk Group

Risk Group

A Risk

Group

Risk Group

Risk Type

Risk Type

1

Risk Type

Risk Event

Risk Event 3

Risk

Risk

Event 2 2 Risk

Event Risk Type

Risk

Group

C

B Risk Group

Event Trigger

B Risk Type

D Risk Group Risk Type D

1 Risk Event

Risk Event 3

Risk Identification (continued) The chosen method of identifying risks may depend on culture, industry practice and compliance. The identification methods are formed by templates or the development of templates for identifying source, problem or event. Common risk identification methods include: -

3. Objectives-based risk identification Organizations and project teams have objectives. Any event that may endanger achieving an objective partly or completely is identified as risk. Objective-based risk identification is at the basis of COSO's Enterprise Risk Management -Integrated Framework

4. Scenario-based risk identification In scenario analysis different scenarios are created. The scenarios may be the alternative ways to achieve an objective, or an analysis of the interaction of forces in, for example, a market or battle. Any event that triggers an undesired scenario alternative is identified as risk -see Futures Studiesfor methodology used by Futurists.

5. Taxonomy-based risk identification The taxonomy in taxonomy-based risk identification is a breakdown of possible risk sources. Based on the taxonomy and knowledge of best practices, a questionnaire is compiled. The answers to the questions reveal risks. Taxonomy-based risk identification in software industry can be found in CMU/SEI-93-TR-6.


Risk Relationships – Groups and Types

A

B

C

D

E

G

H

F

Above is an illustration of risk relationships - how risk types might be connected. A detailed and

intimate understanding of the connection between risks may help us to answer questions such as: -

• Is risk type A related to risk types B and H – and if so, what is the nature of their relationships ?

• If risk type B occurs what is the impact on risk types C - G – are they more / less likely to occur ?


strategy – and to decide how to better focus our resources and effort on enterprise risk management.

Risk Group

Risk Type

Risk Group Domain 1

Risk

Cluster

Risk

Cluster

Risk Identification (continued)

Risk Identification (continued) The chosen method of identifying risks may depend on culture, industry practice and compliance. The identification methods are formed by templates or the development of templates for identifying source, problem or event. Common risk identification methods include: -

6. Common-risk Checking There are several industry risk check-lists

available where common and well-known risks are documented. Every risk in the check-list can be reviewed for suitability in application to a particular set of common situations. An example of known risks in the software industry is the Common Vulnerability and Exposures list may be found at http://cve.mitre.org

7. Risk Charting This method extends the risk check-list approach by documenting Enterprise Resources at risk, Threats to those resources and any Modifying Factors which may increase or reduce that risk are identified – along with any Risk Consequences that it is deemed desirable to avoid. Creating a multi-dimensional risk matrix under these headings supports a variety of different approaches. We can begin with resources and consider the threats they are exposed to - along with the consequences of each threat. Alternatively we can start with the threats and examine which resources they would affect, or we can begin with the consequences of risk and determine what combination of threats and resources would bring about any manifestation of those risk consequences

http://cve.mitre.org/

http://cve.mitre.org/

Risk Management Strategies

• The objective of Risk Management is to reduce the diverse risks related to a particular

domain to the level acceptable by stakeholders - the public, the company, regulators, the

shareholders, the board of directors, the risk committee, the management team etc.

– Event Risk Management strategies are focused on risks stemming from physical

causes – such as natural disasters, fires or accidents causing damage, injury or death

– Legal Risk Management strategies are focused on risks stemming from legal causes

such as lawsuits and prosecution that are mainly operational and due diligence risks.

– Financial Risk Management focuses on those risks associated with financial or

traded instruments – such as trade risk, market risk, credit risk, liquidity risk or

insurance risk – which can be managed via transactions in financial markets.

• Risk may refer to the numerous types of threats caused by the environment, technology,

politics, economics, human actions, 3rd Parties, regulations, compliances, best practices,

standards, processes and events. Risk management involves deploying all the means

available for risk mitigation – resources such as assets, people, processes and technology


COSO – Risk Domains The list below summarises some of the most common risk domains – along with

some indication of the potential risk impact and effects: -

• External Risk Domains – Infrastructure: - transport for staff, power and water supply business

relationships with partners, communications – voice / data / internet / email

– Economic: - interest rates, exchange rates, inflation

– Legal and Regulatory: - e.g. health and safety legislation

– Environmental : - energy consumption, pollution, climate change

– Political: - possible political constraints such as a change of government

– Trade: - Traded Instruments, counterparty performance, vendor performance,

– Market: - Competition, supply / demand and price curves for commodities

– "Act of God“ Natural Disaster: - fire, flood, drought, pandemic, landslide, earthquake, volcanic eruption, tsunami, impact of deep space objects.....

• Reputational Risk – Public Reputation: - Public Relations, performance, reputation, brand loyalty,

goodwill towards the organisation – along with consequential (intended and unintended) internal and external impact and ramifications

– Personal Reputation: - Reputation, conduct and behaviour of the officers of the organisation and consequential (intended and unintended) internal and external effects on the organisation

COSO – Threats

• There may be a certain degree of overlap between some threat categories, they are, however, suggested in order to help ensure that you do not overlook important threat categories. Try to put each threat in the category that it belongs to – the one which is most relevant to that threat. Some enterprises may even find they can amalgamate some of these categories and some may find they need extra ones; -

– Strategic Threats - This allows you to look at external threats, which may affect your enterprise such as changes in the environment in which you operate. It also lets you look at setting organisational objectives and ensuring you set the right objectives - and then meet them.

– Operational Threats - This looks at the risks, which arise from the services you deliver or the activities you carry out.

– Financial Threats - This covers financial risks facing the organisation in terms of internal systems, planning, funding etc.

– Human Threats - Review risks associated with both the employment of staff and the involvement of volunteers.

– Statutory and Regulatory Governance Threats - This threat category looks at the legislative framework within which your enterprise operates.

– Principles, Policies and Standards Governance Threats - This category of threats allows you to review and examine those threats which are part of the management of the enterprise.

COSO – Risk Categories

• Category of Risk Relating to... External Threats

– Infrastructure such as transport systems, utilities and power supply systems, suppliers, business relationships with partners, dependency on internet and email service providers

– Economic factors such as commodity prices, interest rates, availability of funds and credit, exchange rates, inflation and liquidity risk

– Legal and regulatory – statutory regulation which if complied with will reduce risk of litigation (e.g. Clinger-Cohen Act, Sarbanes-Oxley Act)

– Environmental Issues – such as fuel consumption, pollution

– Political – possible political constraints such as change of government

– Market Issues – such as competition and supply of goods

– ‘Act of God’ – natural disasters such as fire, flood, earthquake

• Category of Risk Relating to... Human Resources

– Recruitment – availability, recruitment and retention of suitable staff,

– Personnel – training, motivation and morale of staff

– Health and safety – laws and regulations which if complied with should reduce hazards and increase security and well-being of employees

COSO – Risk Groups • Internal Risk Groups – Operational / Organisational Risk

– Policy Risk: - appropriateness and quality of policy decisions

– Operational Risk: - procedures employed to achieve particular objectives

– Information Risk: - adequacy of information used for decision making

– Transferable Risks: - opportunity cost of outsourcing risks at appropriate cost – risks that may be transferred outside of the organisation to be dealt with by third parties (managed, insured, underwritten)

– Technology Risk: - risk in use of technology to achieve corporate objectives

– Project / Programme Risk: - project planning and management procedures

– Innovation Risk: - exploitation of opportunities to make gains

– Personnel Risk: - availability and retention of suitable staff

– Health and Safety Risk: - health, safety and well-being of people

COSO – Risk Groups

• Financial Risk Domain – Budgetary Risk - availability and allocation of resources

– Fraud or theft: - unproductive loss of assets and resources

– Insurable - potential areas of loss that can be insured against

– Capital investment - making appropriate investment decisions

– Liability - the right to sue or be sued in contract agreements

– External Finance (Trade) Risk – Market Risk (Commodities) / Money Supply Risk – Credit Options, Interest Rate

– Internal Finance (Operational) Risk - Credit Risk / Liquidity Risk

• Internal Reputation Risk – Fraud Risk – rogue trading, trading beyond authorisation / limits, breach of

contractual / statutory / regulatory / ethical obligations

– Employee Performance Risk – achievement of quality / financial / performance targets by employees

– Employee Relations Risk - staff morale and goodwill, internal reputation of the organisation and consequent internal effects

Enterprise Risk Management Framework Delivery

Changement est vieux comme le monde….. changement est aussi vieux que le temps.

Section 3 – Risk Management Framework Delivery

• This Section describes how to implement an Enterprise Risk Management Framework – a set of


every type of risk event - and which facilitate rapid and agile business transformation in order to deliver













Risk Management Framework Delivery – Key Issues

• Enterprise Risk Management Frameworks are a set of processes, data, systems

and technology which help to manage and control every type of risk event.

• Enterprise Risk Management Frameworks facilitate rapid and agile business

transformation in order to deliver the clients desired future organisational structure

and target business operating model which are resilient to the impact of risk

• Enterprise Risk Management Frameworks therefore ensure Critical Success

factors such as enterprise governance, reporting and controls, disaster planning and

recovery management, business continuity, statutory and regulatory compliance

• The Enterprise Risk Management Framework can easily be implemented using

Amphora Symphony supported by SAP modules - SAP HANA, Business Objects,

EPM, GRC, SEM, TRM. There are also Oracle and Microsoft options.....



1. Framing and Scoping the Risk Management Study – Risk Research – understanding and evaluating the problem domain

2. Decide Risk Appetite and Risk Mitigation Strategies – Risk Identification – identifying applicable Threats, Risk Categories, Risk Groups and Risk Types

3. Determine Risk Organization Structure and Governance Methods – Risk Prioritization – ordering and prioritising threats by probability / magnitude

4. Develop Risk Management Framework Structure, Methods and Metrics – Risk Assessment – comparing and balancing the individual threat posed by each risk item in the

ordered and prioritized consolidated enterprise risk register

5. Design Risk Management Framework Structure – Risk Model and Processes – Risk Planning – assessing the overall threat contained within the risk register

6. Develop Risk Management Framework Content – Risk Reporting and Controls – Risk Management Strategies – transferring, avoiding, reducing or accepting risk

7. Deploy Risk Management Framework – Training, Infrastructure and Systems – Risk Mitigation – introduce Risk Management processes, systems and controls

8. Implement Risk Management Framework – Go-live – Risk Implementation – start managing risk by reducing uncertainty through the targeted application of

strategic foresight, planning and forecasting and rolling out Risk Management processes, systems and controls


Professors Peter Bishop and Andy Hines at the University of Texas Futures Studies School at the Houston Clear Lake site, have developed a definitive

Strategic Foresight Framework for Enterprise Risk Management: –

Thinking About the Future Framework

1. FRAMING AND SCOPING •

• This important first step enables organizations to define the purpose. focus, scope and boundaries of the Political, Legal, Economic, Cultural, Business and Technology problem / opportunity domains requiring resolution. Taking time at the outset of an Enterprise Risk Management programme, the Strategic Foresight Team defines the Threat / Risk Study domain, outlines the required outcomes, goals and objectives and determines how best to achieve them. •

• Risk Strategy Study Definition – Problem / Opportunity Domains: -

– Definition - Focus, Scope, Purpose and Boundaries

– Approach - What – How – Why – Who – When – Where?

– Justification - Cost, Duration and Resources v. Future Benefits and Cash Flows


2. ENGAGING and DECIDING RISK APPETITE•

• This second phase is about stakeholder management - developing action agendas for mobilising the Risk Programme and opening stakeholders communications channels, soliciting collaborative participation and input to determine risk appetite.

• This may involve staging a wide range of Programme kick-off Events , organising Stakeholder Strategy and Communications, Target-setting and Programme Planning streams, establishing mechanisms for reporting actual achievement against targets – in order that the Strategic Foresight Team engage a wide range of stakeholders, presents a future-oriented, customer-focussed approach and enables the efficient delivery of Strategy Study artefacts & benefits in planned / managed work streams. •

• Risk Strategy Study Mobilisation – Stakeholder Engagement: -

– Communication Strategy

– Benefits Realisation Strategy

– Strategy Study Programme Plan

– Stakeholder, SME and TDA Strategy Study Launch Events

– Risk Appetite – Conservative (risk averse) to Aggressive (high risk / high reward)


3. RISK RESEARCH – HORIZON SCANNING, MONITORING AND TRACKING: •

• Once the Strategic Foresight Team is clear about the engagement boundaries, purpose, problem / opportunity domains and scope of a Risk Strategy Study - they can begin to scan both internal and external environments for all relevant input content – information and data describing extrapolations, patterns and trends – or indicating global transformations, emerging and developing factors and catalysts of change – and to search for, seek out and identify any Weak Signals indicating the potential for disruptive future Wild Card or Black Swan events. •

• Risk Strategy Investigation – Content Capture: -

– Internal and External Content, Information and Data

– Threat Landscape - Extrapolations, Patterns and Trends

– Managing Uncertainty - Factors and Catalysts of Change

– Potential Threats, Risk Domains, Risk Categories, Risk Groups

– Horizon Scanning, Monitoring and Tracking Systems and Infrastructure


4. RISK STRATEGY DISCOVERY – STAKEHOLDER EVENTS & STRATEGY THEMES •

• Here we begin to identify and extract useful information from the mass of Research Content that we have collected. Critical Success Factors, Strategy Themes and Value Propositions begin to emerge from Data Set “mashing”, Data Mining and Analytics against the massed Research Data – and all supplemented via the very human process of Cognitive Filtering and Intuitive Assimilation of selected information - through Discovery Workshops, Strategy Theme Forums, Value Chain Seminars, Special Interest Group Events and one-to-one Key Stakeholder Interviews. •

• Risk Strategy Discovery – Content Analysis: -

– Risk Catalogue - Outline Structure

– Data Set “mashing”, Data Mining and Analytics

– Stakeholder, SME and TDA Strategy Discovery Events and Risk Strategy Themes

– Discovered Assumptions, Critical Success Factors, Strategy Themes and Value Propositions, Threats, Risk Domains, Risk Categories and Risk Groups


5. STRATEGIC RISK MANAGEMENT •

• The underlying premise of Strategic Risk Management is that every enterprise exists to provide value for its stakeholders. All entities face uncertainty and the possibility of chaos and disruption. Risk Management is the evaluation of uncertainty. The challenge is to determine how much risk we are able to accept as we strive to grow stakeholder value. Uncertainty presents both opportunity and risk with the possibility of either erosion or enhancement of value. Strategic Foresight enables stakeholders to deal effectively with uncertainty and associated risk and opportunity - thus enhancing the capability of the Enterprise to build long-term value. •

• Risk Management – Value Chain Building and Value Erosion: -

– Risk Research and Identification

– Risk Catalogue – Proposed Format

– Business Value Chain and threat of Value Erosion by Risk Factors

– Randomness – factors of Uncertainty, Disorder, Chaos and Disruption

– Identified Assumptions, Critical Success Factors, Strategy Themes and Value Propositions, Threats, Risk Domains, Risk Categories and Risk Groups.


Strategic Risk Management • Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment

Scenario, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - subsequent risks with lower probability of occurrence and lower consequential losses are then handled in descending order. As a foresight concept, Wild Card or Black Swan events refer to those events which have a low probability of occurrence - but an inordinately high impact when they do occur.

– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for many governments and global enterprises. We are now moving into a period of time impacted by unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a world of increasingly uncertain, complex and interwoven global events.

– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15 years or so - but there are also limitations to this technique in this period of unprecedented complexity and change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and integrated into our risk management and strategic planning processes.

• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme manifestations with a very low probability of, occurrence - but an inordinately high impact when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies – with a view to identifying those Weak Signals which may have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

Strategic Risk Management

• Systemic Risk (external threats) – Political Risk – Political Science, Futures Studies and Strategic Foresight – Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting – Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals – Black Swan Events – Scenario Planning & Impact Analysis – Future Management

• Market Risk (macro-economic threats) – Equity Risk – Traded Instrument Product Analysis and Financial Management – Currency Risk – FX Curves and Forecasting – Commodity Risk – Price Curves and Forecasting – Interest Rate Risk – Interest Rate Curves and Forecasting

• Trade Risk (micro-economic threats)

– Credit Risk – Debtor Analysis and Management – Liquidity Risk – Solvency Analysis and Management – Insurance Risk – Underwriting Due Diligence and Compliance – Counter-Party Risk – Counter-Party Analysis and Management

Strategic Risk Management

• Operational Risk (internal threats)

– Legal Risk – Contractual Due Diligence and Compliance – Statutory Risk – Legislative Due Diligence and Compliance – Regulatory Risk – Regulatory Due Diligence and Compliance – Competitor Risk – Competitor Analysis, Defection Detection / Churn Management – Reputational Risk – Internet Content Scanning, Intervention / Threat Management – Corporate Responsibility – Enterprise Governance, Reporting and Controls – Digital Communications and Technology Risk

• Security Risk – Security Principles, Policies and Architecture • Process Risk – Business Strategy and Architecture • Information Risk – Information Strategy and Architecture • Technology Risk – Technology Strategy and Architecture • Stakeholder Risk – Benefits Realisation Strategy and Communications Management • Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

6. THREAT ANALYSIS •

• In most organizations, many stakeholders, if unchallenged, tend to believe that threat

scenarios - as discovered in various SWOT / PEST Analyses - are going to play out pretty much the same way as they have always done so in the past. When the Strategic Foresight Team probes an organization’s view of the future, they usually discover an array of unexamined, unexplained assumptions tending to either maintain the current status quo – or converging around discrete clusters of small, linear, incremental future changes •

• Threat Analysis – Value Chain Analysis: - – Risk Catalogue – Proposed Detailed Content

– Threat Analysis, Assessment and Prioritisation

– Global Transformations, Factors and Catalysts of Change

– Analysed Assumptions, Critical Success Factors, Strategy Themes and Value Propositions , Threats, Risk Domains, Risk Categories and Risk Groups


Strategic Risk Management • Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment

Scenario, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - subsequent risks with lower probability of occurrence and lower consequential losses are then handled in descending order. As a foresight concept, Wild Card or Black Swan events refer to those events which have a low probability of occurrence - but an inordinately high impact when they do occur.

– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for many governments and global enterprises. We are now moving into a period of time impacted by unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a world of increasingly uncertain, complex and interwoven global events.

– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15 years or so - but there are also limitations to this technique in this period of unprecedented complexity and change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and integrated into our risk management and strategic planning processes.

• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme manifestations with a very low probability of, occurrence - but an inordinately high impact when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies – with a view to identifying those Weak Signals which may have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

7. STRATEGIC FORESIGHT •

• The prime activity in the Strategic Foresight Process is, therefore, to challenge the

status quo viewpoint and provoke the organisation into thinking seriously about the possibility that things may not continue as they always have done - and in fact, rarely do so.

• Strategic Foresight processes should therefore include searching for and identifying any potential Weak Signals predicating future Wild Card and Black Swan events – in doing so, revealing previously hidden factors and catalysts of change – thus exposing a much wider range of challenges, issues, problems, threats, opportunities and risks than may previously have been considered. •

• Strategic Foresight – Business Value Chain and Risk Management: - – Risk Planning, Mitigation and Management

– Weak Signals, Wild Cards and Black Swan Events

– Risk Catalogue – Evaluated Threats, Prioritised Risk Register

– Managed Assumptions, Critical Success Factors, Strategy Themes and Value Propositions, Threats, Risk Domains, Risk Categories and Risk Groups


8. RISK SCENARIO FORECASTING and IMPACT ANALYSIS •

• Scenarios are stories about how the future may unfold – and how that future will

impact on the way that we work and do business with our business partners, customers and suppliers. The Strategy Study considers a broad spectrum of possible scenarios as the only sure-fire way to develop robust strategic responses that will securely position the Strategic Foresight Programme to deal with every opportunity and threat domain that may transpire.

• The discovery of multiple scenarios and their associated opportunity / threat impact assessments, along with their probability of materialising – covers a wide range of possible and probable Opportunity / Threat situations – describing a rich variety of POSSIBLE, PROBABLE and ALTERNATIVE FUTURE RISKS •

• Scenario Forecasting – Impact Analysis: -

– Possible, Probable and Alternative Risk Scenarios

– Reviewed Threats, Risk Domains, Risk Categories and Risk Groups

– Clustered Assumptions, Critical Success Factors, Risk Strategy Themes

– Possible Future Business Models and Value Propositions, Products and Services


9. RISK STRATEGY VISIONING, FORMULATION AND DEVELOPMENT •

• After forecasting has laid out a range of potential Future Scenarios, visioning comes

into play — generating a pragmatic view of our “preferred” Future Risk Environment – thus starting to suggest stretch goals for moving towards our “ideal” Strategy Models - using the Strategic Principles and Policies to drive out the “desired” Vision, Missions, Outcomes, Goals and Objectives for managing Risk •

• Risk Strategy Visioning, Formulation and Development: -

– Threats, Risk Domains, Risk Categories and Risk Groups – Strategic Principles and Policies, Guidelines and Best Practices

– Strategy Models and desired Vision, Missions, Outcomes, Goals and Objectives

– Proposed Future Business Models and Value Propositions, Products and Services

– Proposed Enterprise Risk Management Framework Structure


10. PLANNING: the bridge between the VISION and the ACTION – IMPLEMENTATION •

• Here, the Strategy team transforms the desired Vision, Missions, Outcomes, Goals

and Objectives into the Strategic Master Plan, Enterprise Landscape Models, Strategic Roadmaps and Transition Plans for organisational readiness and mobilisation – maintaining Strategic Foresight mechanisms (Horizon Scanning, Monitoring and Tracking) to preserve the capability to quickly respond to fluctuations in internal and external environments •

• Strategy Enablement and Delivery Planning: - – Threats, Risk Domains, Risk Categories and Risk Groups

– Proposed Enterprise Risk Management Framework Design

– Horizon Scanning, Monitoring and Tracking Systems and Infrastructure

– Planned Future Business Models and Value Propositions, Products and Services

– Strategic Master Plan, Enterprise Landscape Models, Roadmaps, Transition Plans


11. IMPLEMENTATION •

• This penultimate phase is about communicating results and developing action agendas for mobilising strategy delivery – thus launching Business Programmes that will drive forwards to the realisation of Strategic Master Plans and Future Business Models through Business Transformation, Enterprise Portfolio Management, Technology Refreshment and Service Management - via Cultural Change, innovative multi-tier and collaborative Business Operating Models, Emerging Technologies (Smart Devices, the Smart Grid and Cloud Services) Business Process Re-engineering and Process Outsource - Onshore / Offshore. •

• Strategy Enablement and Delivery Programmes: - – Threats, Risk Domains, Risk Categories and Risk Groups – Launched Enterprise Risk Management Framework Content – Proposed Future Business Models and Value Propositions, Products and Services – Enterprise Portfolio Management - Technology Refreshment • System Management • – Business Transformation – Organisational Re-structuring • Cultural Change • Business

Process Management • Operating Models • Programme Planning & Control – DCT Models - Demand / Supply Models • Shared Services.• Business Process

Outsource • – Emerging Technologies – Real-time Analytics • Smart Devices • Smart Grid • Mobile

Computing • Cloud Services • – Service Management - Service Access • Service Brokering • Service Provisioning •

Service Delivery •


12. REVIEW •

• In this final phase, we focus on Key Lessons Learned and maintaining the flow of useful information from the Strategic Foresight mechanisms and Enterprise Risk Management Framework infrastructure – in order to support an ongoing lean and agile capability to continually and successfully respond to the volatile and dynamic internal and external environment - through Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting. •

We also prepare for the next full round of the Risk Strategy Cycle, beginning again with Phase 1 – Framing and Scoping.

• Strategy Review: -

– Reviewed Enterprise Risk Management Framework

– Reviewed Threats, Risk Domains, Risk Categories and Risk Groups

– Reviewed Business Models and Value Propositions, Products and Services

– Horizon Scanning, Monitoring and Tracking Systems, Infrastructure and Data

– Futures Studies, Strategy Reviews, Business Planning and Forecasting

– “Crystal Ball” Report

Peter Bishop and Andy Hines – University of Houston


13. THE CRYSTAL BALL REPORT - PUBLICATION •

The “Crystal Ball Report” is a comprehensive document that aggregates the results from all

12 phases of strategic risk analysis. The findings from the technical analysis of SWAT, PEST

and Risk Catalogue – along with an assessment of Business and Technical (non-functional)

Drivers / Requirements – taking into account client desired outcomes, goals and objectives.

Recommendations for Risk Strategy Implementation, Organisational Change and Business

Transformation – is contained in the Strategic Roadmap and grouped together in the “Crystal

Ball Report”. SWAT, PEST and Risk Catalogue are highlighted. Stakeholder Groups, roles

and responsibilities are defined, a Strategy Programme Plan is generated and an Architecture

Roadmap is produced and elaborated. The Crystal Ball Report includes a detailed System

Dependency Map – outlining application system and platform candidates for Technology

Refreshment – COTS integration, Application Consolidation, Application Re-hosting in the

Cloud – or complete Application Renovation and Renewal based on new Enterprise Platforms.

The “Crystal Ball Report” is designed to become the “shared vision” reference point, where

all stakeholders can see how their needs and functions are both addressed and add value to

the overall corporate plan, keeping everyone “in the boat”, and “rowing in the same direction.”


Trading and Risk Management

Section 4 – Risk Management Systems

• This Section describes the structure of Risk Management Systems. Risk is the threat of loss from

unexpected events. Risk measurement systems seek to quantify the actual or potential (realised or

unrealised) exposure of the total asset portfolio to the Risk Register plus unexpected Wild Card or Black

Swan Events. Risk may arise from Systemic (external) sources or Operational (internal) sources : -










Trading and Risk Management – Key Issues

• Trading and Risk Management Systems support a set of processes, data, systems and technology which help to manage and control trade, operational, market, systemic and event risk – wild card and black swan events.

• Trading and Risk Management Systems are designed and built using rapid and agile development methods in order to support the clients desired target business operating model, technology strategy and enterprise architecture – providing statutory and regulatory compliance and resilience to the impact of risk events.

• Trading and Risk Management Systems drive the collection and analysis of Key Performance Indicators which support the achievement of Critical Success Factors such as enterprise governance, reporting and controls, disaster planning and recovery management, business continuity, statutory and regulatory compliance

• Trading and Risk Management Systems can easily be implemented using Amphora Symphony and standard SAP modules - SAP HANA, Business Objects, EPM, GRC, SEM, TRM. There are also Oracle and Microsoft options.....


Example – Inter-connected Risk

A Trigger

A

Freedom of Information Act (USA)

B Trigger

B

Financial Services De-regulation Act (USA)

Employers can now view unspent convictions when reviewing job applications from Convicted Felons

Convicted Felons can now apply for jobs

as Independent Financial Agents (IFAs)

C Trigger

C


Risk Event

Risk Event

D

Related Risk Example – Sub-Prime Mortgage Crisis

The only jobs easily available to Convicted Felons is

as self-employed Independent Financial Agents (IFAs)

Risk Event

Risk Event

Independent Financial Agents (IFAs) miss-sell Sub -prime Mortgages in Unregulated Financial Markets

Risk Event

E

Mortgagees with miss-sold Sub -prime Mortgages repay year one and two repayments of their low-start mortgage payment plan – but begin to struggle when monthly payments rise

Risk Event

F

Mortgagees with miss-sold Sub -prime Mortgages begin to default

on repayments when their monthly payments rise at the end of the

low-start payment plan and increase in interest rates – so mortgages are foreclosed , they become evicted and their homes are re-possessed.

Trigger D

USA Sub-Prime

Mortgage Crisis

2008

USA Sub-Prime

Mortgage 2006

Trigger Events

Related Risk Example – Credit Default Obligation (CDO - Toxic Asset) Crisis

Trigger D

USA Sub-Prime

Mortgage Crisis

2008

Example – Inter-connected Risk

G Trigger

E


Risk Event

Risk Event

H

Rating Agencies (e.g. Standard and Poors etc.) award AAA Rating to Credit Default Obligation (CDO) Products

Risk Event

I

Investment Analysts in US and European Banks recommend that their clients invest in sub-prime Credit Default Obligation (CDO) Products with AAA Rating

Risk Event

J

US and European Banks invest heavily in sub-prime

Credit Default Obligation (CDO) Products with AAA Rating In expectation of low risk / high returns on investments

Trigger F

CDO Toxic

Asset Crisis

2008

Merchant Banks rack-and-stack tranches of sub-prime mortgages into Credit Default Obligation (CDO) Products

K

E Trigger

K Sovereign

Debt Crisis

2008

B Trigger

I

Money

Supply

Shock

2008

C Trigger

H

Financial

Services

Sector

Collapse

2008

D Trigger

G

L

CDO Products

A Trigger

J

Credit

Crisis

2008

Global

Recession

2008

Enterprise Risk Management Frameworks

Cluster Theory Business Clusters are economic agglomerations of firms - all of which are interconnected

by a common value-chain – co-located within a geographic area which also benefit from

regional access to local concentrations or availability of specific activities,

competencies and resources, such as input/output markets and infrastructure, in a

favourable environment which is coordinated via public and private sector institutions and

policies.

Cluster R&D tends to become more demand driven. Greater competition is

encouraged with a culture of co-operation also being fostered – but driving cluster

productivity is the opportunity for collaboration with co-specialisation amongst the

firms within the Cluster.

Ifor Ffowcs-Williams - CEO, Cluster Navigators Ltd & Author, “Cluster Development”

Section 5 – Management of Risk across Business Clusters and the Supply Chain • This Section describes how to manage risk across Business Clusters and Supply Chain Partners. Third

Party Risk is the threat of loss from unexpected events impacting on Business Partners and the Supply Chain.

Risk measurement systems seek to quantify the actual or potential (realised or unrealised) exposure of the total

asset portfolio to risk from Supply Chain (Third Party) sources and unexpected Wild Card / Black Swan Events.









3rd Party Risk

“Socio-economic

changes and

emerging digital

technology combined

with increasingly

global supply chains

pose a clear and

present danger to

ongoing business

continuity and create

an ever growing

potential for risk and

losses.”

“ The very flexibility

that provides the

supply chain with its

cost advantages has

also caused its

inherent vulnerability.”

Cluster Theory – Industry Sectors

• A Business Cluster is a Geographic Location where a local concentration or availability of specific

competencies and resources in a industry sector, develops favourable conditions that reach a critical

concentration or threshold level, sufficient to create a decisive sustainable competitive advantage –

over and above that of other competing locations – and may further evolve into a position of regional

or even global supremacy in that industry sector or competitive field (e.g. Silicon Valley, Hollywood).

• The fundamental concept of Geographical Economic Clusters – to which social geographers and

economists have also referred to as agglomeration economies – is very well documented by Alfred

Marshall in his work of 1890. The term Business Cluster, also known as an Industry Cluster,

Competitive Cluster, or Technology Cluster, was further popularised by Michael Porter in his book

The Competitive Advantage of Nations (1990). The importance of the role of clusters in economic

geography, or more correctly geographical economics, was also brought to the public attention by

Paul Krugman in his book Geography and Trade (1991). Cluster development has since become an

important focus for numerous government infrastructure and regional development programs.

• Michael Porter claims that clusters have the potential to affect competition in three ways – through

increasing the productivity of the companies in the cluster, by driving innovation in the cluster, and by

stimulating new businesses in the cluster. According to Porter, from 1990 onwards in the modern

global economy, comparative advantage – where certain locations enjoy favourable conditions for

example, cheap labour for Manufacturing (China) and harbour, faculties for Mercantilism (Hong Kong

and Singapore) - are becoming less relevant. Today, it is how companies make efficient use of inputs

to stream continuous innovation – that has achieved increased significance for competitive advantage

.

https://en.wikipedia.org/wiki/Sustainable_competitive_advantage

https://en.wikipedia.org/w/index.php?title=Agglomeration_economies&action=edit&redlink=1

https://en.wikipedia.org/wiki/Alfred_Marshall

https://en.wikipedia.org/wiki/Alfred_Marshall

https://en.wikipedia.org/wiki/Michael_Porter

https://en.wikipedia.org/wiki/Paul_Krugman

https://en.wikipedia.org/wiki/Paul_Krugman

https://en.wikipedia.org/wiki/Cluster_development

https://en.wikipedia.org/wiki/Global_economy

https://en.wikipedia.org/wiki/Comparative_advantage

https://en.wikipedia.org/wiki/Innovation

https://en.wikipedia.org/wiki/Competitive_advantage


• Regional Clusters are created by the local availability or concentration of specific competencies and

resources. Cluster Theory states that any Regional Geographic concentration of any specific Industry

Sector may create a number of advantageous local conditions. The first effect is increased competition

– so greater efficiency is encouraged, leading to improved productivity and higher total profits which are

shared between all of the participating firms in that Industry Sector. It is also claimed that Business

Clustering drives increased Research, Development and Business Innovation (Michael Porter).

• Greater competition is encouraged, but also the opportunity for collaboration, and a culture of

co-operation is fostered – with co-specialisation amongst the firms within the cluster driving

productivity. Public R&D tends to become more demand driven – Ifor Ffowcs-Williams..

• Suppliers are attracted to co-locate into the Regional Cluster – thus shortening the Supply Chain and

improving Logistics. The presence of a wide choice of suppliers in the region leads to greater vendor

performance and thus reduced costs for collaborating firms. Those firms with a successful Business

Operating Model also tend to become more competitive, eventually leading to economies of scale being

derived from both vertical and horizontal integration – Business Agglomeration – that is, absorption of

smaller, less efficient competitors, customers and suppliers by expanding industry conglomerates. The

presence of a regional centre of excellence for any Industry Sector also attracts an increasingly Global

customer base seeking reliable Business Partners – this Globalisation effect in turn promotes both local

and inward investment and drives further business expansion and industry sector growth.


• Globalisation and localisation are two sides of the same coin. Merger and Acquisition activity is

healthily enhanced within a strong cluster - but needs to be continually fed by new start-ups and

spin-offs - Ifor Ffowcs-Williams..

• Concentrating related industries together in specific regions also creates greater demand in the local

Labour Market, leading over time to the development of a specialist regional skills base. This may

cause the spin-off of new businesses exploiting the skills available in the labour pool. Increased

employment opportunities also means increased Wages flowing into the Regional Economy and greater

Regional Taxation Revenues - which in turn yields multiple benefits across the region as a whole.

• 'Smart Specialisation' is the term being increasingly used by the European Union. Skills

development is often the main issue facing a high growth cluster - Ifor Ffowcs-Williams.

• Note that a cluster is not artificially confined within a rigid Geographic Area - e.g. Tech City lies both in

and around the boundaries of Shoreditch's Technology Campus. There are other Digital Clusters - e.g.

The Financial Technology (Fin Tech) Cluster in Canary Wharf, the Science Parks established around

University Campus sites - such as those north of Cambridge and west of Oxford - and in the Digital

Campus around the BT Laboratories Innovation Hub at Adastral Park in Martlesham Heath, Ipswich.

Expert Commentator: -

• Ifor Ffowcs-Williams, CEO, Cluster Navigators Ltd and Author, “Cluster Development”

– Address : Nelson 7010, New Zealand (Office)

– Email : [email protected]



Cluster Definitions

• Clusters are economic agglomerations of firms co-located within a geographic area - all connected by

a common value-chain – which benefit from regional access to local concentrations or availability of

specific activities, competencies and resources, such as input/output markets and infrastructure, in a

favourable environment which is coordinated via public and private sector institutions and policies.

• Clustering is the tendency of vertically and horizontally integrated firms in related lines of business to

concentrate together geographically (OECD, 2001). Clusters are geographically co-located groups of

interconnected companies and institutions which operate together in a specific field or industry sector

and are linked together by a number of common and complementary factors (Michael Porter, 1998).

• Clusters are co-located groups of Business Enterprises, Government Agencies and NGOs for whom a

close association is an important source of individual and collective competitive advantage – using

common factors such as Finance (venture capital) , Procurement (buyer-supplier relationships) and

Distribution (supply chain channels), that exploits shared activities, resources, technologies, skills,

knowledge and labour pools – which binds the cluster closely together (Bergman and Feser, 1999).

• Clusters are networks of strongly interdependent enterprises (customers and suppliers), all linked

together in an integrated production chain, in value-added activities or via business partnerships, (e.g

Automotive and Aerospace sector). In many types of Cluster enterprise relationships also encompass

strategic alliances with Government Agencies, universities, research institutes, bridging institutions and

knowledge providers (i.e. consultants, brokers, business services), (Roelandt / den Hertog, 1999)

NESTA

creative clusters

• NESTA have created the first ever map of the UK's most creative business clusters.

• This definitive work identifies all of the nation's top 'creative hotspots', - areas which host clusters of creative businesses which are promoting technology innovation and driving economic growth across their region.

Financial Technology (Fin Tech) Innovation Clusters

Management of Risk across Business Clusters and the Supply Chain

• Rapid globalization has increased the interconnection (sharing) of risks over Business

Partnerships and the extended Supply Chain. Events that occur in one industry or country

can now rapidly impact on other industries around the globe. In 2011, both the earthquake

and tsunami in Japan and the floods in Thailand caused not only immense losses in these

countries, but also disrupted sourcing and manufacturing in industries around the globe.

• Globalization has introduced fundamental third-party and systemic risk into corporate supply

chains which are threatened by natural catastrophes, pandemics, cyber risks or terrorism.

The very flexibility and cost-effectiveness that give a modern supply chain its strength and

competitive advantage also create its vulnerability to disruption. The only way to deal with this

threat is to develop better supply chain risk management systems even if these might add

some cost back into today’s very lean processes. Organizations now need to consider the

necessary trade-off between driving business efficiencies and ensuring that there is “no

single point of failure” – at the cost of operational redundancy.

• Today’s global supply chains work to an ever tighter set of process interdependencies, with

‘just-in-time’ and ‘lean manufacturing’ now standard practices. This evolution, combined with

an increasing trend to source globally and with a rise in disruptive natural catastrophes, has

led to growth in business interruption and contingent business interruption. Enterprises are

increasingly being caught out by the impact of failure and closure of critical business partners

and suppliers - a trend which has both insurers and businesses concerned.


• Not only are companies re-examining how to better mitigate disruptions in future, but insurers

are also re-assessing risks in their portfolios. Their key concern is a potential accumulation of

related or un-related risk – the burden of which builds incrementally in the risk portfolio - or the

potential of a single catastrophic event triggering multiple insured supply chain-related losses.

• While insurance can provide cover for some of the losses faced by business disruption,

dependence on insurance alone is a risky strategy. Coverage for financial losses does not

take account of a loss of market share, declines in investor confidence, or share price losses

caused by the failure of a key supplier. The impact of these blows can be just as devastating, if

not more so, to a business than financial losses on their own. To cover all bases, companies

must improve their risk management strategies to manage stakeholder interests after a

business interruption. Robust business continuity plans will go some way towards validating

supply chain resilience in the event of a breakdown in these links.

• Companies should also look to improve supply chain resiliency by adding redundancy, even if

that process adds cost back into previously stripped back supply chain calculations. Sourcing

alternative suppliers in advance of a possible supply chain collapse will increase a company’s

preparedness, while resiliency can be further improved by ensuring where possible that the

chain contains no “single point of failure” – vulnerability caused by single supplier sourcing.


• “Socio-economic changes and emerging digital technology combined with increasingly global

supply chains pose a clear and present danger to ongoing business continuity and create an

ever growing potential for risk and losses.”

• Insurers support these risk management strategies as a means of reducing their exposure to an

accumulation of risk. Under business and contingent business interruption covers insurers are

increasingly finding that the ripple effect of one event can affect multiple insured parties and policies -

therefore lead to high losses. Insurers want and need to better understand supply chain risk and are

encouraging companies to provide improved information about their critical suppliers and their risk

management approaches.

• “The very flexibility that provides the supply chain with its cost advantages has also caused

its inherent vulnerability.”

• The sharing of supply chain resiliency data can, for some companies, be a barrier because of

the perceived propriety value of the information. Reciprocal data sharing with insurers will,

however, ultimately improve relationships, facilitate claims handling, and ensure that capacity

(at prices which accurately reflect risk) is available in the insurance markets. Co-operation and

transparency in equal measures have already resulted in the emergence of alternatives to

traditional business interruption and CBI coverage and this trend can be expected to continue

as relationships between insurers and insured parties mature further.

Risk Research

• Traditional approaches to risk studies and risk management are based upon the

paradigm of risk as an event adequately characterised by a single feature. This

simplistic conceptualisation of risk leads to the use of analysis tools and models

which do not reliably integrate qualitative and quantitative information or model the

interconnectivity of the dynamic behaviour of risks. For complex systems, like an

economy or financial organisations, a new paradigm or philosophy is required to

understand how the constituent parts interact to create behaviours not predictable

from the ‘sum of the parts’. Systems theory provides a more robust conceptual

framework which views risk as an emerging property arising from the complex and

adaptive interactions which occur within companies, sectors and economies.

• Risk appetite is a concept that many practitioners find confusing and hard to

implement. The fundamental problem is that there is no common measure for all

risks, and it is not always clear how different risk factors should be limited in order to

remain within an overall “appetite”. Attempts are generally made to force everything

into an impact on profit or capital but this is problematic when businesses and risk

decisions become more complex. There is a lack of real understanding about how

they would propagate, or indeed how the appetite may shift or evolve to have a

preference for specific risks.

Risk Research

• By thinking holistically, risk appetite can be viewed as “our comfort and preference for

accepting a series of interconnected uncertainties related to achieving our strategic

goals”. By making those uncertainties and the connectivity of the underlying drivers

explicit, it is possible for decision makers to define their risk appetite and monitor

performance against it more effectively. The ability to link multiple factors back to

financial outcomes also makes the challenge of expressing risk appetite in those

terms more tractable.

• Similarly, the identification and assessment of emerging risks can become more

robust by using a systems approach that enables a clearer understanding of the

underlying dynamics that exist between the key factors of the risks themselves. It is

possible to identify interactions in a system that may propagate hitherto unseen risks.

Emerging risks can be viewed as evolving risks from a complex system. It is also

known that such systems exhibit signals in advance of an observable change in

overall performance. Knowing how to spot and interpret those signs is the key to

building a scientific and robust emerging risk process. Also it is becoming increasingly

clear that risk appetite and emerging risks are interconnected in numerous complex

relationships over many layers.

Risk Research

• Assuming that strategic goals are already identified, establishing a risk appetite framework

comprises two distinct parts, one top down and the other bottom up. First, it is necessary

to describe how much uncertainty about the achievement of specific business goals is

acceptable, and what the key sources of that uncertainty are. Second, it is necessary to

identify the key operational activities or actions which contribute towards each source of

uncertainty and then apply the necessary limits to those activities to maintain performance

within the desired risk appetite.

• Systems techniques used in the case study proved extremely effective at helping

businesses to explain their understanding of how uncertainty arises around their business

goals. Cognitive mapping was used to elicit a robust understanding of the business

dynamics creating uncertainty in business goals. This process was useful for engaging the

business and capturing their collective knowledge of the risk appetite problem.

• By carrying out a mathematically based analysis on the cognitive maps it is possible to

quickly and objectively identify which parts of the description are most important in driving

explaining the uncertainties we are attempting to constrain. It also highlights areas which

have not been particularly well described or understood, prompting further discussion and

analysis. This provides a hypothesis for our risk appetite, and associated limit, framework.

Risk Research

Bayesian Networks

• Bayesian Networks are proposed as a mechanism to provide a dynamic model of how

various risk factors connect and interact. This links the behaviour of the operational

activities to the levels of risk they produce and can be parameterised through a

combination of qualitative and quantitative data. Bayesian Networks permit evidence

to propagate up and down the model, providing the business with a robust method for

determining risk limits by setting the level of risk to be at the risk appetite point and

observing what level the limits should be to ensure compliance with this level of risk.

• Alternatively, the observed indicator values can be entered and the implied level of

risk is computed. Making this linkage explicit provides a mechanism for companies to

understand more immediately where their risk exposure is coming from and how to

control it.

Risk = Impact x Probability

Risk Complexity Map

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what

they can do with it - is largely unknown, undisclosed and un-policed.....

Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and

who we see is now watched and recorded

“Big Data”

“Big Data” Global Content Analysis

• “Big Data” refers to those aggregated datasets whose size and scope is beyond the capability of conventional transactional Database Management Systems and Enterprise Software Tools to capture, store, analyse and manage. This definition of “Big Data” is of necessity subjective and qualitative – “Big Data” is defined as a large collection of unstructured information, which, when initially captured, contains sparse or undiscovered internal references, links or data relationships.

• Data Set Mashing or “Big Data” Global Content Analysis – supports Strategic Foresight Techniques such as Horizon Scanning, Monitoring and Tracking by taking numerous, apparently un-related RSS and other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH Structures and Unstructured Databases and Document Management Systems for interrogating using Data Mining and Real-time Analytics – that is, searching for and identifying possible signs of hidden data relationships (Facts/Events) – in order to discover and interpret previously unknown “Weak Signals” indicating emerging and developing Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative transformations, catalysts and agents of change which may develop and unfold as future “Wild Card” or “Black Swan” events.

Data-driven v. Model-driven Domains Model-driven

Data-driven Rationalism

Positivism Gnosticism, Sophism

Reaction

Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics

Business Studies / Administration / Strategy

Psychology / Psychiatry / Medicine / Surgery

Behavioural Research Domains

Arts and the Humanities

Life Sciences

History Arts Literature Religion

Law Philosophy Politics

Biological basis of Behaviour

Biology Ecology Anthropology and Pre-history

Clinical Trials / Morbidity / Actuarial Science

“Goal-seeking” Empirical Research Domains

Applied (Experimental) Science

Earth Sciences

Economic Analysis

Classical Mechanics (Newtonian Physics)

Applied mathematics

Geography

Geology

Chemistry

Engineering

Geo-physics Environmental Sciences

Archaeology

Palaeontology

“Blue Sky” – Pure Research Domains

Future Management

Pure (Theoretical) Science

Quantitative Analysis

Computational Theory / Information Theory

Astronomy

Cosmology

Relativity

Astrophysics

Astrology

Taxonomy and Classification

Climate Change

Complex and Chaotic Research Domains

Narrative (Interpretive) Science

Statistics

Strategic Foresight

Data Mining “Big Data” Analytics

Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics

Complex Systems – Chaos Theory

Futures Studies

Weather Forecasting Predictive Analytics

Wave-form Analytics in “Big Data”

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle – and

has been adapted and introduced very recently into the study of noisy, complex,

compound and dynamic data streams in the field of complex and adaptive systems

- human activity such as economic cycles, business cycles, patterns and trends.

• Trend-cycle decomposition is a critical technique for testing the validity of multiple

(compound) dynamic wave-form models competing in a complex array of

interacting and inter-dependant cyclic systems in the study of complex cyclic

phenomena - which are subject to both interventional (deterministic) and stochastic

(probabilistic) paradigms. There are a number of competing analytic paradigms in

the study of complex natural periodic biological, behavioural and economic

phenomena – which are in turn driven by either deterministic methods (goal-

seeking - testing the validity of a range of explicit / pre-determined / pre-selected

cycle periodicity value) or stochastic methods (random / probabilistic - testing

every possible implicit (detected) wave periodicity value – for identifying actual

wave periodicity values from “noise” – harmonic resonance / interference patterns.


• A fundamental challenge found everywhere in business cycle theory is how to

interpret very large scale / long period compound-wave (polyphonic) time series data

sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new

analytical too based on Time-frequency analysis – a technique which exploits the

wave frequency and time symmetry principle. The role of time scale and preferred

reference from economic observation are fundamental constraints for Friedman's

rational arbitrageurs - and will be re-examined from the viewpoint of information

ambiguity and dynamic instability.

• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for

revealing multiple and complex superimposed cycles or waves within dynamic, noisy

and chaotic time-series data sets. A variety of competing deterministic and

stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)

filter - may be deployed with the multiple-frequency mixed case of overlaid cycles

and system noise. The FD filter does not produce a clear picture of business cycles

– however, the HP filter provides us with strong results for pattern recognition of

multiple co-impacting business cycles. The existence of stable characteristic

frequencies in large economic data aggregations (“Big Data”) provides us with strong

evidence and valuable information about the structure of Business Cycles.


Wave-form Analytics in Natural Cycles

• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex

Adaptive System (CAS) behaviour - behaviour of ecologies which are more similar to an

organism than that of random and chaotic “Stochastic” systems. The remarkable long-

term stability and sustainability of cyclic climatic systems contrasted with random and

chaotic short-term weather systems are demonstrated by the metronomic regularity of

climate pattern changes driven by Milankovich, the 1470-year Dansgaard-Oeschger and

Bond Cycles – regular and predictable Solar and Oceanic Forcing Climate Sub-systems.

Wave-form Analytics in Human Activity Cycles

• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour -

more similar to an ecology than chaotic “Random” systems. The capacity of market

economies for cyclic “boom and bust” – financial crashes and recovery - can be seen

from the impact of Black Swan Events causing stock market crashes - such as the

failure of sovereign states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and

market participants (Lehman Brothers) due to oil price shocks, money supply shocks

and credit crises. Surprising pattern changes occurred during wars, arm races, and

during the Reagan administration. Like microscopy for biology, non-stationary time

series analysis opens up a new space for business cycle studies and policy diagnostics.


• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in an organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

Big Data Analytics Goes Big Time

Big Data Analytics Goes Big Time • Organizations around the globe and across

industries have learned that the smartest business decisions are based on fact, not gut feel. That means they're based on analysis of data, and it goes way beyond the historical information held in internal transaction systems. Internet click-streams, sensor data, log files, mobile data rich with geospatial information, and social-network comments are among the many forms of information now pushing information stores into the big-data league above 10 terabytes.

• Trouble is, conventional data warehousing deployments can't scale to crunch terabytes of data or support advanced in-database analytics. Over the last decade, massively parallel processing (MPP) platforms and column-store databases have started a revolution in data analysis. But technology keeps moving, and we're starting to see upgrades that are blurring the boundaries of known architectures. What's more, a whole movement has emerged around NoSQL (not only SQL) platforms that take on semi-structured and unstructured information.

This info-graph presents from 2011 to 2013 update on what's available, with options including ExtremeData xdb, EMC's Greenplum appliance, Hadoop and MapReduce, HP's recently acquired the Autonomy and Vertica platforms, IBM's separate DB2-based Smart Analytic System and Netezza offerings, and Microsoft's Parallel Data Warehouse. Smaller, niche database players include Infobright, Kognitio and ParAccel. Teradata reigns at the top of the market, picking off high-end defectors from industry giant Oracle. SAP's Sybase unit continues to evolve Sybase IQ, the original column-store database. In short, there's a platform for every scale level and analytic focus

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what they can do with it - is largely unknown, undisclosed and un-policed..... Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and who we see is now watched and recorded.

“Big Data” Definitions

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

“Big Data” Applications • Science and Technology

– Pattern, Cycle and Trend Analysis

– Horizon Scanning, Monitoring and Tracking

– Weak Signals, Wild Cards, Black Swan Events

• Multi-channel Retail Analytics – Customer Profiling and Segmentation

– Human Behaviour / Predictive Analytics

• Global Internet Content Management

– Social Media Analytics

– Market Data Management

– Global Internet Content Management

• Smart Devices and Smart Apps

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Broadband / Home Entertainment

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Smart Metering / Home Energy

– Energy Consumption Details Records

• Civil and Military Intelligence Digital Battlefields of the Future – Data Gathering

Future Combat Systems - Intelligence Database

Person of Interest Database – Criminal Enterprise,

Political organisations and Terrorist Cell networks

Remote Warfare - Threat Viewing / Monitoring /

Identification / Tracking / Targeting / Elimination

HDCCTV Automatic Character/Facial Recognition

• Security Security Event Management - HDCCTV, Proximity

and Intrusion Detection, Motion and Fire Sensors

Emergency Incident Management - Response

Services Command, Control and Co-ordination

• Biomedical Data Streaming Care in the Community

Assisted Living at Home

Smart Hospitals and Clinics

• SCADA Operational Technology SCADA Remote Sensing, Monitoring and Control

Smart Grid Data (machine generated data)

Vehicle Telemetry Management

Intelligent Building Management

Smart Homes Automation

Exploitation – “Big Data”

• There has been much speculation about how industries will cash in on “Big Data” In a nutshell “Big Data” occurs in volumes or structures that exceeds the functionality / capacity of conventional hardware, database platforms and analytical tools.

• Social media and search are leading the way with big data applications. As “Big Data” tools and methods enter the mainstream we will see businesses make use of the "data exhaust" that today doesn't get exploited To put it bluntly, most companies are failing to leverage their data assets by failing to realise the benefits of the huge volumes of data they are already generating.

Big Data Partnership

Training - For more information on Big Data Partnership’s training offerings, please visit the Training page. Feel free to Contact Us directly to discuss your specific needs.

http://bigdatapartnership.com/our-services/training/

http://bigdatapartnership.com/contact-us/

Big Data Partnership 3D Approach

• Discovery - As enterprises move into this new age for data analytics, companies can often struggle to identify where in their large data architecture, big data software and techniques can be utilised. Big Data Partnership can help those organisations understand where those use cases are through short workshop engagements. These are typically 2-5 days long and will help not only identify where Big Data Analytics could help drive more customer insight and ROI but also educate on what tools are in the eco-system.

• Develop - Even with solid use cases and a good understanding of how Big Data software and techniques could help businesses, it is not always easy to prove the model and commit to the necessary investment to really make the positive transformation in an organisation. One way of doing this is to take a single use case and develop a Proof of Concept to prove the expected ROI and business benefit and also validate the technology. This level of engagement can typically be a month long and can help businesses not only take the big step towards big data but also help them understand whether the expected ROI is there.

• Deliver - Big Data Partnership are able to assist enterprises in fully realising their Big Data initiatives through offering fixed price and day-based consultancy to help deliver full data analytics projects. We understand that each customer has differing needs, therefore we tailor our approach specific to each client. Effective big data is not just about predetermined buckets or templates for business intelligence; it is about meaningful analysis and processing of information in a way that is highly relevant to the business. We have highly skilled Data Scientists as well as deep rooted Big Data Engineers who can help you fully make the most of your implementations and ensure success of your Big Data projects.

“Big Data”

• Put yourself in the Big Data driver’s seat.

• Today, companies are generating massive amounts of data—everything from web clicks, to customer transactions, to routine business events—and attempting to mine that data for trends that can inform better business decisions.

• Quantivo enables a new analytics experience that is bound only by imagination of the user - it’s a full stack for turning raw data into intelligence. The Quantivo platform features patented, pattern-based technology that efficiently integrates event data across multiple sources, in hours not weeks. Your query quest starts here.

“Big Data” Analytics

Quantivo sifts through mountains of data—and spots the patterns that matter.

• When faced with overwhelming amounts of data, looking for the big “aha” can be next to impossible. That is, unless you’ve got Quantivo on your side. Unlike the other vendors that overpromise and under-deliver, Quantivo hits the mark with pattern-based analytics that brings Big Data down to size by tracking relationships between attributes and ignoring redundancies. With easy-to-use tools, users can zero in on predictive and repeatable patterns and trends—without losing any of the original data. In addition, Quantivo pattern-based analytics: -

– Creates behavioural segments derived from a combination of contextually specific attributes and online/offline detailed event data

– Uncovers buried relationships that link attributes to behaviours

– Tracks how behaviors change over time—and identifies the trigger for these changes

– Helps you “learn what you don’t know” by intelligently auto-compiling lists of patterns existing in your data

“Big Data” – Analysing and Informing

• “Big Data” is now a torrent raging through every aspect of the global economy – both the public sector and private industry. Global enterprises generate enormous volumes of transactional data – capturing trillions of bytes of information from their extended supply chain – global markets, customers and suppliers – and from their own internal business operations.

– SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN?

– GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE?

– INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY?

– SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT ?

– COMMUNICATION LAYER – Mobile Enterprise Platforms

– INFRASTRUCTURE LAYER – Cloud Service Platforms

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra


• SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN? – Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices

– Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV

– Remote Monitoring, Command and Control – SCADA

• GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE? – Person and Social Network Directories - Personal and Social Media Data

– Location and Property Gazetteers - Building Information Models (BIM)

– Mapping and Spatial Analysis – Landscape Imaging & mapping, Global Positioning (GPS) Data

– Temporal / Geospatial data feeds –Weather and Climate, Land Usage, Topology / Topography

• INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY? – Content – Structured and Unstructured Data and Content

– Information – Atomic Data, Aggregated, Ordered and Ranked Information

– Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks


• SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT? – Global Mapping and Spatial Analysis - GIS

– Service Aggregation, Intelligent Agents and Alerts

– Data Analysis, Data Mining and Statistical Analysis

– Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis an Extrapolation

• COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid – Connectivity - Smart Devices, Smart Apps, Smart Grid

– Integration - Mobile Enterprise Application Platforms (MEAPs)

– Backbone – Wireless and Optical Next Generation Network (NGE) Architectures

• INFRASTRUCTURE LAYER – Cloud Service Platforms – Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models

– Infrastructure – Network, Storage and Servers

– Applications – COTS Software, Utilities, Enterprise Services

– Security – Principles, Policies, Users, Profiles and Directories, Data Protection

Mobile Enterprise (MEAPs)

Risk Clustering in “Big Data” “A Cluster is a grouping of the same, similar and equivalent data

elements, containing values which are aggregated – or closely

distributed – together”

Clustering is a technique used to explore content and understand

information in every business and scientific field that collects and

processes verify large volumes of data

Cluster Analysis is an essential tool for solving any “Big Data” problem

Clustering Phenomena in “Big Data”

Risk Clustering in “Big Data”

Cluster Analysis – Key Issues

• Link / measure risk cost / mitigation to shareholder value (quantifiable) • Causal Layer Analysis (CLA) – systemic risk modelling • Efficiency / leaning / optimisation opportunity cost (quantifiable) • Real-time decision modelling on occurrence of every risk event • Decision Support using business intelligence and analytics • Risk Indexing – impact, compliance, threat environment • Systems Integration opportunities – change management • Predictive Analytics – risk event and customer behavioural analysis • Stress testing (war-gaming and detailed interdependent scenarios) • Future Management – early warning systems, critical success factors

key performance metrics, risk tolerance and volatility thresholds

• “Big Data” refers to vast aggregations (super sets) consisting of numerous individual

datasets (structured and unstructured) - whose size and scope is beyond the capability of

conventional transactional (OLTP) or analytics (OLAP) Database Management Systems

and Enterprise Software Tools to capture, store, analyse and manage. Examples of “Big

Data” include the vast and ever changing amounts of data generated in social networks

where we maintain Blogs and have conversations with each other, news data streams,

geo-demographic data, internet search and browser logs, as well as the ever-growing

amount of machine data generated by pervasive smart devices - monitors, sensors and

detectors in the environment – captured via the Smart Grid, then processed in the Cloud –

and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.

• Data Set Mashing and “Big Data” Global Content Analysis – drives Horizon Scanning,

Monitoring and Tracking processes by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS)

DWH Structures and Document Management Systems for Real-time Analytics – searching

for and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown Data Relationships driven by hidden Clustering

Forces – revealed via “Weak Signals” indicating emerging and developing Application

Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative

global transformations which may unfold as future “Wild Card” or “Black Swan” events.

“Big Data”

“Big Data”

The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Spatial versus Temporal Domains Spatial Analysis

(Location)

Temporal Analysis (History)

Sub-atomic

Phenomena Transitive Phenomena

Long-lived Phenomena

Space-Time Continuum

Global Phenomena Economic Analysis

Cosmic Space-Time

Temporal Analysis

Earth Sciences

“Goal-seeking” Empirical Research Domains Applied (Experimental) Science


Applied mathematics

Chemistry

Engineering

Geography

Geology


Archaeology

Palaeontology



Futures Studies

Weather Forecasting

Strategic Foresight


Predictive Analytics


Climate Change

Statistics

Cluster Theory Particle Physics

Quantum Mechanics



Phenomenology

Anthropology and Pre-history

Social Sciences

Sociology




Economics

Life Sciences





Biology Ecology


String Theory

Cosmology

Astronomy

Relativity

Astrophysics

Astrology

Future Management

Pure mathematics




Universal Phenomena

Local Phenomena

Regional Phenomena

Short-lived Phenomena

Atomic Space-Time

Micro-

Phenomena


1

2

3

4

5

7

8

6

The above is an illustration of risk relationships - how risk events might be connected. A detailed and

intimate understanding of risk clusters and the connection between risks may help us to understand: -

• What is the relationship between Risks 1 and 8, and what impact do they have on Risks 2 - 7 ?

• Risks 2 - 5 and Risks 6 and 7 occur in clusters – what are the factors influencing these clusters ?


strategy – and to decide how to better focus our resources and effort on risk and fraud management.

Claimant 1

Risk Event

Claimant 2 Residence

Vehicle

Risk

Cluster


• Aggregated risk includes coincident, related, connected and interconnected risk: -

• Coincident - two or more risks appear simultaneously in the same domain – but

they arise from different triggers (unrelated causal events)

• Related - two more risks materialise in the same domain sharing common risk

features or characteristics (may share a possible hidden common trigger or cause

– and so are candidates for further analysis and investigation)

• Connected - two more risks materialise in the same domain due to the same

trigger (common cause)

• Interconnected - two more risks materialise together in a risk cluster or event

series - the previous (prior) risk event triggering the subsequent (next) risk event

• Aggregated risks may result in a significant cumulative impact - and are therefore

frequently identified incorrectly as Wild-card or Black Swan Events - rather than just

simply as risk clusters or event “storms”.....

Aggregated Risk

A Trigger A

Coincident Risk

B Trigger B

Risk Event

Risk Event

C Trigger

Related Risk

D Trigger

Risk Event

Risk Event

E

Trigger

Connected Risk

Risk Event

Risk Event F

G Trigger


Risk Event

Risk Event

H

• The profiling and analysis of very large aggregated datasets in order to determine a

‘natural’ or implicit structure of data relationships or groupings – in order to discover

hidden data relationships driven by unknown factors where no prior assumptions

are made concerning the number or type of groups discovered or Cluster / Group

relationships, hierarchies or internal data structures – is a critically important starting

point – and forms the basis of many statistical and analytic applications.

• The subsequent explicit Cluster Analysis of discovered data relationships is an

important and critical technique which attempts to explain the nature, cause and

effect of unknown clustering forces driving implicit profile similarities, mathematical

or geographic distributions. Geo-demographic techniques are frequently used in

order to profile and segment Demographic and Spatial data by ‘natural’ groupings –

including common behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes –

along with numerous other shared characteristics and common factors Cluster

Analysis attempt to understand and explain those natural group affinities and

geographical distributions using methods such as Causal Layer Analysis (CLA).....

Clustering in “Big Data”

Clustering in “Big Data”

“A Cluster is a group of profiled data similarities aggregated closely together”

• Clustering is an essential tool for any “Big Data” problem. Cluster Analysis of both

explicit (given) or implicit (discovered) data relationships in “Big Data” is a critical

technique which attempts to explain the nature, cause and effect of the forces which drive

clustering. Any observed profiled data similarities – geographic or temporal aggregations,

mathematical or statistical distributions – may be explained through Causal Layer Analysis.

• Cluster Analysis is a technique used to explore content and information in order to

understand very large volumes of data in every business and scientific field that collects

and processes vast quantities of machine generated (automatic) data

– Choice of clustering algorithm and parameters are processes and data dependent

– Approximate Kernel K-means provides a good trade-off between clustering accuracy

and data volumes, throughput, performance and scalability

– Challenges include homogeneous and heterogeneous data (structured versus

unstructured data), data quality, streaming, scalability, cluster cardinality and validity

Cluster Types Deep Space Galactic Clusters

Hadoop Cluster – “Big Data” Servers

Molecular Clusters

Geo-Demographic Clusters

Crystal Clusters

Cluster Types DISCIPLINE CLUSTER TYPE CLUSTERS DIMENSIONS DATA TYPE DATA SOURCE CLUSTERING

FACTORS / FORCES

Astrophysics Distribution of Matter through the Universe across Space and Time

Star Systems Stellar Clusters Galaxies Galactic Clusters

Mass / Energy Space / Time

Astronomy Images Optical Telescope Infrared Telescope Radio Telescope X-ray Telescope

Gravity Dark Matter Dark Energy

Climate Change Temperature Changes Precipitation Changes Ice-mass Changes

Hot / Cold Dry / Wet More / Less ice

Temperature Precipitation Sea / Land Ice

Average Temperature Average Precipitation Greenhouse Gases %

Weather Station Data Ice Core Data Tree-ring Data

Solar Forcing Oceanic Forcing Atmospheric Forcing

Actuarial Science Morbidity Epidemiology

Place / Date of birth Place / Date of death Cause of Death

Birth / Death Longevity Cause of Death

Medical Events Geography Time

Biomedical Data Demographic Data Geographic data

Register of Births Register of Deaths Medical Records

Health Wealth Demographics

Price Curves Economic Modelling Long-range Forecasting

Economic growth Economic recession

Bull markets Bear markets

Monetary Value Geography Time

Real (Austrian) GDP Foreign Exchange Rates Interest Rates Price movements Daily Closing Prices

Government Central Banks Money Markets Stock Exchange Commodity Exchange

Business Cycles Economic Trends Market Sentiment Fear and Greed Supply / Demand

Business Clusters Retail Parks Digital / Fin Tech Leisure / Tourism Creative / Academic

Retail Technology Resorts Arts / Sciences

Company / SIC Geography Time

Entrepreneurs Start-ups Mergers Acquisitions

Investors NGAs Government Academic Bodies

Capital / Finance Political policy Economic policy Social policy

Elite Team Sports Performance Science

Winners Loosens

Team / Athlete Sport / Club League Tables Medal Tables

Sporting Events Team / Athlete Sport / Club Geography Time

Performance Data Biomedical Data

Sports Governing Bodies RSS News Feeds Social Media Hawk-Eye Pro-Zone

Technique Application Form / Fitness Ability / Attitude Training / Coaching Speed / Endurance3

Future Management Human Activity Natural Events

Random Events Waves, Cycles, Patterns, Trends

Random Events Geography Time

Weak Signals Wild Card Events Black Swan Events

Global Internet Content / Big Data Analytics - Horizon Scanning, Tracking and Monitoring

Random Events Waves, Cycles, Patterns, Trends, Extrapolations

GIS MAPPING and SPATIAL DATA ANALYSIS

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.....

GIS MAPPING and SPATIAL DATA ANALYSIS

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes.

• Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).

World-wide Visitor Count – GIS Mapping

Geo-Demographic Profile Data GEODEMOGRAPHIC INFORMATION – PEOPLE and PLACES

Age Dwelling Location / Postcode

Income Dwelling Owner / Occupier Status

Education Dwelling Number-of-rooms

Social Status Dwelling Type

Marital Status Financial Status

Gender / Sexual Preference Politically Active Indicator

Vulnerable / At Risk Indicator Security / Threat Indicator

Physical / Mental Health Status Security Vetting / Criminal Record Indicator

Immigration Status Profession / Occupation

Home / First language Professional Training / Qualifications

Race / ethnicity / country of origin Employment Status

Household structure and family members Employer SIC

Leisure Activities / Destinations Place of work / commuting journey

Mode of travel to / from Leisure Activities Mode of travel to / from work

BTSA Induction Cluster Map

Geo-Demographic Profile Clusters

Star Clusters

• New and

improved

understanding

of star cluster

physics brings

us within reach

of answering a

number of

fundamental

questions in

astrophysics,

ranging from

the formation

and evolution

of galaxies –

to intimate

details of the

star formation

process itself.

Hertzsprung Russell

• The Hertzsprung

Russell diagram is a

scatter plot Cluster

Diagram which shows

the Main Sequence

Stellar Lifecycles.

• A Hertzsprung Russell

diagram is a scatter

plot Stellar Cluster

Diagram which

demonstrates the

relationship between a

stars temperature and

luminosity over time –

using red to blue colour

to indicate the mean

temperature at the

surface of the star.

Star

Clusters • The Physics of star

clustering leads us

to new questions

related to the

make-up of stellar

clusters and

galaxies, stellar

populations in

different types of

galaxy, and the

relationships

between high-

stellar populations

and local clusters –

overall, resolved

and unresolved –

the implications

for their relative

formation times

and galactic star-

formation histories.

Cluster Analysis

• Data Representation – Metadata - identifying common Data Objects, Types and Formats

• Data Taxonomy and Classification – Similarity Matrix (labelled data)

– Grouping of explicit data relationships

• Data Audit - given any collection of labelled objects..... – Identifying relationships between discrete data items

– Identifying common data features - values and ranges

– Identifying unusual data features - outliers and exceptions

• Data Profiling and Clustering - given any collection of unlabeled objects..... – Pattern Matrix (unlabelled data)

– Discover implicit data relationships

– Find meaningful groupings (Clusters)

– Predictive Analytics – Event Forecasting

– Wave-form Analytics – Periodicity, Cycles and Trends

– Explore hidden relationships between discrete data features

Many big data problems feature unlabeled objects

Distributed Clustering Models

Number of processors

Speedup Factor - K-means

Speedup Factor - Kernel K-means

2 1.1 1.3

3 2.4 1.5

4 3.1 1.6

5 3.0 3.8

6 3.1 1.9

7 3.3 1.5

8 1.2 1.5

K-means

Kernel K -means

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core

Intel Xeon processors, with 8GB memory in intel07 cluster

Network communication cost increases with the no. of processors

Cluster Analysis

Clustering Algorithms

Hundreds of spatial, mathematical and statistical clustering algorithms are available –

many clustering algorithms are “admissible” – but no single algorithm alone is “optimal”

• K-means

• Gaussian mixture models

• Kernel K-means

• Spectral Clustering

• Nearest neighbour

• Latent Dirichlet Allocation

Challenges in “Big Data” Clustering

• Data quality

• Volume – number of data items

• Cardinality – number of clusters

• Synergy – measures of similarity

• Values – outliers and exceptions

• Cluster accuracy - validity and verification

• Homogeneous versus heterogeneous data (structured and unstructured data)

k-means/Gaussian-Mixture Clustering of Audio Segments

Distributed Clustering Model Performance

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster Network communication cost increases with the no. of processors

K-means Kernel K -means

HPCC Clustering Models

High Performance / High Concurrence Real-time Delivery (HPCC)

Distributed Clustering Models

Distributed Clustering Model Performance

Distributed Approximate Kernel K-means

2-D data set with 2 concentric circles

2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster

Run-time

Size of dataset (no. of Records)

Benchmark Performance (Speedup Factor )

10K 3.8

100K 4.8

1M 3.8

10M 6.4

Enterprise Risk Management: - TECHNICAL APPENDICES


Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems

Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures


Enterprise Risk Management Research

Changement est vieux comme le monde….. le changement est aussi vieux que le temps.

//upload.wikimedia.org/wikipedia/commons/2/22/An_Experiment_on_a_Bird_in_an_Air_Pump_by_Joseph_Wright_of_Derby,_1768.jpg

Research Philosophies, Paradigms, Investigative and Analytic Methods

PROBABILISTIC versus DETERMINISTIC PARADIGMS

Rationalism – “blue-sky” pure research - the stance of the natural scientist Rationalism can be defined as “probabilistic research approaches that employ forensic and

analytical methods, make extensive use of both qualitative and quantitative analysis - free from

any pre-determined behavioral models - in order to discover hidden or unknown truths”

Positivism – goal seeking - the stance of the applied scientist Positivism can be defined as “deterministic research approaches that employ empirical methods,

and make extensive use of quantitative analysis, or develop logical calculi in order to develop

hypotheses and build conceptual models in support of formal explanatory theory”

Research Philosophy

Random Event Clustering Patterns in the Chaos

The Nature of Uncertainty – Randomness Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects

– any apparent randomness is as a result of Unknown Forces

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics

Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object – apparent randomness and asymmetry is as a result of Quantum and Unknown Forces

Futures Studies

• Futures Studies, Foresight, or Futurology is the practice and art of postulating possible, probable, and preferable future outcomes. Futures studies (colloquially referred to as "Futures" by many of the field's practitioners) seeks to understand what is likely to continue, what is likely to change, and what will be completely new and novel. Part of the discipline seeks to develop a systematic and pattern-based understanding of the past, present and future, and thus attempts to determine both the content (description) and probability (likelihood of occurrence) of a wide range of possible, probable and alternative future outcomes, scenarios, events and trends.

• Futures is an interdisciplinary curriculum, studying yesterday's and today's changes, through aggregating and analysing both lay and professional strategies, views and opinions about the future with respect to what may happen tomorrow. This includes analysing the sources, patterns, and causes of change and stability in an attempt to develop foresight and to map possible futures. Futures Studies has been greatly enhanced by the recent arrival of “Big Data” technologies – which automates the process of Horizon (human domains) and Environment (natural domains) futures research - scanning, monitoring and tracking massive volumes of global Internet Content, Social Media Postings, RSS News Feeds and other Data Streams in order to discover “Weak Signals” and “Wild Cards” – predicators of future change.

• Around the world the field is variously referred to as futures studies, strategic foresight, futurology, futuristics, futures thinking, futuring, futuribles (in France, the latter is also the name of the important 20th century foresight journal published only in French), and prospectiva (in Spain and Latin America). Futures studies (and one of its sub-disciplines, strategic foresight) are the academic field's most commonly used terms.

Forecasting and Prediction • Forecasting is the process of logical estimation of events in unknown

future situations. Prediction is a similar, but less rigorous term. Both may refer to estimation of time series, cross-sectional or longitudinal datasets.

• Usage can differ between areas of application: for example in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for the logical projection of probable values at certain specific future times, while the term "prediction" may be used for more general estimates - such as the number of times flooding will occur over a given (longer) period.

• Risk and uncertainty are central to forecasting and prediction. Scenarios and Mathematical Models are both used in the practice of Forecasting for every day events such as weather forecasting for agriculture and shipping, and business performance forecasting for industry and commerce. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and scenario analysis.

• Forecasting is commonly used in discussion of time-series data where the timeline extends over historic (past), current (present) and future events.

http://en.wikipedia.org/wiki/Estimation

http://en.wikipedia.org/wiki/Prediction

http://en.wikipedia.org/wiki/Time_series

http://en.wikipedia.org/wiki/Cross-sectional_data



http://en.wikipedia.org/wiki/Longitudinal_study

http://en.wikipedia.org/wiki/Hydrology

http://en.wikipedia.org/wiki/Future

http://en.wikipedia.org/wiki/Risk

http://en.wikipedia.org/wiki/Uncertainty

Goal-seeking and Back-casting

• Back-casting starts by defining a desirable future and then works backwards to identify the policies and programs that will connect that desired future to the present situation. The empirical question of back-casting asks: - "if we want to attain a certain goal or set of objectives – then what actions must be taken in order to facilitate our journey and arrive at our desired outcomes ?“

• Forecasting is the process of predicting the future based on extrapolating current patterns and trends. Back-casting approaches the challenge of describing the future from the opposite direction: - “a method in which the future desired conditions are envisioned and steps are then defined to attain those conditions - rather than taking steps that are merely a continuation of present methods extrapolated into the future”

• Goal-seeking and Back-casting are a key component of the Soft Path, a concept developed by Amory Lovins in response to the shock of the 1973 energy crisis in the United States. Goal-seeking and Back-casting has been further developed, refined and deployed by various Future Research Groups e.g. “The Natural Step” (TNS) Framework.

• Back-casting is increasingly used in urban planning and resource management of water and energy. In 2006, the Capital Regional District Water Services, which services the greater Victoria area in British Columbia, Canada, committed to back-casting from the year 2050 as a formal element of all future strategic water planning initiatives.

http://en.wikipedia.org/wiki/Amory_Lovins

http://en.wikipedia.org/wiki/Amory_Lovins

http://en.wikipedia.org/wiki/1973_energy_crisis

http://en.wikipedia.org/wiki/Capital_Regional_District

http://en.wikipedia.org/wiki/Victoria

http://en.wikipedia.org/wiki/British_Columbia

http://en.wikipedia.org/wiki/Canada

Foresight • In Futures Studies, the term "Foresight " embraces: -

– Influencing public policy and strategic direction (“Shaping the future”)

– Critical thinking concerning long-term policy development (planning)

– Debate and consultation to create wider stakeholder participation (networking)

• Foresight is being applied to strategic activities in both the public and the private sector, and stresses the need to link every activity or project with any kind of future dimension towards taking action today (the action link) in order to make a planned, integrated future impact (“shaping the future”) possible.

• Foresight differs from much futures research and strategic planning, as it combines a range of approaches that encompasses the three key components highlighted above, which may be recast as: -

– futures (forecasting, forward thinking, perspectives) tools and methods

– planning (strategic analysis, priority setting) timelines and roadmaps

– networking (participatory, dialogic) inclusion and orientation

• Much futures research has been academic, but many Foresight programmes were designed to research Risk and influence Public Policy or explore Disruptive Change and influence Research and Development policy in industry. In the past some technology policy research has been very highly focused. Foresight attempts to go beyond the normal boundaries and gather much more widely distributed intelligence.

Foresight • Foresight draws on traditions of work in long-range forecasting and strategic

planning horizontal policymaking and democratic planning, horizon scanning and futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) - but was also highly influenced by systemic approaches to innovation studies, global design, massive change, science and technology futures, economic, social and demographic policy, fashion and design - and the analysis of "weak signals" and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".

– The longer-term: - futures that are usually at least 10 years away (though there are some exceptions to this, especially in its use in private business). Since Foresight is an action-oriented discipline (via the planning link) it will rarely be applied to perspectives beyond a few decades out. Where major infrastructure decisions such as petrology reservoir exploitation, aircraft design, power station construction, transport hubs and town master planning decisions are concerned - then the planning horizon may well be half a century.

– Alternative futures: - it is helpful to examine alternative paths of development, not just what is currently believed to be most likely or business as usual. Often Foresight will construct multiple scenarios. These may be an interim step on the way to creating what may be known as positive visions, success scenarios or aspirational futures. Sometimes alternative scenarios will be a major part of the output of a Foresight study, with the decision about what preferred future to build being left to other mechanisms (Planning and Strategy).

Foresight and Back-sight

• Foresight is the process of understanding the future based on extrapolation of current patterns and trends along with the analysis of the casual agents of random events and the contributory factors towards disruptive change.

• Back-sight approaches the challenge of examining the current state from the opposite direction - a method in which the current adverse conditions and its causes are analysed and then steps are identified that may have prevented those adverse conditions arising, mitigated the impact of those adverse conditions or simply avoided the consequences of those adverse conditions.

• Back-sight. In a post-apocalyptic Black Swan Event Scenario, we can use Causal Layer Analysis (CLA) techniques to review our Risk Analysis and Management Strategies in order to identify those Weak Signals which may have indicated subsequent Wild Cards – risk events which have a very low probability of occurring, but an inordinately high impact when they do happen – in order to determine future improvements and enhancements to Enterprise Risk Management Frameworks.

• Back-sight examines a Black Swan Event or Wild Card Scenario and then works backwards to identify those actions, policies, agents for change and events that connected the past to the present. The fundamental question of back-casting asks: "if we want to mitigate undesirable outcomes, what future actions could have be taken to avoid it happening or to reduce its impact?“

Strategic Foresight • Strategic Foresight is the ability to create and maintain a high-quality, coherent

and functional forward view, and to use the insights arising in useful organisational ways. For example to detect adverse conditions, guide policy, shape strategy, and to explore new markets, products and services. It represents a fusion of futures methods with those of strategic management (Slaughter (1999), p.287).

– Probabilistic Futures (Rational Futurism) – assessing possible, probable and alternative futures – selecting those futures offering conditions that best fit our strategic goals and objectives for achieving a preferred and desired future. Filtering for a more detailed analysis may be achieved by discounting isolated outliers and focusing upon those closely clustered future descriptions which best support our desired future outcomes, goals and objectives.

• Strategy Visioning – Future outcomes, goals and objectives are defined via Strategic Foresight and are determined by design, planning and management - so that the future becomes realistic and achievable. Possible futures may comply with our preferred options - and therefore our vision of an ideal future and desired outcomes could thus be fulfilled.

– Deterministic Futurism (Strategic Positivism) – articulating a single, preferred vision of the future. The future will conform to our preferred options - thus our vision of an ideal future and desired outcomes will be fulfilled.

Weak Signals and Wild Cards

• “Wild Card” or "Black Swan" manifestations are extreme and unexpected events which have a very low probability of occurrence, but an inordinately high impact when they do happen. Trend-making and Trend-breaking agents or catalysts of change may predicate, influence or cause wild card events which are very hard - or even impossible - to anticipate, forecast or predict.

• In any chaotic, fast-evolving and highly complex global environment, as is currently developing and unfolding across the world today, the possibility of any such “Wild Card” or "Black Swan" events arising may, nevertheless, be suspected - or even expected. "Weak Signals" are subliminal indicators or signs which may be detected amongst the background noise - that in turn point us towards any "Wild Card” or "Black Swan" random, chaotic, disruptive and / or catastrophic events which may be on the horizon, or just beyond......

• Weak Signals – refer to Weak Future Signals in Horizon and Environment Scanning - any unforeseen, sudden and extreme Global-level transformation or change Future Events in either the military, political, social, economic or the environmental landscape – some having an inordinately low probability of occurrence - coupled with an extraordinarily high impact when they do occur.

Horizon Scanning, Tracking and Monitoring

• Are you all at sea over your future.....?

Horizon Scanning • Horizon Scanning is an important technique for establishing a sound knowledge

base for planning and decision-making. Anticipating and preparing for the future – uncertainty, threats, challenges, opportunities, patterns, trends and extrapolations – is an essential core component of any organisation's long-term sustainability strategy.

• What is Horizon Scanning ?

Horizon Scanning is defined by the UK Government Office for Science as: -

“the systematic examination of potential threats, opportunities and likely future developments, including (but not restricted to) those at the margins

of current thinking and planning”.

• Horizon Scanning may explore novel and unexpected issues as well as persistent problems or trends. The government's Chief Scientific Adviser is encouraging every Department to undertake horizon scanning in a structured and auditable manner.

• Horizon Scanning enables organisations to anticipate and prepare for new risks and opportunities by looking at trends and information in the medium- to long-term future.

• The government's Horizon Scanning Centre of Excellence, part of the Foresight Directorate in the Department for Business, Innovation and Skills, has the role of supporting Departmental activities and facilitating cross-departmental collaboration.

Horizon Scanning, Tracking and Monitoring Processes

• Horizon Scanning, Tracking and Monitoring is a systematic search and examination of

global internet content – “BIG DATA” – information which is gathered, processed and

used to identify potential threats, risks, emerging issues and opportunities in the Human

World - allowing for the incorporation of mitigation and exploitation into in policy making

process - as well as improved preparation for contingency planning and disaster response.

• Horizon Scanning is used as an overall term for discovering and analysing the future of

the Human World – Politics, Economics, Sociology, Religion Culture and War –

considering how emerging trends and developments might potentially affect current policy

and practice. This helps policy makers in government to take a longer-term strategic

approach, and makes present policy more resilient to future uncertainty. In developing

policy, Horizon Scanning can help policy makers to develop new insights and to think

about “outside of the box” solutions to human threats – and opportunities.

• In contingency planning and disaster response, Horizon Scanning helps to manage risk

by discovering and planning ahead for the emergence of unlikely, but potentially high

impact Black Swan events. There are a range of Futures Studies philosophical

paradigms, and technological approaches – which are all supported by numerous

methods, tools and techniques for developing and analysing possible, probable and

alternative future scenarios.

Scenario Planning and Impact Analysis

• Scenario Planning and Impact Analysis is the archetypical method for futures studies

because it embodies the central principles of the discipline:

– The future is uncertain - so we must prepare for a wide range of possible, probable

and alternative futures, not just the future that we desire (or hope) will happen.....

– It is vitally important that we think deeply and creatively about the future, else we run

the risk of being either unprepared for, or surprised by events – or even both.....

• Scenarios contain the stories of these multiple futures - from the Utopian to the Dystopian,

from the preferred to the expected, from the Wild Card to the Black Swan - in forms which

are analytically coherent and imaginatively engaging. A good scenario grabs our attention

and says, ‘‘Take a good look at this future. This could be your future - are you prepared ?’’

• As consultants and stakeholders have come to recognize the value of scenario planning

and impact analysis, three scenario techniques have become default options – Outsights,

the Eltville Model (Pero Micic) and the Royal Dutch Shell / Global Business Network (GBN)

matrix approach, created by Pierre Wack in the 1970s and popularized by Schwartz (1991)

in “The Art of the Long View” – and by Professor Kees Van der Heijden of Saeed Business

School, Oxford University, in “Scenarios: The Art of Strategic Conversations” (1996). In

fact, Millett (2003, p. 18) calls this the ‘‘gold standard of corporate scenario generation’’.


Outsights "21 Drivers for the 21st Century"

• Outsights Strategy Scenarios are specially constructed stories about the future - each one portraying a distinct, challenging and plausible world in which we might one day live and work - and for which we need to anticipate, plan and prepare.

• The Outsights Technique emphasises collaborative scenario building with internal clients and stakeholders. Embedding a new way of thinking about the future in the organisation is essential if full value is to be achieved – a fundamental principle of the “enabling, not dictating” approach

• The Outsights Technique promotes the development and execution of purposeful action plans so that the valuable learning experience from “outside-in” scenario planning enables planning and building for profitable business change.

• The Outsights Technique develops scenarios at the geographical level; at the business segment, unit and product level, and for specific threats, risks and challenges facing organisations. Scenarios add value to organisations in many ways: - future management, business strategy, managing change, managing risk and communicating strategy initiatives throughout an organisation.


1. War, terrorism and insecurity 2. Layers of power 3. Economic and financial stability 4. BRICs and emerging powers • Brazil • Russia • India • China

5. The Five Flows of Globalisation • Ideas • Goods • People • Capital • Services

6. Intellectual Property and Knowledge 7. Health, Wealth and Wellbeing 8. Transhumanism – Geo-demographics,

Ethnographics and Social Anthropology 9. Population Drift, Migration and Mobility 10. Market Sentiment, Trust and Reputation 11. Human Morals, Ethics, Values and Beliefs

12. History, Culture, Religion and Human Identity 13. Consumerism and the rise of the Middle Classes 14. Social Media, Networks and Connectivity 15. Space - the final frontier

• The Cosmology Revolution - String Theory

16. Science and Technology Futures • The Nano Revolution • The Quantum Revolution • The Information Revolution • The Bio-Technology Revolution • The Energy Revolution • Oil Shale Fracking • • Kerogen • Tar Sands • Methane Hydrate • • The Hydrogen Economy • Nuclear Fusion •

17. Science and Society – the Social Impact of Disruptive Technology and Convergence

18. Natural Resources – availability, scarcity and control – Food, Energy and Water (FEW) crisis

19. Climate Change • Global Massive Change – the Climate Revolution

20. Environmental Degradation & Mass Extinction 21. Urbanisation and the Smart Cities of the Future


• Outsights Strategy Scenarios create a shared context, clarity and vision over challenging issues shaping the future in which decision makers can take better informed decisions on opportunity exploitation and risk management strategies.

• Managing Change Scenario thinking can compel a wide range of people to open up to new options and change their own images of reality by sharing and discussing assumptions on what is shaping the world.

• The Outsights Technique translates what is learnt into action in the following ways to achieve sustainable change and risk management : -

– Providing the content and insight needed to understand changes in the outside world (Drivers of Change, Scenario Building, Risk Categories)

– Designing and executing processes to devolve organisational change, business transformation and risk management down from the segment and business unit level to the individual responsible manager level – delivering personal accountability for Strategy & Planning, Budgeting & Forecasting, Change Management, Risk Management, Performance Management and Standards Compliance with Enterprise Governance, Reporting and Controls


• Outsights Strategy Scenarios supports a shared resource pool covering those issues shaping the future in which decision makers can make difficult choices about opportunity exploitation and risk management strategies.

• The Outsights Technique helps stakeholders stand back, take stock and seek fresh points of view: -

– Facilitation of the internal debate exploring stakeholder value, opportunity exploitation and risk management

– Sounding board for business innovation and strategy

– Stakeholder engagement and the communication of the process with the wider partner, stakeholder and employee community

– Review of specific opportunity exploitation and risk management agendas

– Surfacing diverse opinions from internal and external stakeholders to identify needs for strategic content, clarity, perspective and action


• The insights discovered by Scenario Planning and Impact Analysis can provide the basis

for prioritising research and development programmes, gathering business intelligence,

designing organisational scorecard objectives and establishing visions and strategies.

Steps

1. Participants are given a scope, focus and time horizon for the exercise.

2. Horizon Scanning, Monitoring and Tracking and Monte Carlo Simulations provide

sources of information. These data sets can come from internal or external sources

– Data Scientists, Domain Experts and Researchers, “Big Data” Analysts, the project

team, or from prior studies and data collection exercises from the individual team

participants. These should cover a broad external analysis, such as STEEP.

3. Individuals review the sources and spot items that cause personal insights on the

focus given. These insights and their sources are captured in the form of abstracts.

4. Abstracts are discussed and themed to indicate wave-forms over the time horizon

concerned. Scenarios are stacked, racked and prioritised by impact and probability.

5. The participants agree on how to address the resulting Scenarios, Waves, Cycles,

Patterns and Trends with supporting information for further futures analysis.

• More information about tools and uses of horizon scanning in Central Government can be

found on the Foresight Horizon Scanning Centre website.

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Review-of-abstracts.html

http://www.bis.gov.uk/foresight/our-work/horizon-scanning-centre

Seeing in Multiple Horizons: - Connecting Strategy to the Future

• THE THREE HORIZONS MODEL describes a Strategic Foresight method called “Seeing in Multiple Horizons: - Connecting Strategy to the Futures " The current THREE HORIZONS MODEL differs significantly from the original version first described in management literature over a decade ago. This model enables a range of Futures Studies techniques to be integrated with Strategy Analysis methods in order to reveal powerful and compelling future insights – and may be deployed in various combinations, whenever or wherever the Futures Studies techniques and Strategy Analysis methods are deemed to support the futures domains, subjects, applications and data in the current study.

• THE THREE HORIZONS MODEL method connects the Present Timeline with deterministic (desired or proposed) futures, and also helps us to identify probabilistic (forecast or predicted) future scenarios which may emerge as a result of interaction between embedded present-day factors and emerging catalysts of change – thus presenting us with a range of divergent possible futures. The “Three Horizons” method connects to models of change developed within the “Social Shaping” Strategy Development Framework via the Action Link to Strategy Execution. Finally, it summarises a number of futures applications where this evolving technique has been successfully deployed.

• The new approach to “Seeing in Multiple Horizons: - Connecting Strategy to the Future” has several unique features. It can relate change drivers and trends-based futures analysis to emerging issues. It enables policy or strategy implications of futures to be identified – and links futures work to processes of change. In doing so this enables Foresight to be connected to existing and proposed underlying system domains and data structures, with different rates of change propagation impacting across different parts of the system, and also to integrate seamlessly with tools and processes which facilitate Strategic Analysis. This approach is especially helpful where there are complex transformations which are likely to be radically disruptive in nature - rather than simple incremental transitions.

Andrew Curry Henley Centre HeadlightVision

United Kingdom

Anthony Hodgson Decision Integrity United Kingdom

Seeing in Multiple Horizons: - Connecting Strategy to the Future

The Three Horizons

Horizon and Environment Scanning, Tracking and Monitoring Processes

• Horizon and Environment Scanning, Tracking and Monitoring processes exploit the

presence and properties of Weak Signals – their discovery, analysis and interpretation

were first described by Stephen Aguilar Milllan in the 1960’s, and later popularised by

Ansoff in the 1970’s. Horizon Scanning is defined as “a set of information discovery

processes which data scientists, environment scanners, researchers and analysts use

to prospect, discover and mine the truly massive amounts of internet global content -

innumerable news and data feeds - along with the vast quantities of information stored

in public and private document libraries, archives and databases.”

• All of this external data is found widely distributed across the internet as Global Content

– RSS News Feeds and Data Streams, Academic Research Papers and Datasets - is

processed in order to detect and identify the possibility of unfolding random events and

clusters – “to systematically reduce the level of exposure to uncertainty, to reduce risk

and gain future insights in order to prepare for adverse future conditions – or to exploit

novel and unexpected opportunities for innovation" (LESCA, 1994). As a management

support tool for strategic decision-making, horizon and environment scanning process

have some very special challenges that need to be taken into account by environment /

horizon scanners, researchers, data scientists and analysts - as well as stakeholders.


• Horizon Scanning (Human Activity Phenomena) and Environment Scanning (Natural

Phenomena) are the broad processes of capturing input data to drive futures projects and

programmes - but they also refer to specific futures studies tool sets, as described below.

• Horizon Scanning, Tracking and Monitoring is a highly structured evidence-gathering

process which engages participants by asking them to consider a broad range of input

information sources and data sets - typically outside the scope of their specific expertise.

This may be summarised as looking back for historic Wave-forms which may extend into

the future (back-casting), looking further ahead than normal strategic timescales for wave,

cycle, pattern and trend extrapolations (forecasting), and looking wider across and beyond

the usual strategic resources (cross-casting). A STEEP structure, or variant, is often used.

• Individuals use sources to draw insights and create abstracts of the source, then share

these with other participants. Horizon scanning lays a platform for further futures activities

such as scenarios or roadmaps. This builds strategic analysis capabilities and informs

strategy development priorities. Once uncovered, such insights can be themed as key

trends, assessed as drivers or used as contextual information within a scenario narrative.

• The graphic image below illustrates how horizon scanning is useful in driving Strategy

Analysis and Development: -

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Scenarios.html

http://hsctoolkit.bis.gov.uk/Roadmaps.html

Strategy versus Horizon Scanning


• Horizon Scanning, Tracking and Monitoring is the major input for unstructured “Big Data” to

be introduced into the Scenario Planning and Impact Analysis process (along with Monte

Carlo Simulation and other probabilistic models providing structured data inputs). In this

regard, Scenario Planning and Impact Analysis helps to create a conducive team working

environment. It allows consideration of a broad spectrum of input data – beyond the usual

timescales and sources – drawing information together in order to identify future challenges,

opportunities and trends. It looks for evidence at the margins of current thinking as well as in

more established trends. This allows the collective insights of the group to be integrated -

demonstrating the many differing ways which diverse sources contribute to these insights.

• Horizon Scanning, Tracking and Monitoring is ideal as an initial activity for collecting Weak

Signal data input into the Horizon Scanning, Tracking and Monitoring process to kick-off

major futures studies projects and future management programmes. Scenario Planning and

Impact Analysis is also useful as a sense-making and interaction tool for an integrated

future-focused team. Horizon Scanning, Tracking and Monitoring combined with Scenario

Planning and Impact Analysis works best if people external to the organisation are included

in the team - and are encouraged to help bring together new and incisive perspectives.

• The graphic image below illustrates how horizon scanning is useful in spotting weak signals

that might be otherwise difficult to see – and so risk being overlooked: -

Seeing in Multiple Horizons

Horizon Scanning, Tracking and Monitoring Processes

• Horizon Scanning, Tracking and Monitoring is a systematic search and examination

of global internet content – “BIG DATA” – information which is gathered, processed and

used to identify potential threats, risks, emerging issues and opportunities as a result of

Human Activity - allowing for the incorporation of mitigation and exploitation themes into

in the policy making process – as well as improved preparation for business continuity,

contingency planning and disaster response, and enterprise risk management events.

• Horizon Scanning is used as an overall term for discovering and analysing the unfolding

future of the Human World – Politics, Economics, Sociology, Religion Culture and War –

considering how emerging trends and developments might potentially affect current policy

and practice. This helps policy makers in government to take a longer-term strategic

approach, and makes present policy more resilient to future uncertainty. In developing

policy, Horizon Scanning can help policy makers to develop new insights and to think

about “outside of the box” solutions to human activity threats – and opportunities.

• In contingency planning and disaster response, Horizon Scanning helps to manage risk

by discovering and planning ahead for the emergence of unlikely, but potentially high

impact Black Swan events. There is a wide range of Futures Studies philosophical

paradigms, and technology approaches – which are all supported by numerous methods,

tools and techniques for exploring possible, probable and alternative future scenarios.


• Horizon and Environment Scanning Event Types – refer to Weak Signals of any unforeseen,

sudden and extreme Global-level transformation or change Future Events in either the military,

political, social, economic or environmental landscape - having an inordinately low probability of

occurrence - coupled with an extraordinarily high impact when they do occur (Nassim Taleb).

• Horizon Scanning Event Types

– Technology Shock Waves

– Supply / Demand Shock Waves

– Political, Economic and Social Waves

– Religion, Culture and Human Identity Waves

– Art, Architecture, Design and Fashion Waves

– Global Conflict – War, Terrorism, and Insecurity Waves

• Environment Scanning Event Types

– Natural Disasters and Catastrophes

– Human Activity Impact on the Environment - Global Massive Change Events

• Weak Signals – are messages, subliminal temporal indicators of ideas, patterns, trends or

random events coming to meet us from the future – or signs of novel and emerging desires,

thoughts, ideas and influences which may interact with both current and pre-existing patterns

and trends to predicate impact or effect some change in our present or future environment.

Random Event Clustering

Patterns in the Chaos

Clustering of co-impacting Events

• It is the function of every Futures Researcher or Disruptive Futurist to seek out and

discover a combination, sequence or chain of apparently Random Events which

occur together in Time and Space as an integrated sequence (groups or clusters) of

linked events. Random Event Clusters interacting together demonstrate transient or

instantiated relationships or dependencies (are co-related or dependant) – that is,

they are influencing each other in some way or another. These hidden relationships

show up in the values of data items (variables) in the data stack when we apply

systems modelling techniques (to discover explicit data relationships) or “Big Data”

methods (to discover implicit data relationships) in order to resolve Future Domain

opportunities or threats, risks, challenges, issues or problems .

• What factors or forces do we need to consider as being in-scope and critical to the

overall behaviour of the system? Which other factors or forces have we ignored,

overlooked or simply failed to consider? What further unknown factors or unseen

forces are out there which we have not detected – but still exist – which are

somehow impacting upon the behaviour and outcomes of the observable system -

thus exerting minute but critical influence on system elements (objects and their

interactions ) through Space and Time?

Random Event Clustering Patterns in the Chaos

• The discovery of Chaos and Complexity has increased our understanding of the

Cosmos and its effect on us. If you surf the chaos content regions of the internet,

you will invariably encounter terms such as: -

• These influences can take some time to manifest themselves, but that is the nature

of the phenomena identified as a "strange attractor." Such differences could be

small to the point of invisibility - how tiny can influences be to have any effect?

This is captured in the “butterfly effect” scenario which is described later.

1. Attraction 14. Phase space and locking

2. Chaos 3. Clustering 4. Complexity 5. Butterfly effect 6. Disruption 7. Dependence 8. Feedback loops 9. Fractal patterns and dimensions 10. Harmonic Resonance 11. Horizon of predictability 12. Interference patterns 13. Massively diverse outcomes

15. Randomness 16. Repellence 17. Sensitivity to conditions 18. Self similarity (self affinity) 19. Starting conditions 20. Stochastic events 21. Strange attractors 22. System cycles (iterations) 23. Time-series Events 24. Turbulence 25. Uncertainty 26. Vanishingly small differences

http://syzygyastro.hubpages.com/hub/Defining-the-Fractal


• Nothing in the galaxy, in our world, or in our own personal existence, ever happens in isolation of other places, objects, individuals and events. This is a very simple and fundamental fact about life, nature the universe and everything.

• In precisely the same moment as one event occurs or one transformation happens in one location, infinite other events are taking place simultaneously in countless other locations - which in turn impact on an innumerable collection or set of other co-impacted objects, individuals and events.

• In order to study and prepare for Threat Analysis, Hazard and Risk Identification and Future Management - we need to bear in mind the fact that all objects and events are potentially connected in some way or other. None of these random events occurs in isolation, none are entirely independent or unconnected.

• Every object in the Universe exerts an influence over every other object, every process impacts on every other process – however tenuous these relationships. This phenomenon of Event Clustering is something that, through our own everyday experience, we are all familiar with, aware of and know about – and with training and preparation we are all easily able to follow, analyse, interpret and understand.


Multiple Random processes also occur in clusters.....

• Random Processes (with the notable exception of Quantum Events) are never truly or

completely random or symmetrical – they are triggered by the manifestation of “unseen

forces” interacting with complex systems. It is the nature of Random Processes to

generate Chaotic Events – which may occur together in multiple, related and similar

sequences as a result of these hidden forces.

• At the local level, we see stochastic processes at work when we experience the myriad

of phenomena that make up our everyday life experiences – which also have a tendency

to occur in groups or clusters. Almost without exception, we hear of events by type

occurring close together in temporal and spatial proximity. The saying that bad or good

news comes in groups has some validity based upon the nature of event clustering.

Human disasters – train, boat or plane accidents, along with natural disasters – volcanic

eruptions, earthquakes and tsunamis – often arrive in groups or clusters aggregated

together in Time and Space – separated by long periods of no such events.

Complex Systems and Chaos Theory

• There is an interesting Sensitive Dependence phenomenon called Phase

Locking where two loosely coupled systems with slightly different frequencies

show a tendency to move into resonance – in order to harmonise with one

another. We also know that the opposite of Attraction (or system convergence)

– Repellence (or system divergence) - is another type of System Dependency

possible with phase-locked systems/ Sensitive Dependence to external

forces demonstrates that minute, imperceptible changes to forces acting during

a system cycle are sufficient to dramatically alter the final state of the system,

which can display diverge trajectories with only very tiny inputs – especially if we

run those harmonised phase-locked systems in reverse.....(why ?)

• Phase locking draws two nearly harmonic systems into resonance and to the

observer, gives us the appearance of a “coincidence”. There are, however, no

such thing as coincidences in Newtonian Physics. Complexity Theory also

shows us that minute, imperceptible changes to input parameters at the initial

state of a system, at the beginning of a cycle - Sensitive Dependence to initial

conditions - are sufficient to dramatically alter the final state after even only a

few iterations of the system cycle. Such “coincidences” are, however, entirely

due to external forces acting upon the system - far beyond our ability to detect.


Attractors and Repellents

• Sensitive Dependence in Complexity Theory tells us that minute, imperceptible

changes to a system – at the beginning of a cycle, or dynamic forces acting as the

cycle evolves - are sufficient to dramatically alter the final state of the system -

even after a relatively few iterations of the system cycle.. Changes to a system at

the initial state constitutes Initial Sensitive Dependence, whilst dynamic external

forces acting on the system as the cycle evolves constitutes Dynamic Sensitive

Dependence. Thus Attractors and Repellents are examples of Dynamic

Sensitive Dependence.

• Any trajectory of the dynamic system in the attractor does not have to satisfy any

special constraints - except for remaining as an attractor. The trajectory may be

periodic or chaotic. In a set of periodic or chaotic points, if the average flow in the

neighbourhood is generally towards the set, then it is an attractor. If the average

neighbourhood flow is generally away from the set, then that set is instead

referred to as a repellent (repellor)..

http://en.wikipedia.org/wiki/Trajectory

http://en.wikipedia.org/wiki/Periodic_function

http://en.wikipedia.org/wiki/Chaos_theory

http://en.wikipedia.org/wiki/Repellor


Attractors and Repellents

• An attractor is a set within a dynamic system, towards which a moving variable

evolves over time. That is, points in that set get close enough to the attractor to

remain close - even when slightly disturbed by an external force The evolving

time-variant variable may be represented algebraically as an n-dimensional vector.

• The attractor is a region in n-dimensional space. In physical systems, the n

dimensions may be, for example, three positional coordinates and one temporal

co-ordinate for each of one or more physical entities; in economic systems, they

may be separate variables such as the inflation rate and the unemployment rate.

• If the evolving variable is two- or three-dimensional, the attractor of the dynamic

process can be represented geometrically in two or three dimensions, (as for

example in the three-dimensional case depicted to the right). An attractor can be

a point, a finite set of points, a curve, a manifold, or even a complicated set with

a fractal structure known as a strange attractor. If the variable is a scalar, the

attractor is a subset of the real number line. Describing attractors in dynamic

chaotic systems has been one of the greatest achievements of chaos theory.

http://en.wikipedia.org/wiki/Set_(mathematics)

http://en.wikipedia.org/wiki/Dynamical_system

http://en.wikipedia.org/wiki/Variable_(mathematics)

http://en.wikipedia.org/wiki/Algebra

http://en.wikipedia.org/wiki/Coordinate_vector

http://en.wikipedia.org/wiki/Space_(mathematics)



http://en.wikipedia.org/wiki/Physics

http://en.wikipedia.org/wiki/Economics

http://en.wikipedia.org/wiki/Inflation_rate

http://en.wikipedia.org/wiki/Unemployment_rate

http://en.wikipedia.org/wiki/Geometry

http://en.wikipedia.org/wiki/Point_(geometry)

http://en.wikipedia.org/wiki/Curve

http://en.wikipedia.org/wiki/Manifold

http://en.wikipedia.org/wiki/Fractal

http://en.wikipedia.org/wiki/Attractor

http://en.wikipedia.org/wiki/Scalar_(mathematics)



Strange Attractor

• A Strange Attractor has a fractal dimensional structure. This is often the case when the system

dynamics are chaotic. Strange attractors that are non-chaotic may also exist. The term Strange

Attractor was coined by David Ruelle and Floris Takens to describe the attractor resulting from

a series of bifurcations in a system modelling the heat convection dynamics of a fluid heated

from below and cooled at the top. This process drives Plate Tectonics in the Earth’s mantle –

causing the spreading of Oceans from the mid-oceanic rift and resulting in Continental Drift.

• Strange attractors are often differentiable in a few directions, but some are like Cantor dust, and

are therefore not differentiable. Strange attractors may also be found in presence of noise -

where they may be shown to support invariant Random Probability measures of Sinai-Ruelle-

Bowen type - see Chekroun et al. (2011).

• A Strange Attractor is an attracting set that has zero measure in the embedding phase

space and has fractal dimensions. Trajectories within a strange attractor appear to skip around

randomly. On the surface these three equations seem relatively simple to solve. However, they

represent an extremely complicated and variable dynamic system. If the results are plotted in

three dimensions, then the following three-dimensional figure, called the Lorenz attractor, is

obtained: -

http://en.wikipedia.org/wiki/Fractal


http://en.wikipedia.org/wiki/David_Ruelle

http://en.wikipedia.org/wiki/David_Ruelle

http://en.wikipedia.org/wiki/Floris_Takens



http://en.wikipedia.org/wiki/Bifurcation_theory

http://en.wikipedia.org/wiki/Differentiable

http://en.wikipedia.org/wiki/Homeomorphic

http://en.wikipedia.org/wiki/Cantor_dust

http://mathworld.wolfram.com/Measure.html

http://mathworld.wolfram.com/PhaseSpace.html

http://mathworld.wolfram.com/PhaseSpace.html

http://mathworld.wolfram.com/Fractal.html


• There is a further interesting phenomenon called Phase Locking where two

loosely coupled systems with slightly different frequencies show a tendency to

move into resonance – they are seeking to harmonise with one another. We also

know that the opposite of system convergence - system divergence - is also

possible with phase-locked systems, Sensitive dependence also tells us that very

tiny inputs are enough to completely alter the final state after several iterations of

the dynamic. We thus know of systems that diverge with only very tiny inputs, but

the opposite is also true with convergence, especially if we run things in reverse.

• Thus phase locking draws two nearly harmonic systems into resonance and gives

us the appearance of a “coincidence”. There are, however, no coincidences in

nature or Physics - all random processes (with the notable exception of Quantum

Events) – are neither truly random nor completely symmetrical – but are simply the

outcome of unseen forces acting on a system. Such 'coincidences' are like the

clusters of personality types that are governed by certain recurring planets -

according to the statistical researches of M. Guaquelin.

http://www.knowledge.co.uk/frontiers/sf061/sf061b06.htm



• Phase Divergence drives two phase-locked harmonic systems out of

synchronisation into random, chaotic and discordant behaviour - where phase

locked systems can diverge from each other with only very tiny inputs (especially

when we run those phase-locked harmonic system models in reverse).....

• It is safe to say that pure coincidence is a vanishingly small reality. In fact, it is safe to

say that phenomena such as coincidence (which is more properly called serendipity)

The fact that very complex systems are invoked – as seen drawing two interacting

bodies into perfect resonance - is due to unknown factors or unseen forces behind

effects such as phase locking, and sensitive dependence. Sensitive dependence and

the interaction of every object upon all of the rest accounts for the phenomenon of

clustering – not serendipity, coincidence or mere chance.....

• The structure of the universe is based on such stochastic events. Here too, we find

random clustering events. The distribution of matter in the universe is based on the

quantum foundation. Clustering at the quantum level when the universe was just a

few thousands of a millimetre across – has lead to the creation of the super massive

black holes at the centre of each galaxy which, through gravitational attraction drive

the clustering of star / planetary systems, star clusters, galaxies and galactic clusters.

http://www.umbrars.com/sr/introsr.htm

Complex Systems and Chaos Theory

• There are many kinds of stochastic or random processes that impact on every area of Natural

Cycles and Human Activity. Randomness can be found in Science and Technology and in

Humanities and the Arts. Random events are taking place almost everywhere we look – for

example from Complex Systems and Chaos Theory to Cosmology and the distribution and

flow of energy and matter in the Universe, from Brownian motion and quantum theory to

Fractal Branching and linear transformations. Further examples include Random Events,

Weak Signals and Wild Cards occurring in each aspect of Nature and Human Activity – from

Ecology and the Environment to Weather Systems and Climatology in Economics and in the

Biological basis of Behaviour. And then there are the examples of atmospheric turbulence,

and the complex orbital and solar interaction cycles – and much, much more besides.....

• There is an interesting phenomenon called Phase Locking where two loosely coupled

systems with slightly different frequencies show a tendency to move into resonance – in order

to harmonise with one another. We also know that the opposite of system convergence -

system divergence - is also possible with phase-locked systems, which can also diverge with

only very tiny inputs - especially if we run those systems in reverse. Thus phase locking

draws two nearly harmonic systems into resonance and gives us the appearance of a

“coincidence”. There are, however, no coincidences in Physics. Sensitive Dependence in

Complexity Theory also tells us that minute, imperceptible changes to inputs at the initial state

of a system, at the beginning of a cycle, are sufficient to dramatically alter the final state after

even only a few iterations of the system cycle.

Random Event Clustering – Patterns in the Chaos.....

Order out of Chaos – Patterns in the Randomness

• The long horizon of predictability for astronomical cycles and planetary alignment allows us to determine when events associated with the movement of the planets will exhibit a trendy to cluster. Planetary clustering in a non-cyclic periodic fashion will generate non-cyclic periodic effects, each object (sun, moon, planets) impacting upon all others.

• The Earth is not exempt from the forces of these objects. They manifest in many ways, obvious and subtle. Some are easy to understand, others are not. We can calculate the perturbation and tidal influences with some ease and match these with the real-life nature of these influences and effects that we experience. The Psychic influences of stochastic clustering are much harder to track. Such “coincidences” are the clusters of personality types that are “governed” by certain planetary influences - according to the statistical research of M. Guaquelin.

• We can calculate the perturbation and tidal influences with some ease and match these with real effects we experience. The psychic influences are much harder to track. They are there nonetheless as evidenced by the lunar and solar influences. The stochastic and clustering nature of these influences is what is behind the seeming stochastic and clustering nature of events we experience.



Random Event Clustering – Patterns in the Chaos.....

Order out of Chaos – Patterns in the Randomness

• Even when we look to the formation of solar systems, we see stellar evolution

mediated by forces, random events and harmonics in synchronicity. As random

events tend to cluster as part of the natural evolution of the universe, it is not

surprising to find that, as a natural consequence of this clustering, all complex

systems will evolve in a similar way. Planets in orbit around a star must have

orbital periods in dissonance to each other in order to have reasonable stability.

• This dissonance will evolve to create patterns that occur randomly in space and

time where planets aggregate together along one line of sight or another. Such

is the nature of the great planetary conjunctions - stelliums. In our solar system,

this kind of planetary alignment occurs roughly once very forty years, but no two

stelliums are ever exactly alike in planetary grouping, distribution or location in

reference to the other objects in the solar system - or even in alignment with

background stellar objects. Since planets orbit in more or less well-defined and

periods, these events are highly predictable - unlike the events in the quantum

realm or with a chained sequence of coin tosses forming random event clusters.


• Every Risk Analyst, Contingency Planner or Disruptive Futurist is continuously seeking to discover a combination, sequence or chain of events which occur together in clusters – and when acting together demonstrate either transient or instantiated dependencies (are interacting or co-related) or both. That is, they are impacting with each other in some way or another in a manner whereby we are able to forecast the next event. Basically, we need to apply complex systems and chaos theory thinking to resolving all Future Domain problems, opportunities, threats, issues or challenges.

• What factors or forces do we need to consider as being in-scope and critical to the behaviour of the system? Which other factors or forces have we ignored, overlooked or not considered? What further unknown factors or unseen forces are there which we have not detected – but may still exist – and what unknown factors or unseen forces are somehow exerting influence over the behaviour of the system subject to the study - thus influencing the behaviour of that systems elements as it evolves through Space and Time?

Complex Adaptive Systems Adaption and Evolution

When Systems demonstrate properties of Complex

Adaptive Systems (CAS) - often defined as a

collection or set of relatively simple and loosely

connected interacting systems exhibiting co-adapting

and co-evolving behaviour - then those systems are

much more likely to adapt successfully to their

environment and, thus better survive the impact of both

gradual change and of sudden random events.

Complex Adaptive Systems

• Complex Adaptive Systems (CAS) and Chaos Theory has also been

used extensively in the field of Futures Studies, Strategic Management,

Natural Sciences and Behavioural Science. It is applied in these domains

to understand how individuals within populations, societies, economies and

states act as a collection of loosely coupled interacting systems which

adapt to changing environmental factors and random events – biological,

ecological, socio-economic or geo-political.

• Complex Adaptive Systems (CAS) and Chaos Theory treats individuals,

crowds and populations as a collective of pervasive social structures which

may be influenced by random individual behaviours – such as flocks of

birds moving together in flight to avoid collision, shoals of fish forming a

“bait ball” in response to predation, or groups of individuals coordinating

their behaviour in order to respond to external stimuli – the threat of

predation or aggression – or in order to exploit novel and unexpected

opportunities which have been discovered or presented to them.


• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is

often defined as a collection or set of relatively simple and loosely connected interacting

systems exhibiting co-adapting and co-evolving behaviour (sub-systems or components

changing together in response to the same external stimuli) - then those systems are

much more likely to adapt successfully to their environment and, thus better survive the

impact of both gradual change and of sudden random events. Complexity Theory

thinking has been present in biological, strategic and organisational system studies since

the first inception of Complex Adaptive Systems (CAS) as an academic discipline.

• Complex Adaptive Systems are further contrasted compared with other ordered and

chaotic systems by the relationship that exists between the system and the agents and

catalysts of change which act upon it. In an ordered system the level of constraint means

that all agent behaviour is limited to the rules of the system. In a chaotic system these

agents are unconstrained and are capable of random events, uncertainty and disruption.

In a CAS, both the system and the agents co-evolve together; the system acting to

lightly constrain the agents behaviour - the agents of change, however, modify the

system by their interaction. CAS approaches to behavioural science seek to understand

both the nature of system constraints and change agent interactions and generally takes

an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.


• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in an population than to truly Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable long-term

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards) – by

the ability of Financial markets to rapidly absorb and recover from these events.

• Unexpected and surprising Cycle Pattern changes have historically occurred during

regional and global conflicts being fuelled by technology innovation-driven arms

races - and also during US Republican administrations (Reagan and Bush - why?).

Just as advances in electron microscopy have revolutionised the science of biology

- non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

Crowd Behaviour – the Swarm

• An example of Random Clustering is a Crowd or Swarm. There are a various forces

which contribute towards Crowd Behaviour – or Swarming. In any crowd of human

beings or a swarm of animals, individuals in the crowd or swarm are closely connected

so that they share the same mood and emotions (fear, greed, rage) and demonstrate

the same or very similar behaviour (fight, flee or feeding frenzy). Only the initial few

individuals exposed to the Random Event or incident may at first respond strongly and

directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat –

such as external predation, aggression or discovery of a novel or unexpected

opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).

• Those individuals who have been directly exposed to the initial “trigger” event or incident -

the system input or causal event that initiated a specific outbreak of behaviour in a crowd or

swarm – quickly communicate and propagate their swarm response mechanism and share

with all the other individuals – those members of the Crowd immediately next to them – so

that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.

• Peripheral Crowd members in turn adopt Crowd response behaviour without having been

directly exposed to the “trigger”. Members of the crowd or swarm may be oblivious to the

initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour

response quickly spreads to all of the individuals in or around that core crowd or swarm.

Crowd Behaviour – the Swarm

• One of the dangers posed by human crowd behaviour is that of “de-individualisation” in a

crowd, where a group of random individuals aggregate together and begin acting in concert

- adopting common behaviour, aims and objectives – and may begin to exhibit uninhibited

crowd responses to external information and stimuli. Crowd participants in this state begin

to respond without the usual constraints of their normal social, ethical, moral, religious and

behavioural rules. These are the set of circumstances which led to events such as the Arab

Spring and London Riots - which spread rapidly through deprived communities across the

country, both urban and rural. This type of collective group behaviour – such as a “feeding

frenzy” – has been observed in primates and carnivores - and even in rodents and fish.....

• Crowd behaviour is not just the domain of Demonstrators and Protesters - it can also be

seen in failing economies with the actions of Economic Planners in Central Banks - along

with their Political Masters – who also behave as a group of individuals acting together in

concert without the usual constraints – and thus, under extreme psychological stress as

systems such as the economy begins to collapse unpredictably – start to demonstrate "de-

individualisation" - collective uninhibited responses to external information and stimuli,

without the constraints of their normal political, economic, social, ethical, moral and

behavioural rules. These circumstances may lead to further panic and crowd behaviour

across Towns and Cities, Banks and Financial Institutions, ultimately Municipal, State and

Federal government departments - causing the failure of Global Markets or the fall of

Governments – as was recently witnessed in both the Arab Spring and the Euro Crisis.

Moore's Law

Moore's Law

• In 1965, the observation made by Gordon Moore, co-founder of Intel, is that the number

of transistors per square inch on integrated circuits had doubled every year since the integrated

circuit was invented. Moore predicted that this trend would continue for the foreseeable future. In

subsequent years, the pace of change has slowed down somewhat - but Data Storage Density

(gigabytes) has doubled approximately every 18 months - a definition which Moore himself has

blessed. The current definition of Moore's Law, accepted by most experts, including Disruptive

Futurists and Moore himself, is that Computing Power (gigaflops) will double about every two

years. Expect Moore's Law to hold good for at least another generation.....

• A forecast - and a challenge. Gordon Moore’s forecast for the pace of change in silicon

technology innovation - known as Moore's Law - essentially describes the basic business model

for the semiconductor industry. Intel, through investments in technology and manufacturing has

made Moore’s Law a reality. As transistor scale gets ever smaller Intel expects to continue to

deliver on Moore’s prediction well into the foreseeable future by using an entirely new transistor

formula that alleviates wasteful electricity leaks creating more energy-efficient processors.

• Exponential growth that continues today. Continuing Moore's Law means the rate of

progress in the semiconductor industry will far surpass that of nearly all other industries. The

future of Moore’s Law could deliver a magnitude of exponential capability increases, driving a

fundamental shift in computing, networking, storage, and communication devices to handle the

ever-growing digital content and Intel's vision of 15 billion intelligent, connected smart devices.

http://www.webopedia.com/TERM/I/Intel.html

http://www.webopedia.com/TERM/T/transistor.html

http://www.webopedia.com/TERM/I/integrated_circuit_IC.html

Forecasting and Predictive Analytics

• ECONOMIC MODELLING and LONG-RANGE FORECASTING •

• Economic Modelling and Long-range Forecasting is driven by atomic Data Warehouse

Structures and sophisticated Economic Models containing both Historic (up to 200 years daily

closing prices for Commodities, shares and bonds) and Future values (daily forecast and weekly

projected price curves, monthly and quarterly movement predictions, and so on for up to 50

years into the future – giving a total timeline of up to 250 years (Historic + 50 years Future trends

summary, outline movements and highlights). Forecast results are obtained using Economic

Models - Quantitative (technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis -

Economic Growth and Recession / Depression shapes and Commodity Price Data Sets) in order

to construct a continuous 100 year “window” into Commodity Price Curves and Business Cycles

for Cluster Analysis and Causal Layer Analysis (CLA) – which in turn is used for driving out

Qualitative (narrative) Scenario Planning and Impact Analysis for describing future narrative epic

stories, scenarios and use-cases.

• PREDICTIVE ANALYITICS and EVENT FORECASTING •

• Predictive Analytics and Event Forecasting uses Horizon Scanning, Tracking and Monitoring

methods combined with Cycle, Pattern and Trend Analysis techniques for Event Forecasting and

Propensity Models in order to anticipate a wide range of business. economic, social and political

Future Events – ranging from micro-economic Market phenomena such as forecasting Market

Sentiment and Price Curve movements - to large-scale macro-economic Fiscal phenomena

using Weak Signal processing to predict future Wild Card and Black Swan Events - such as

Monetary System shocks.


• MARKET RISK •

Market Risk = Market Sentiment – Actual Results (Reality)

• The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've

struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook,

burst on to the scene five years ago and have since grown into internet giants. Facebook has

over 900 million active members and Twitter over 250 million, with users posting over 2 billion

"tweets“ or messages every week. This provides hugely valuable and rich insights into how

Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels –

and so is also a source of real-time data that can be “mined” by super-fast computers to forecast

changes to Commodity Price Curves

• STRATEGIC FORESIGHT •

• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional

forward view, and to utilise Future Insights in order to gain Competitive Advantage - for example

to identify and understand emerging opportunities and threats, to manage risk, to inform

planning and forecasting and to shape strategy development. Strategic Foresight is a fusion of

Foresight techniques with Strategy Analysis methods – and so is of great value in detecting

adverse conditions, threat assessment, guiding policy and strategic decision-modelling, in

identifying and exploring novel opportunities presented by emerging technologies, in evaluating

new markets, products and services and in driving transformation and change.


• INNOVATION •

• Technology Innovation is simply combining existing resources in new and different ways –

in order to create novel and innovative Products and Services. Understanding the impact

of Technology Convergence is the Key to driving Innovation. Many common and familiar

objects in use today exist only as a result of technology convergence? Your average,

everyday passenger vehicle or laptop computer is the culmination of a series of technology

consolidation and integration events from a large number of apparently unrelated

technological innovations and advancements. Light-weight batteries were developed to

provide independence from fixed power sockets and hard-disk drives were made compact

enough to be installed in portable devices. The smart phone and tablet resulted from a

further convergence of technologies such as cellular telecommunications, mobile internet,

and Smart Apps - mini-applications that do not need an on-board hard-disk drive.

• FUTURE MANAGEMENT •

• Providing future analysis and strategic advice to stakeholders so that they might

understanding how the Future may unfold - in order to anticipate, prepare for and manage

the Future, to resolve challenging business problems, to envision, architect, design and

deliver novel solutions in support of major technology refreshment and business

transformation programmes • Future Analysis • Innovation • Strategic Planning •

Business Transformation • Technology Refreshment •


. • GEO-DEMOGRAPHICS •

• The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ or

implicit structure of data relationships or groupings where no prior assumptions are made

concerning the number or type of groups discovered or group relationships, hierarchies or

internal data structures - in order to discover hidden data relationships - is an important starting

point forming the basis of many statistical and analytic applications. The subsequent explicit

Cluster Analysis as of discovered data relationships is a critical technique which attempts to

explain the nature, cause and effect of those implicit profile similarities or geographic

distributions. Geo-demographic techniques are frequently used in order to profile and segment

populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity

or Actuarial outcomes, along with many other shared characteristics and common factors –and

then attempt to understand and explain those natural group affinities and geographical

distributions using methods such as Causal Layer Analysis (CLA).....

• Social Media is the fastest growing category of user-provided global content and will eventually

grow to 20% of all internet content. Gartner defines social media content as unstructured data

created, edited and published by users on external platforms including Facebook, MySpace,

LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition

to internal Corporate Wikis, special interest group blogs, communications and collaboration

platforms. Social Mapping is the method used to describe how social linkage between

individuals defines Social Networks and to understand the nature and dynamics of intimate

relationships between individuals


• GIS MAPPING and SPATIAL DATA ANALYSIS • • A Geographic Information System (GIS) integrates hardware, software, and

data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes. Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).


• “BIG DATA” •

• “Big Data” refers to vast aggregations (super sets) of individual datasets whose size and

scope is beyond the capability of conventional transactional Database Management

Systems and Enterprise Software Tools to capture, store, analyse and manage. Examples

of Big Data include the vast and ever changing amounts of data generated in social

networks where we have (unstructured) conversations with each other, news data streams,

geo-demographic data, internet search and browser logs, as well as the ever-growing

amount of machine data generated by pervasive smart devices - monitors, sensors and

detectors in the environment – captured via the Smart Grid, then processed in the Cloud –

and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.

• Data Set Mashing and “Big Data” Global Content Analysis – supports Horizon Scanning,

Monitoring and Tracking activities by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH

Structures and Document Management Systems for Real-time Analytics – searching for

and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown “Weak Signals” indicating emerging and

developing Application Scenarios, Patterns and Trends - in turn predicating possible,

probable and alternative global transformations unfolding as future “Wild Card” or “Black

Swan” events.


• WAVE-FORM ANAYITICS in “BIG DATA” •

• Wave-form Analytics help identify Cycles, Patterns and Trends in Big Data – characterised as

a sequence of high and low activity in time-series data – resulting in periodic increased and

reduced phases in regular, recurring cyclic trends. This approach supports an integrated study

of the impact of multiple concurrent cycles - and no longer requires iterative and repetitive

processes of trend estimation and elimination from the background “noise”.

• FORENSIC “BIG DATA” •

• Social Media Content and Spatial Mapping Data is used in order to understand intimate

personal relationships between individuals and to identify, locate and describe their participation

in various Global Social Networks. Thus the identification, composition, monitoring, tracking

,activity and traffic analysis of Social Networks Criminal Enterprises and Terrorist Cells – as

defined by common locations, business connections, social links and inter-personal

relationships – is used by Businesses to drive Influencer Programmes and by Government for

National Security, Counter-Terrorism, Anti-Trafficking, Criminal Investigation and Fraud

Prevention purposes.....

• Forensic “Big Data” combines the use of Social Media and Social Mapping Data in order to

understand intimate inter-personal relationships for the purpose of National Security, anti-

Trafficking and Fraud Prevention – through the identification, composition, activity analysis and

monitoring of Criminal Enterprises and Terrorist Cells.....

Threat Analysis, Hazard Research and Risk Management

Research Philosophy “Research philosophy is an over-arching term relating to the

development of knowledge - and understanding the nature of that

knowledge which is under development.....”

• Adapted from Saunders et al, (2009) •

Epistemology concerns the scope of what constitutes acceptable knowledge in a field of study.

Ontology is concerned with the nature of reality - and raises questions about assumptions

Risk Research Methods

When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,

Saunders et al, (2009)

Probabilistic v. Deterministic Domains Deterministic

Probabilistic Rationalism


Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


Reaction

Stoicism

Qualitative and Quantitative Methods

Research Study Roles and Responsibilities

• Supervisor – authorises and directs the Risk Research Study.

• Project Manager – plans and leads the Risk Research Study.

• Moderator – reviews and mentors the Risk Research Study.

• Researcher – undertakes the detailed Risk Research Tasks.

• Research Aggregator – examines hundreds of related Research

papers - looking for hidden or missed Findings and Extrapolations.

• Author – compiles, documents and edits the Research Findings.

Quantitative v. Qualitative Domains Quantitative (Technical)

Qualitative (Narrative)

Futures Studies

Numeric Definitive

Quantitative

(Technical) Analysis

Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change

“Goal-seeking” Empirical Research Domains Formulaic


Earth Sciences


Applied mathematics

Future Management

Environmental Sciences



Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics

Quantitative Analysis Pure mathematics

Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative

(Narrative) Analysis

Quantum Mechanics


Quantitative (Technical) Analysis

• Quantitative (Technical) Analysis involves studying detailed micro-economic models which process vast quantities of Market Data (commodity price data sets). This method utilises a form of historic data analysis technique which smoothes or profiles market trends into more predictable short-term price curves - which will vary over time within a specific market.

• Quantitative (Technical) Analysts can initiate specific market responses when prices reach support and resistance levels – via manual information feeds to human Traders or by tripping buying or selling triggers where autonomous Computer Trading is deployed. Technical Analysis is data-driven (experiential), not model-driven (empirical) because our current economic models do not support the observed market data. The key to both approaches, however, is in identifying, analysing, and anticipating subtle changes in the average direction of movement for Price Curves – which in turn reflect relatively short-term Market Trends.

Qualitative (Narrative) Analysis

• Qualitative (Narrative) Analysis involves further processing of summarised results generated by Quantitative (Technical) Analysis - super sets of many individual micro-economic model runs. Techniques such as Monte Carlo Simulation cycle macro-economic model runs repeatedly through thousands of iterations – minutely varying the starting conditions for each and every individual run cycle.

• Results appear as a scatter diagram consisting of thousands of individual points for commodity prices over a given time line. Instead of a random distribution – we discover clusters of closely related results in a background of a few scattered outliers. Each of these clusters represents a Scenario – which is analysed using Cluster Analysis methods - Causal Layer Analysis (CLA), Scenario Planning and Impact Analysis– where numeric results are explained as a narrative story about a possible future outcome – along with the probability of that scenario materialising.

Quantitative / Qualitative Analysis Techniques

TECHNICAL (QUANTITATIVE) METHODS TECHNICAL (QUANTITATIVE) METHODS (cont.)

Asymptotic Methods and Perturbation Theory Statistical Arbitrage

“Big Data” - Statistical analysis of very large scale (VLS) datasets Technical (Quant) Analysis

Capital Adequacy – Liquidity Risk Modelling – Basle / Solvency II Trading Strategies - neutral, HFT, pairs, macro; derivatives;

Convex analysis Trade Risk Modelling: – Risk = Market Sentiment – Actual Results

Credit Risk Modelling (PD, LGD) Value-at-Risk (VaR)

Data Audit, Data Profiling. Data Mining and CHAID Analysis Volatility modelling (ARMA, GARCH)

Derivatives (vanilla and exotics)

Dynamic systems behaviour and bifurcation theory NARRATIVE (QUALITATIVE) METHODS

Dynamic systems complexity mapping and network reduction

Differential equations (stochastic, parabolic) “Big Data” -, Clinical Trials ,Morbidity and Actuarial Outcomes

Extreme value theory Business Strategy, Planning, Forecasting Simulation and Consolidation

Economic Growth / Recession Patterns (Boom / Bust Cycles) Causal Layer Analysis (CLA)

Economic Planning and Long-range Forecasting Chaos Theory

Economic Wave and Business Cycle Analysis Cluster Theory

Financial econometrics (economic factors and macro models) Complexity Theory

Financial time series analysis Complex (non-linear) Systems

Game Theory and Lanchester Theory Complex Adaptive Systems (CAS)

Integral equations Computational Theory (Turing)

Interest rates derivatives Delphi Oracle /Expert Panel / Social Media Survey

Ordered (Linear) Systems (simple linear multi-factor equations) Economic Wave Theory – Business Cycles (Austrian School)

Market Risk Modelling (Greeks; VaR) Fisher-Pry Analysis and Gomperttz Analysis

Markov Processes Forensic “Big Data” – Social Mapping and Fraud Detection

Monte Carlo Simulations and Cluster Analysis Geo-demographic Profiling and Cluster Analysis

Non-linear (quadratic) equations Horizon Scanning, Monitoring and Tracking

Neural networks, Machine Learning and Computerised Trading Information Theory (Shannon)

Numerical analysis & computational methods Monetary Theory – Money Supply (Neo-liberal and Neo-classical)

Optimal Goal-seeking, System Control and Optimisation Pattern, Cycle and Trend Analysis

Options pricing (Black-Scholes; binomial tree; extensions) Scenario Planning and Impact Analysis

Price Curves – Support / Resistance Price Levels - micro models Social Media – market sentiment forecasting and analysis

Quantitative (Technical) Analysis Value Chain Analysis – Wealth Creation and Consumption

Statistical Analysis and Graph Theory Weak Signals, Wild Cards and Black Swan Event Forecasting



Qualitative Methods - tend to be deterministic, interpretive and subjective in nature. • When we wish to design a research project to investigate large volumes of unstructured data

producing and analysing graphical image and text data sets with a very large sample or set of information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity - what people think or feel about the world - enters into the scope (e.g. discovering Market Sentiment via Social Media postings), then the adoption of a qualitative research method is vital. If your aim is to understand and interpret people’s subjective experience and the broad range of meanings that attach to it, then interviewing, observation and surveying a range of non-numerical data (which may be textual, visual, aural) are key strategies you will consider. Research approaches such as using focus groups, producing case studies, undertaking narrative or content analysis, participant observation and ethnographic research are all important qualitative methods. You will also want to understand the relationship of qualitative data to numerical research. Any qualitative methods pose their own problems with ensuring the research produces valid and reliable results (see also: Analytics - Working with “Big Data”).

Quantitative Methods - tend to be probabilistic, analytic and objective in nature. • When we want to design a research project to tests a hypothesis objectively by capturing and

analysing numerical data sets with a large sample or set of information – then the quantitative method is preferred. There are many key issues to consider when you are designing an experiment or other research project using quantitative methods, such as randomisation and sampling. Also, quantitative research uses mathematical and statistical means extensively to produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).

Quantitative v. Qualitative Domains Quantitative (Technical)

Qualitative (Narrative)

Futures Studies

Numeric Definitive

Quantitative

(Technical) Analysis

Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change

“Goal-seeking” Empirical Research Domains Formulaic


Earth Sciences


Applied mathematics

Future Management

Environmental Sciences



Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics

Quantitative Analysis Pure mathematics

Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative

(Narrative) Analysis

Quantum Mechanics


Risk Research Philosophies and

Investigative Methods • This section aims to discuss Risk Research Philosophies in detail, in order to develop

a general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full Risk

Research Design. Kvale (1996) and Denzin and Lincoln (2003) highlight how different

Research Philosophies can result in much tension amongst research stakeholders.

• When undertaking any research of either a Scientific or Humanistic nature, it is most

important to consider, compare and contrast all of the varied and diverse Research

Philosophies and Paradigms that are available to the researcher and supervisor -

along with their respective treatments of ontology and epistemology issues.

• Since Research Philosophies and paradigms often describe dogma, perceptions,

beliefs and assumptions about the nature of reality and truth (and knowledge of that

reality) - they can radically influence the way in which the research is undertaken,

from design through to outcomes and conclusions. It is important to understand and

discuss these contrasting aspects in order that approaches congruent to the nature

and aims of the particular study or inquiry in question, are adopted - and to ensure

that researcher and supervisor biases are understood, exposed, and mitigated.

Risk Research Philosophies and

Investigative Methods • James and Vinnicombe (2002) caution that we all have our own inherent

preferences that are likely to shape our research designs and conclusions,

Blaikie (2000) describes these aspects as part of a series of choices that the

researcher has to consider, and demonstrates that this alignment that must

connect choices made back to the original Research Problem. If this is not

achieved, then certain research methods may be adopted which turn out to be

incompatible with the researcher’s stance, and result in the final work being

undermined through lack of coherence and consistency.

• Blaikie (1993) argues that Research Methods aligned to the original Research

Problem are highly relevant to Social Science since the humanistic element

introduces a component of “free will”’ that adds a complexity beyond those usually

encountered in the natural sciences – whilst others, such as Hatch and Cunliffe

(2006) draw attention to the fact that different paradigms ‘encourage researchers

to study phenomena in different ways’, going on to describe a number of

organisational phenomena from three different perspectives, thus highlighting

how different kinds of knowledge may be derived through observing the same

phenomena from different philosophical viewpoints and perspectives.

Aspects of Research Philosophy

• Rationalism – “blue-sky” pure research - the stance of the natural scientist – Rationalism can be defined as “probabilistic research approaches that employ forensic and

analytical methods, make extensive use of both qualitative and quantitative analysis - free from any pre-determined behavioral models - in order to discover hidden or unknown truths”

• Positivism – goal seeking - the stance of the applied scientist – Positivism can be defined as “deterministic research approaches that employ empirical

methods, and make extensive use of quantitative analysis, or develop logical calculi in order to develop hypotheses and build conceptual models in support of formal explanatory theory”

• Realism – direct and critical realism – The essence of realism is that what the senses show us as reality is the truth; that objects

have an existence independent of the human mind.

• Interpretation – researchers as ‘social actors’ – Interpretation advocates the necessity for researchers to understand differences between

humans in our role as social actors.

• Pragmatism – studies judgements about value – Pragmatism holds that the most important determinant of the epistemology, ontology,

axiology adopted is the research question

Saunders et al, (2009)

Probabilistic v. Deterministic Domains Deterministic

Probabilistic Rationalism


Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


Reaction

Stoicism

Human Activity Shock Waves

1. Stone – Tools for hunting, crafting artefacts and making fire

2. Fire – Combustion for warmth, cooking and for managing the environment

3. Agriculture – Neolithic Age Human Settlements

4. Bronze – Bronze Age Cities and Urbanisation

5. Ship Building – Communication, Culture ,Trade

6. Iron – Iron Age Empires, Armies and Warfare

7. Gun-powder – Global Imperialism, Colonisation

8. Coal – Mining, Manufacturing and Mercantilism

9. Engineering – Bridges, Boats and Buildings

10. Steam Power – Industrialisation and Transport

11. Industrialisation – Mills, Factories, Foundries

12. Transport – Canals, Railways and Roads

13. Chemistry – Dyestuff, Drugs, Explosives, Petrochemicals and and Agrochemicals

14. Electricity – Generation and Distribution

15. Internal Combustion – Fossil Fuel dependency

16. Aviation – Powered Flight – Airships, Aeroplanes

17. Physics – Relativity Theory, Quantum Mechanics

18. Nuclear Fission – Abundant Energy & Cold War

19. Electronics – Television, Radio and Radar

20. Jet Propulsion – Global Travel and Tourism

21. Global Markets – Globalisation and Urbanisation

22. Aerospace – Rockets, Satellites, GPS, Space Technology and Inter-planetary Exploration

23. Digital Communications – Communication Age -Computers, Telecommunications and the Internet

24. Smart Devices / Smart Apps – Information Age

25. Smart Cities of the Future – The Smart Grid – Pervasive Smart Devices - The Internet of Things

26. The Energy Revolution – The Solar Age – Renewable Energy and Sustainable Societies

27. Hydrogen Economy – The Hydrogen Age – fuel cells, inter-planetary and deep space exploration

28. Nuclear Fusion – The Fusion Age – Unlimited Energy - Inter-planetary Human Settlements

29. Space-craft Building – The Exploration Age - Inter-stellar Cities and Galactic Urbanisation

“Kill Moments” – Major Natural and Human Activity catastrophes – War, Famine, Disease, Natural Disasters

“Culture Moments” – Major Human Activity achievements - Technology Development, Culture and History

Industrial Cycles – the phases of evolution for any given industry at a specific location / time (variable)

Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Digital and Information Ages: -

Technology Shock Waves Type Force Technology Shock Wave Event

1 Technology

Shock Waves

Technology

Innovation

Stone – Tools for Hunting, Crafting Artefacts and Making Fire

Fire – Combustion - Warmth, Cooking, changing the Environment

Agriculture – Neolithic Age Human Settlements

Bronze – Bronze Age Cities and Urbanisation

Ship Building – Communication, Culture and Trade

Iron – Iron Age Empires, Armies and Warfare

Gun-powder – Global Imperialism and Colonisation

Coal – Mining, Manufacturing and Mercantilism

Engineering – Bridges, Boats and Buildings

Steam Power – Industrialisation and Transport

Industrialisation – Mills, Factories and Foundries

Transport – Canals, Railways and Roads

Chemistry – Dyestuff, Drugs, Explosives and Agrochemicals

Electricity – Generation and Distribution

Internal Combustion – Fossil Fuel dependency

Physics – Relativity Theory and Quantum Mechanics

Nuclear Fission – Abundant Energy and the Cold War

Electronics – Satellites and Space Technology

Digital Communications – The Information Age

Global Markets – Globalisation and Urbanisation

Smart Cities of the Future – The Solar Age – Renewable Energy

Nuclear Fusion– The Hydrogen Age - Inter-planetary Settlements

Space-craft Building – The Exploration Age - Inter-stellar Cities

Horizon Scanning, Tracking and Monitoring – Human Impact Scenarios

Type Force Random

Event

Weak

Signal

Strong

Signal

Wild card Black Swan

1 Oil-Price

Shock

Market

forces

Oil and gas

demand grows

beyond supply

Oil and Gas

Price inflation

2 Money

Supply Shock

Market

forces

Cash demand

grows beyond

money supply

Money Supply

shrinks, high

Interest rates

3 Sovereign

State Default

Market

forces

Public Debt

exceeds limits

Interest rates

rapidly rise

State cannot

raise Loans

State cannot

repay Loans

Sovereign Loan

Default Crisis

4 Food Crisis Natural +

Market

forces

Food demand

grows beyond

food supply

Food Price

inflation

Food

shortage -

hunger

Food crisis –

hunger and

illness

Famine – hunger,

illness, starvation

and death

5 Energy Crisis Natural +

Market

forces

Energy

demand grows

beyond supply

Energy Price

inflation

Energy

shortage

Energy crisis Energy Failure –

supply interruption

brown / black out

6 Water Crisis Natural

forces +

Human

Impact

Climate

Change

Rainfall

increases in

wet areas

High tide

with severe

storm cause

Sea / River

levels to rise

Coastal cities

and farmland

inundated by

Storm and

Tidal Surges

Flooding - Coast,

Deltas, Estuaries

and River Valleys

are submerged up

to 90km inland


forces +

Human

Impact

Climate

Change

Rainfall

decreases in

dry areas

Water

shortfall –

wells dry out

& crops fail.

Water crisis –

rivers no

longer reach

the sea

Drought –

Famine, Disease

(typhoid, cholera

and dysentery)

Fiscal Shock Waves

Type Force Fiscal Black Swan Event

1 Oil-Price

Shock

Market

forces

Cyclic Economic downturns and the global recessions that followed have

been linked with Oil price shocks since the 1970s. In the 1980s spurred on

by these events, Economists analysed the relationship between the price of

oil and industrial performance in a number of econometric studies, finding a

positive correlation in the US and other industrial countries between rising oil

prices and falling industrial output. The Oil Price shock of 2008 (oil prices

rose to well over $100 / barrel) had a negative impact on the world economy.

2 Money

Supply

Shock

Market

forces

The Money Supply Shock Event of 2008 led to the “Credit Crunch” Black

Swan Event. Fiscal Models of the demand and supply of money are either

inconsistent with the contemporary adjustment of the price level to expected

changes in the nominal money supply - or imply implausible fluctuations in

interest rates in response to unexpected changes in the nominal money

supply. A “shock-absorber” model of money demand and supply in which

money supply shocks affect the synchronisation of purchases and sales of

assets - creates a temporary desire to hold more or less liquidity (money

reserves) than would otherwise be the case. Estimated values for Shock-

absorber model variables improve the short-run money demand functions.

3 Sovereign

Sate Debt

Default

Market

forces

Whilst Portugal, Italy, Greece, Ireland, Iceland and Spain – even the USA –

might be on the brink of defaulting on their sovereign loan repayments –

causing global markets to plunge and economies to decelerate – historically

there’s nothing particularly new or unusual about this type of financial crisis.

Human Activity Cycles

Type Force Fiscal Cycles

1 Short Period

Human

Activity

Waves

Market

Forces

SHORT PERIOD HUMAN ACTIVITY WAVES

Seasonal Activities – Diurnal to Annual (1 day to 1 year)

– Farming, Forestry and Fishing

Price Curves – short-term, variable Market Trends

– Trading and Fiscal Cycles

2 Medium

Period Human

Activity

Waves

Market

Forces

MEDIUM PERIOD HUMAN ACTIVITY WAVES – Joseph Schumpter Economic Waves

Kitchin inventory cycle of 3–5 years (after Joseph Kitchin);

Juglar fixed investment cycle of 7–11 years (Clement Juglar - as 'the business cycle’);

Kuznets infrastructural investment cycle of 15–25 years (after Simon Kuznets);

Generation Wave – 20-25 years (four or five per Innovation Wave and Saeculum)

Innovation Wave – Major Scientific, Technology, Industrial Cycles and Waves @ 80 yr

Sub-Innovation Waves – Minor Technology Innovation Cycles @ 40 years

(2 x Kuznets Waves ?)

Kondratiev wave or long technological cycle of 45–60 years (after Nikolai Kondratiev)

Saeculum or Century Wave– Major Geo-political rivalry and conflict waves @ 100 years

Sub-Century Waves – Minor Arms Race Cycles @ 50 years

(1 x Kondratiev long technological wave ?)

3 Long Period

Human

Activity

Waves

Market

Forces

LONG PERIOD HUMAN ACTIVITY WAVES

Kill Moments – Major Human Activity threats – War, Famine, Disease, Natural Disasters

Culture Moments – Major Human Activity achievements – Science, Technology, Culture

Industrial Cycles – Evolution of any given industry at a specific location/time (variable)

Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Information Ages

Human Activity - Impact on the Environment

• The global shortage of Food, Energy and Water – the FEW Crisis

FEW Crisis


• FEW - Food, Energy, Water Crisis - as scarcity of Natural Resources (FEW - Food, Energy,

Water) and increased competition from a growing population to obtain those scarce resources

begins to limit and then reverse population growth, global population levels will continue

expansion towards an estimated 8 or 9 billion human beings by the middle of this century –

and then collapse catastrophically to below 1 billion – slowly recovering and stabilising out

again at a sustainable population of about 1 billion human beings by the end of the century.

• The decline in quality and quantity of fresh water, combined with increased competition

among resource-intensive systems, such as food and energy production, is resulting in a

water supply crisis. The 2013 World Economic Forum Global Risks Report identified that the

water supply crisis is one of the top five risks in both likelihood of occurrence and severity of

impact on society over the next 10 years. The risks “underscore the need for technological

innovation to transform the way that we treat, distribute, use, recover, clean and reuse water

toward a differential, distributed and localised water treatment and reuse paradigm (i.e., treat

water and wastewater locally only to the required level dictated by the next intended use).”

• Estimates by the United Nations suggest that by 2050 the global population will increase to 9

billion, 50% greater than the population in 2010 (Figure 1), and that by 2025 nearly half of the

world’s population will be living in megacities – which may not necessarily be located in areas

where there is a sustainable, renewable or even a reliable water supply.....

FEW Crisis

The Food Energy and Water (FEW) Crisis

FEW Crisis

• The food inflation index in India rose 11.43% in the year to November 2013. Food

Price inflation is a “Weak Signal” predicating a forthcoming Food Shortage (Strong

Signal), which is often followed by a Food Crisis (Wild Card) and finally a Famine

(Black Swan Event) arrives.

• The fuel price index climbed 14.70% during the same period. The food price index

and fuel inflation stood at 10.60% and 15.17%, respectively in the previous month,

October. The primary articles price index rose was up 11.75%, compared with an

annual rise of 11.18%.

• Merchants – middlemen who loan farmers money to plant and sow crops, secured

against the following years crop – begin Stockpiling Produce (onions, cooking oil,

spices, rice) and Food Hoarding in anticipation of higher future prices – thus causing

food shortages in towns and cities and so driving up food prices (food inflation).

• Large global Agronomy corporations distort local food demand and supply by dumping

imported crops at below the price of production in a food glut (e.g. Importing bananas

to producing countries), or favouring export markets over local markets during a food

shortage (e.g. exporting basmati rice from India to higher price markets in the west).

FEW Crisis

• Most economists agree that the Indian government has little room for manoeuvre – they

understand that the government has been unable to address pressure points in the food

supply chain, such as failures in food production, food hoarding and stockpiling by the food

merchants who buy produce directly from farmers, as well as logistics bottlenecks in food

distribution and supply further along the food chain. These factors all contribute towards

an increasing shortage of food reaching urban markets – and in turn drive food inflation.

• The Reserve Bank of India (RBI) increased its base interest rate by 25 basis points to 8.5

percent at the end of November 2013. The central bank has raised its key benchmark

rates 12 times in the last 18 months – causing economic growth in India to slow down and

stall.

• Market analysts were earlier supportive of this fiscal stance to some extent - but over

recent time, they have become somewhat frustrated with the early inflexibility and later

rigidity of the RBI and now market sentiment greets the raising of interest rates with

dismay – as it has been largely ineffective in tackling inflation.

• The RBI is expecting the annual inflation to fall to 7 percent by March and assures

analysts that further rate hikes will not be made if the inflation moderates as per

estimations the central

FEW Crisis


1 Food Crisis Natural

forces +

Market

forces

Food Price inflation is a “Weak Signal” predicating a forthcoming Food

Shortage (Strong Signal), which is often followed by a Food Crisis (Wild Card)

and then ultimately a Famine (Black Swan Event) arrives. Pressure points in

the food supply chain include failures in food production, food hoarding and

stockpiling by the food merchants who buy produce directly from farmers, and

logistics bottlenecks in food distribution and supply further along the food chain

by agronomy conglomerates. These factors contribute towards an increasing

shortage of food reaching urban markets – and so in turn drives food inflation.

2 Energy

Crisis

Natural

forces +

Market

forces

Energy Price inflation is a “Weak Signal” predicating a forthcoming Energy

Shortage (Strong Signal), which is often followed by a Energy Crisis (Wild

Card) and finally a collapse in Energy Supply (Black Swan Event). Pressure

points in the energy supply chain include Government intervention in Energy

Markets – energy policy, taxation, over regulation and failure to plan for

succession in energy production and supply. In the UK, demand exceeds

supply by 10% - the balance being imported from France. Closure of Nuclear

and Coal-fired power stations without adequate replacement means that from

2015-2025 the total UK energy shortfall will rise rapidly to between 20-30%


forces +

Market

forces

Global warming is likely to cross the critical threshold of 2C by the end of this

century. That would have serious consequences, including sea level rises,

heat-waves and changes to rainfall - meaning that dry regions get less and wet

areas receive more rain. More rivers will run dry before reaching the ocean.

Environment Scanning, Tracking and Monitoring Processes

• Environment Scanning, Tracking and Monitoring is a systematic search and examination of global internet content – “BIG DATA” – information which is gathered, processed and used to identify potential threats, risks, emerging issues and opportunities in the Physical World - allowing for the incorporation of mitigation and exploitation into in the policy making process - as well as improved preparation for contingency planning and disaster response.

• Environment Scanning is used as an overall term for analysing the future of the Physical World – ranging from extra-terrestrial threats to the Climate, the Environment and Ecological sub-systems - considering how emerging patterns and trends might potentially affect current policy and practice. This helps policy makers in government to take a longer-term strategic approach, and makes present policy more resilient to future uncertainty. In developing a Global Risk Management policy, Environment Scanning can help policy makers to develop new insights and to think differently about “outside of the box” solutions to climate, environmental and ecological threats – and opportunities.

• In contingency planning and disaster response, Environment Scanning helps to manage risk by discovering and planning ahead for the emergence of unlikely, but potentially high impact events. There are a range of possible methodological approaches, such as developing alternative future scenarios.

Environmental Shock Waves

Environmental Shock Waves

Cat Event Group Force Environmental Shock Waves

A Natural

Disasters &

Catastrophe

Natural

Forces

Natural disasters occur when extreme magnitude events of stochastic

natural processes cause severe damage to human society. "Catastrophe" is

used about an extreme disaster, although originally both referred only to

extreme events (disaster is from the Latin, catastrophe from Ancient Greek).

Human Activity Cycles - Business, Social, Political, Economic, Historic and

Pre-historic (Archaeology) Waves - may be compatible with, and map onto -

one or more Natural Cycles. Current trends in Human Population Growth

are unsustainable – we are already beginning to run out of Food, Energy

and Water (FEW) – which will first limit, then reverse human population

growth. Ecological stability and sustainability will be preserved – but only at

the expense of the continued, unchecked growth of human populations.

B Global

Massive

Change

Events

Human

Activity

Anthropogenic Impact (Human Activity) on the natural Environment - Global

Massive Change Events. In their starkest warning yet, following nearly

seven years of new research on the climate, the Intergovernmental Panel on

Climate Change (IPCC) said it was "unequivocal" and that even if the world

begins to moderate greenhouse gas emissions, warming is likely to cross

the critical threshold of 2C by the end of this century. That would have

serious consequences, including sea level rises, heat-waves and changes to

rainfall - meaning already dry regions get less and wet areas receive more.

http://www.theguardian.com/environment/ipcc


Environment Scanning, Tracking and Monitoring – Extinction Level Scenarios

Event Type Force Random Event Weak

Signal

Strong

Signal


1 Hyperspace

Event

Quantum

Dynamics

Membranes

collide in

Hyperspace

(none – event

unfolds at the

speed of light)

(none –

speed of

light event)

(none – event

unfolds at the

speed of light)

The end of

the Universe

2 Singularity

Event

Quantum

Dynamics

Black Hole

appears in the

Solar System

(none – event

unfolds at the

speed of light)

(none –

speed of

light event)

(none – event

unfolds at the

speed of light)

The end of

the Solar

System

3 Alien

Contact

Event

Biological

Disease

Contact with the

bio-cloud

of an Alien host

People start

collapsing in

the street

Global

Pandemic

declared

Hospitals and

Mortuaries

inundated by

disease

victims

Disease –

90-95 % of the

total Human

Population lost

4 Alien

Contact

Event

Biological

Predation

Contact with an

Alien force

People are

being predated

in the street

Global

Conflict

event

declared

Hospitals and

Mortuaries

inundated by

attack victims

Attack –

90-95 % of the

total Human

Population lost

5 Global

Warfare

Human

Conflict /

WMD

Exposure to

Weapons of Mass

Destruction

People start

collapsing in

the street

Global

Conflict

declared

Hospitals and

Mortuaries

inundated by

attack victims

Attack –

90-95 % of the

total Human

Population lost


Event

Type

Force Random

Event

Weak

Signal

Strong

Signal


6 Coronal

Mass

Ejection

Event

Solar

Nuclear

Fusion

Coronal Mass

Ejection event

from the Sun

Coronal flare

detected by

Astronomers

Sky turns

violet with

blue/green

Aurora

Ozone layer

destroyed and

solar radiation

floods Earth

Radiation –

Biohazard -

lethal levels of

solar radiation

7 Electro-

magnetic

Event

Earths

Magnetic

Force

Weakening

and Reversal

of the Earths

magnetic field

Compasses no

longer point to

magnetic North

Sky turns

violet with

blue/green

Aurora

Magnetic field

destroyed and

solar radiation

floods Earth

Radiation –

Biohazard -

lethal levels of

solar radiation

8 Bio-tech

Disaster

Event

Nano-

Robotics

Nano-robots

engineered to

de-construct

escape from

the laboratory

Nano-robots

begin to de-

construct the

Biosphere and

Eco-systems

Global

Famine

declared

Hospitals and

Mortuaries

inundated by

famine victims

Eco-system

collapses –

90-95 % of the

total Human

Population lost

9 Bio-tech

Disaster

Event

Smart

Robotics

Smart Robots

engineered for

warfare escape

from laboratory

People start

being predated

in the street

Global

Conflict

declared

Hospitals and

Mortuaries

inundated by

attack victims

Attack –

90-95 % of the

total Human

Population lost

10 Bio-tech

Disaster

Event

Viruses

and Germs

Bio-engineered

pathogens

escape from

the laboratory

People start

collapsing in

the street

Global

Pandemic

declared

Hospitals and

Mortuaries

inundated by

disease victims

Disease –

90-95 % of

Human

Population lost


Event

Type

Force Random

Event

Weak

Signal

Strong

Signal


11 Global

Massive

Change

Event

Human

Impact on

Eco-

system

Human

Population

exceeds

Malthusian

limits

The toxic by-

products of

Human Activity

destroy the

Environment

Global

Ecology crisis

and Famine

declared

Hospitals and

Mortuaries

inundated by

poison victims

Eco-system

collapses due

to Poisoning –

90-95 % of the

total Human

Population lost

12 Global

Massive

Change

Event

Food,

Energy

Water

(FEW)

Crisis

Human

Population

exceeds

Malthusian

limits

People are no

longer able to

find enough

Food

Global Food,

Energy Water

(FEW) crisis

declared

Hospitals and

Mortuaries

inundated by

famine victims

Food runs out

– 90-95 % of

the total Human

Population lost

13 Global

Massive

Change

Event

Food,

Energy

Water

(FEW)

Crisis

Human

Population

exceeds

Malthusian

limits

People are no

longer able to

find enough

Energy - as

cities fail and

are abandoned

Global Food,

Energy Water

(FEW) crisis

declared –

society

collapses

Hospitals and

Mortuaries

inundated by

famine, poison

and climate

change victims

Energy runs

out – 90-95 %

of the Human

Population lost

14 Global

Massive

Change

Event

Food,

Energy

Water

(FEW)

Crisis

Human

Population

exceeds

Malthusian

limits

People are no

longer able to

find enough

Water

Global Food,

Energy Water

(FEW) crisis

declared

Hospitals and

Mortuaries

inundated by

drought victims

Water runs out

– 90-95 % of

the total Human

Population lost

Environment Scanning, Tracking and Monitoring – Global Level Scenarios Event

Type

Force Random

Event

Weak

Signal

Strong

Signal


15 Impact

Event

Gravity An Asteroid is

nudged out of

the Oort Cloud

A new Comet

is detected by

Astronomers

Earth-impact

trajectory

calculated

Shock wave,

thermal and

debris waves

Comet Impact

event destroys

Earths biosphere

16 Radiation

Event

Gamma

Rays

Supernova -

death of a star

within our local

Star Cluster.

A Supernova

is detected by

Astronomers

Sky turns

violet with

blue/green

Aurora

Ozone layer

destroyed and

solar radiation

floods Earth

Radiation –

Biohazard event

- lethal levels of

solar radiation

17 Geo-

thermal

Event

Thermal

Energy

Vulcanicity –

The magma

chamber fills

up with lava

Ground level

elevation is

detected by

Geophysicists

Earthquakes

recorded as

the magma

chamber fills

Shock wave,

thermal and

debris waves

Volcanic

eruption –

Environment

destroyed

18 Tsunami

Event

Wave

Energy

Earthquake

occurs at mid-

oceanic ridge

Oceanic ridge

earthquake is

detected by

Geophysicists

Tsunami -

retreats from

Coast then

water rushes

inland

Coastal cities

and farmland

inundated by

Tsunami surge

to 90km inland

Flooding -

Coast, Deltas,

Estuaries & River

Valleys

submerged

Natural Cycles and Human Activity

Event

Type

Force Random

Event

Weak

Signal

Strong

Signal


19 Climate

Change

Solar

Forcing

Milankovich

Orbital Cycles

– Insolation

Solar Cycles

Gradual rise or

fall in average

temperature /

global climate

Environment

warms up and

dries / chills

out & freezes

Extinction-level

event followed

by adaptive

evolution

Ecological

destruction -

global massive

climate change

20 Climate

Change

Oceanic

Forcing

Dansgaard-

Oeschger and

Bond Cycles

(1,470 years)

Cyclic rapid

rise in oceanic

temperature /

global climate

Environment

warms up and

dries / chills

out & freezes

Ecological

event followed

by adaptive

evolution

Ecological

change – global

climate change

21 Climate

Change

Atmospheric

Forcing

Heinrich Event

– Atmospheric

Climate Cycles

Sudden rise in

atmospheric

temperature /

global climate

Environment

warms up and

dries / chills

out & freezes

Ecological

event followed

by adaptive

evolution

Ecological

change – global

climate change

22 Climate

Change

Impact of

Human

Activity

Global Climate

Change – Wet

average rainfall

increases in wet

areas

Combined

effect of Storm

Torrents and

Tidal Surges

are forecast

High tide with

severe storm

cause

Sea / River

levels to rise

Coastal cities

and farmland

inundated by

Storm and

Tidal Surges

Flooding -

Coast, Deltas,

Estuaries and

River Valleys

submerged up

to 90km inland

23 Climate

Change

Impact of

Human

Activity

Global Climate

Change – Dry

average rainfall

decreases in

dry areas

Water

shortage –

wells dry out

and crops fail.

Water

emergency –

rainfall fails or

stops in dry

areas

Water crisis –

rivers no

longer flow into

the sea

Drought –

famine, disease

(typhoid,

cholera and

dysentery)

Weak Signals

Weak Signals

Weak Signals are subtle indicators of novel and emerging ideas, patterns and trends which may give us a glimpse over the current horizon and allow us to peer through the mists of time into the future..... Weak Signals indicate possible future transformations and changes which are happening right now, on or even just beyond the visible horizon, predicating changes in how we do business, what business we do, and the future environment in which we will all live and work. Weak Signals – are messages from the future, subliminal temporal indicators of change (Random Events) coming to meet us from the distant horizon – perhaps indicators of novel and emerging desires, thoughts, ideas, influences, patterns and trends – which may arrive to interact with both current and historic waves, patterns and trends to alter, enhance, impact or effect future outcomes and events, or simply some future change taking place in the current environment in which we all share our life experiences.....

Weak Signals

• Weak Signal is a descriptor for an unusual and unexpected message from the future –

faint and subliminal – predicating a forthcoming Random Event. Weak Signal is sign

indicating either a possible future outcome or random event which has not been forecast

or anticipated (either because it seemed unlikely - or because no-one had even thought

about it) - but which may indicate some future extreme and far-reaching impact or effect.

1. SURPRISE – Weak Signals are a sudden and unexpected surprise to the observer.

2. SIGNIFICANCE - Weak Signals have significance as a message of a future random event,

predicating renewal or transformation – or signaling a new beginning or fresh chapter.

3. SPEED - Weak Signals appear out of nowhere – then either disperse or become stronger.

4. DUALITY OF NATURE - Weak Signals may indicate a possible future serious challenge or

threat – or reveal to the observer a future novel and unexpected window of opportunity.

5. PARADOX - Weak Signals at their first appearance could or should have been picked up

and recognised – if the Weak Signal is detected against the overwhelming foreground and

background noise - then identified, analysed and correctly accounted for.

Weak Signals

• Weak Signals – are messages, subliminal temporal indicators of ideas, patterns or trends

coming to meet us from the future – or perhaps indicators of novel and emerging, ideas,

influences and messages which may interact with both current and pre-existing patterns

and trends to impact or affect some change taking place in our current environment – even

an early warning or sign of impending random events, disasters or catastrophes which, at

some point, time or place in the Future, may predicate, influence or impact on future

events, objects or processes – to effect subtle, minor or major changes in how we live,

work and play – or even threaten the very existence of the world as we know it today.....

• A Weak Signal is an early warning or sign of change, which typically becomes stronger by

combining with other signals. The significance of a weak future signal is determined by the

nature and content of the message it contains – predicating positive or negative change –

and the scope and objectives of its recipient. Finding Weak Signals typically requires

systematic searching through “Big Data” - internet content, news feeds, data streams,

academic papers and scientific research data sets. A weak future signal requires: i)

support, ii) critical mass, iii) growth of its influence space, and dedicated actors, i.e. ‘the

champions’, in order to become a strong future signal - else Weak Signals evaporate or

disappear into the ether. A Weak Future Signal is usually first recognised by research

pioneers, think tanks or special interest groups (amateur astronomers and comets) – but

very often missed or dismissed by acknowledged “main-stream” subject matter experts.

Weak Signals

• Weak Signals – refer to Weak Future Signals in Horizon and Environment Scanning for any

unforeseen, sudden and extreme Global-level transformation or change Future Events in either

the military, political, social, economic or environmental landscape – some having an inordinately

low probability of occurrence - coupled with an extraordinarily high impact when they do occur.

• Weak Signal Types in Horizon Scanning


– Supply / Demand Shock Waves

– Political, Economic and Social Waves

– Religion, Culture and Human Identity Waves

– Art, Architecture, Design and Fashion Waves

– Global Conflict – War, Terrorism, and Insecurity Waves

• Weak Signal Types in Environment Scanning


– Impact of Human Activity on the Environment - Global Massive Change Events

Weak Signals

1. Weak Signals are initially vague in their nature and difficult to interpret at the beginning of a new

Random Event, Weak Signal, Strong Signal, Wild Card and Black Swan Wave Series, so that

their future course and outcomes often remains unclear (ANSOFF, 1990) ;

2. The nature of the early information which can be assimilated from Random Events - Weak

Signals, Strong Signals, Wild Cards and Black Swan Events - arrive in an integrated Wave

Series (ANSOFF, 1975) and has little internal structure or reference, so cannot be described or

defined in advance of receiving those very first Weak Signals (MARCH and FELDMAN, 1981),

3. The Stochastic hybrid and cross-functional and Probabilistic nature of Weak Signals limits the

impact, relevance and application of Deterministic prescriptive methods and approaches, and

precludes rigid, inflexible algorithm-based expert systems approaches (GOSHAL and KIM, 1986).

4. In strategic decision making, the uniqueness in the form and function of Weak Signals, Strong

Signals, Wild Cards and Black Swan Events - implies the use of flexible approaches and

solutions based on Probabilistic Methods – including cognitive filtering, bounded rationality,

“fuzzy” logic, approximate reasoning, neural networks and adaptive systems (SIMON, 1983);

5. The random and ethereal nature of the Horizon and Environment Scanning, Tracking and

Monitoring process involves dependence - strange actors, clustering, numerous elements and

complex interactions - and requires very large scale (VLS) computing and “BIG DATA” Analytics

techniques to reliably and accurately discover, identify, classify and interpret Weak Signals.

Weak Signals

6. Neural Networks and Complex / Adaptive / Learning System Models combined with “BIG DATA”

methods are therefore likely to be the most successful and appropriate technology approaches for

executing both Horizon and Environment Scanning, Tracking and Monitoring studies.

7. A major component of the process of Horizon and Environment Scanning, Tracking and

Monitoring is achieved either by horizon or environmental scanners who capture weak signals

hidden within massive amounts of external raw data, and data scientists using “BIG DATA” content

techniques for data analysis - “washing and mashing” and “racking and stacking”

8. A Weak Future Signal is an early warning of change, which typically becomes stronger by combining

with other signals. The significance of a weak future signal is determined by the objectives of its

recipient, and finding it typically requires systematic searching. A weak future signal requires: i)

support, ii) critical mass, iii) growth of its influence space, and dedicated actors, i.e. ‘the champions’,

in order to become a strong future signal, or to prevent itself from becoming a strong negative signal.

A Weak Future Signal is often recognised by pioneers or special groups - not by acknowledged

subject matter experts

9. The Weak Future Signal Event Types – refer to subliminal indications of future unforeseen,

sudden and extreme Global-level transformation or change. Weak Signal Event Types in either the

military, political, social, economic or environmental landscape - having an inordinately low probability

of occurrence - coupled with an extraordinarily high impact when they do occur.

Weak Signals Weak Signal Property Different views and viewpoints

1 Nature Weak Signals are subtle indicators of ideas, patterns or

trends that give us a glimpse into the future – predicating

possible future transformations and changes which are

happening on or even just over the visible horizon, changes

in how we do business, what business we do, and the future

environment in which we will all live and work.

2 Quality

Weak Signals may be novel and surprising from the signal

analyst's vantage point - although many other signal

analyst's may have already, failed to recognise,

misinterpreted or dismissed the same Weak Signals

3 Purpose Weak Signals are used for Horizon Scanning, Tracking

and Monitoring and for Future Analysis and Management

4 Source Weak Signals, Strong Signals, Wild Cards and Black

Swan Events – are a sequence of waves linked and

integrated in ascending order of magnitude, which have a

common source or origin - either a single Random Event

instance or arising from a linked series of chaotic and

disruptive Random Events – generating Weak Signals from

a Random Event Cluster or Random Event Storm.


5 Wave-form Analytics and “Big Data” Global Internet Content

Wave-form Analytics may be used with “Big Data” to analyse how Random Events propagate through the space-time continuum in a related and integrated series of waves with an ascending order of magnitude and impact – the first wave to arrive is the fastest travelling - Weak Signals - something like a faint echo of a Random Event which may be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which may indicate the unfolding a further increase in magnitude and intensity which finally arrives catastrophically - something like a tsunami (Black Swan Event).

6 Identification Weak Signals are sometimes difficult to track down, receive, tune in, identify, amplify and analyse amid the overwhelming volume of “white noise” from stronger signals and other foreground and background noise sources

7 Principle of Dual Nature (possibility of either an Opportunity or Threat)

Weak Signals may indicate the possibility of either a potential future threat or opportunity to yourself or your organization - or foretell the pending arrival of a future advantage or reversal – a “Wild card” or Black Swan” event


8 Perception Weak Signals are often missed, dismissed or scoffed at by

other Subject Matter Experts

9 Opportunity Weak Signals contain novel and emerging ideas, influences

and messages - therefore they represent an early window of

potential opportunity.

10 Impact Weak Signals arrive, become established, develop, grow

and mature - then peak, plateau decline and collapse – or

interact with current and pre-existing extrapolations,

patterns or trends to transform or change the landscape

11 Receipt / Observation Every Weak Future Signal requires –

1. a Receiver / Observer / Analyst (which could be

automated by deploying “Big Data” Analytics)

2. Subject mater experts, special interest groups etc. and

Empowered Stakeholders to achieve critical

momentum

3. growth of its support, championship and influence space

4. dedicated actors, e.g. “supporters and champions”

Weak Signals

Weak Signal Property Different views and viewpoints

12 Duration Weak signals only last for a brief period: – Transient Signal

1. Weak signals are seen as a sign that lasts for a moment,

but indicate a phenomenon (Random Event) behind it that

lasts longer – there may be a following Strong Signal

2. Weak signals are phenomena that last for a short period of

time (succeeded by strong signals and wild cards?)

Weak signal lasts longer:– it now becomes a Strong Signal

3. A weak signal is a cause for a change in the future

4. A weak signal is itself a change phenomenon

13 Transition phenomenon 1. A weak signal is created as a result of a spontaneous

Random Event phenomenon or Random Event Cluster

2. A weak signal is a sign of a future disruptive changes or

Individual / Local / Regional / Global Transformations

3. A weak signal may be an early indicator - and member of -

an integrated Wave Series

4. The transition phenomenon of a weak signal is that in the

future it will either get stronger (becomes a Strong Signal)

or weaker (attenuate and disappear from view)

Weak Signals


14 Objectivity v. Subjectivity 1. Weak signals exist independently of their receiver.

2. “Weak signals float in the phenomena space and

wait for someone to find them” – automation via

“Big Data” Analytics can address this issue.....

3. A weak signal does not exist without reception /

interpretation by a receiver / observer (which may

mitigated by automated via “Big Data” Analytics)

15 Interpretation The interpretation of a same signal can be different

from the viewpoint of different receivers of the signal.

Human Interpretation adds subjectivity to the signal –

even though it is thought to be objective – “Big Data”

Analytics may be used for the Validation process

16 Signal Strength over Time 1. The weak signal (as an indicator) is strengthening

2. A phenomenon, interpreted as weak signal, is

strengthening – it now becomes a Strong Signal

3. A phenomenon whose status is in question, is

strengthening – it now becomes a Strong Signal

Weak Signals


17 Roles and Responsibilities –

Receivers /Observers /

Analysts of the weak signal

(who receives, identifies,

observes and classifies)

1. Difficulties in defining the concept of Weak

Signals to Empowered Stakeholders – subject

mater experts, special interest groups, etc. –

explaining how they arrive from a single instance

or linked series of Random Events – or Event

Cluster / Storm

2. Differences in opinion on signal content between

signal Receiver, Observer and Analysts :-

resolved by special interest groups, subject mater

experts

18 Roles and Responsibilities –

Analysts / Interpreters /

Stakeholders in the signal

(who analyses and draws

useful valid conclusions)

1. Who is drawing the conclusions on the cause-

effect relationship? – the Receiver and the

Observer

2. Who is defining the credibility and significance of

weak signal? – the Observer and the Analyst

3. Who is the one that can affect important decisions

concerning the future? – Empowered

Stakeholders

Strong Signals

Strong Signals – represent the first clear and visible presence of a Random Event – the secondary arrival of stronger but slower-travelling waves containing more information of possible, probable and alternative future events – random events, future catastrophes, or indications o novel and emerging, ideas, influences and messages

Strong Signals

Strong Signals

• Strong Signal is a descriptor for an unusual and unexpected - but very real and apparent

- signal indicating a possible outcome or random event which has not been forecast or

anticipated (either because it seemed unlikely - or because no-one had even thought

about it) - but which may have some future extreme and far-reaching impact or effect.

1. SURPRISE – Strong Signals are a complete and unexpected surprise to the observer.

2. SIGNIFICANCE - Strong Signals have a significance as an indicator of change - or as an

signal for renewal or transformation – or signify a new beginning or fresh chapter.

3. SPEED - Strong Signals appear out of nowhere – then either disperse or magnify.

4. DUALITY OF NATURE - Strong Signals may indicate a possible serious challenge or

threat – or reveal to the observer a future novel and unexpected window of opportunity.

5. PARADOX - Strong Signals are rationalised by hindsight, as at their first appearance they

could or should have been foreseen had the relevant Weak Signals been available and

detected in the background noise, identified correctly, analysed and accounted for.

Strong Signals

• Strong Signals – represent the first clear and visible presence of a Random Event – the

secondary arrival of stronger but slower-travelling waves containing more information of

possible, probable and alternative future events – random events, future catastrophes, or

indications o novel and emerging, ideas, influences and messages which may interact with

both current and pre-existing patterns and trends to impact or affect some change taking

place in our environment - at some point, time or place in the future – for example, what

future climatic and ecological environment will live , work and play in what political, social

and economic environment will live , work and play in, how we live, work and play, what

business we do, how we do business and who we do Business with......

1. Strong Signals may demonstrate a substantial lag time before they follow their

preceding indicators, prior Weak Signals

2. Strong Signals may contain confirmation about future events – random events,

catastrophes, or indications o novel and emerging, ideas, influences and messages.

They therefore present a second potential window of opportunity if the first Weak Signals

in the series were undetected, overlooked or dismissed

3. Strong Signals arrive, become established, develop, grow and mature - then peak,

plateau decline and collapse or interact with current and pre-existing extrapolations,

patterns or trends which act to transform or change the current outlook or landscape.

Strong Signals

Property Different Views and Viewpoints

1 Nature Strong Signals follow Weak Signals – to give a more clear and apparent

indication of ideas, patterns or trends that provide us with a stronger and

more lasting glimpse into the future – predicating probable future

transformations and changes which are happening on or even just over

the visible horizon, changes in how we do business, what business we

do, and the future environment in which we will all live and work.

2 Purpose Strong Signals are used in Horizon Scanning, Tracking and Monitoring -

for Strategy Analysis and Strategy Management, Future Analysis and

Future Management

3 Source Weak Signals, Strong Signals (which are second in the sequence), Wild

Cards and Black Swan Events – are a linked sequence of integrated

waves in a timeline and ascending order of magnitude, which have a

common source or origin - either a single Random Event instance – or

arising from a linked series of chaotic and disruptive Random Events –

creating a Random Event Cluster or Random Event Storm.

Strong Signals


4 Identification Strong Signals are easier to recognise than Weak Signals,

receive, tune in, identify, amplify and analyse amid the

overwhelming volume of “white noise” from stronger signals and

other foreground and background noise sources

5 Perception Whereas Weak Signals are often missed, dismissed or scoffed at by

other Subject Matter Experts - Strong Signals are more widely

recognised and accepted

6 Opportunity Strong Signals bring confirmation of novel and emerging ideas,

influences and messages - therefore they represent an second

window of potential opportunity.

7 Quality Whereas Weak Signals may be novel and surprising from the signal

analyst's vantage point - Strong Signals are not as easily

dismissed as Weak Signals. Many other signal analyst's may now

confirm and support the content of such Strong Signals

9 Timing Strong Signals may demonstrate a substantial lag time before they

follow their preceding indicators, prior Weak Signals

Wild Cards

Wild Cards

• Wild Card is a descriptor for an unusual and unexpected outcome or event which has not

been forecast or anticipated (either because it seemed unlikely - or because no-one had

even thought about it) - but which has extreme impact and far-reaching and effect. This

term is also often used as a descriptive adjective - as in the expression wild-card event.

1. SURPRISE – Wild Card Events are a complete and totally unexpected surprise to the

observer - the scale of the event falling well outside the realm of previous experience.

2. SIGNIFICANCE - Wild Card Events have a significant impact as a catalyst of change - or

as an agent of renewal or transformation – or even signify a new beginning or fresh chapter.

3. SPEED - Wild Card Events appear out of nowhere – then unfold with speed and rapidity.

4. DUALITY OF NATURE - Wild Card Events may represent either a potentially serious

challenge or threat – or present the observer with a novel and unexpected opportunity.

5. PARADOX - Wild Card Events are rationalised by hindsight, as at their first appearance

they could or should have been foreseen had the relevant Weak Signals been available

and detected in the background noise, identified correctly, analysed and accounted for.

Wild Card Events

Definition of “Wild card” Event

• A “Wild card” Event is a surprise - an event or occurrence that deviates outside of what

would normally be expected of any given situation or set of circumstances, and which therefore

would be difficult to anticipate or predict. This term was coined by Stephen Aguilar-Milan in the

1960’s and popularised by Ansoff in the 1970’s. Wild card Events – are any unforeseen,

sudden and unexpected change events or transformation scenarios which occur within the

military, political, social, economic or environmental landscape - having a low probability of

occurrence, coupled with an high impact when they do occur (Stephen Aguilar-Milan): -

• Horizon Scanning – Wild card Event Types


– Religion, Culture and Human Identity Shock Waves

– Art, Architecture, Design and Fashion Shock Waves

– Epidemics – outbreaks of contagious diseases

• Environment Scanning - Wild card Event Types

– Natural disasters – flooding, drought, earthquakes, volcanic activity

– Human Activity Impact on the Environment – Climate Change Events

Wild Cards

1. Wild Card Events have been defined, for example, by Rockfellow (1994), who speculated that a

wild card is "an event having a low probability of occurrence, but an inordinately high impact if it

does occur."

2. Wild Cards represents the appearance, materialisation or manifestation of a RANDOM EVENT

- either a potential threat or perceived opportunity to yourself and / or your organization - and

may contain within them, the seeds of a possible major future global advantage or reversal – a

forthcoming “Black Swan” event

3. Listing examples of specific 21st Century Wild Cards in 1994, Rockfellow defined three wild

cards principles: -

1. 21st Century Wild Cards manifest themselves at the beginning of the Business Cycle– or

act to bring to an end the current the Business Cycle (i.e. within 11 years of a prior cycle)

2. 21st Century Wild Cards have a probability of re-occurring again at a rate of less than 1 in

10 years – but reappear with increased speed, frequency, severity and impact over time

3. 21st Century Wild Cards events will likely have high impact on international businesses

4. Wild Cards are "low-probability, hi-impact events that happen quickly" and "they have huge

sweeping consequences." Wild cards, according to Petersen, generally surprise everyone,

because they materialize so quickly that the underlying social systems cannot effectively

anticipate or respond to them (Petersen 1999).

5. According to Cornish (2003: 19), a Wild Card is an unexpected, surprising or even startling

event that has sudden impact, important outcomes and far-reaching consequences. He

continues: "Wild cards have the power to radically change many processes and events and to

entirely overturn people's thinking, planning and actions."

Wild Cards


1 Nature Wild cards follow in the sequence of Random Events, Weak Signals and

Strong Signals – to give the first exposure to novel and emerging events

and event clusters, ideas, patterns or trends that arrive from the future –

beginning transformations and changes which now have a very real

presence and effect – impacting on how we do business, what business

we do, and the future environment in which we will all live, work and play.

2 Purpose Wild cards are used in Horizon Scanning, Tracking and Monitoring –

providing information for the purposes of Future Analysis and Future

Management, Strategy Analysis and Strategy Management,

3 Source Random Events, Weak Signals, Strong Signals and Wild cards and

Black Swan Events – are a linked sequence of integrated waves in a

timeline and ascending order of magnitude and impact, which have a

common source or origin - either a single Random Event instance – or

arising from a linked and integrated series of chaotic and disruptive

related Random Events – as part of a Random Event Cluster or

Random Event Storm.

Wild Cards


4 Identification Wild cards are much easier to recognise than Weak Signals and

Strong Signals, above the background of “white noise” from and

other signals from foreground and background noise sources

5 Perception Whereas Weak Signals and even Strong Signals are often missed,

dismissed or scoffed at by other Subject Matter Experts – Wild

cards events are almost universally recognised and accepted

6 Opportunity Wild cards bring realisation of startling new events, novel and

emerging ideas, influences and messages - therefore they represent

an third and final window of potential opportunity.

7 Quality Weak Signals and even Strong Signals may be novel and surprising

from the signal analyst's vantage point - Wild cards, however,

cannot be so easily dismissed. Many other signal analyst's may

now join in to confirm and support the content of such Wild cards.

9 Timing Wild cards may demonstrate a substantial lag time before they

follow their preceding indicators, those prior Weak Signals and their

followers, the Strong Signals

Wild Cards

• Climate and Environmental Agents & Catalysts of Change impact on Human Futures •

• For most of human existence our ancestors led precarious lives as scavengers, hunters,

and gatherers, and there were fewer than 10 million human beings on Earth at any one

time. Today, many of our cities have more than 10 million inhabitants each - as global

human populations continue to grow unchecked. The total global human population

stands today at 7 billion - with as many as three billion more people on the planet by 2050.

• Human Activity Cycles - Business, Social, Political, Economic, Historic and Pre-historic

(Archaeology) Waves - may be compatible with, and map onto - one or more Natural

Cycles – Orbital, Climate and so on. Current trends in Human Population Growth are

unsustainable – we are already beginning to run out of Food, Energy and Water (FEW) –

which will first limit, then reverse human population growth – falling below 1bn by 2060 ?

• Over the long term, ecological stability and sustainability will be preserved – but at the

expense of the continued, unchecked growth of human populations. Global population will

rise to 10 billion by 2040 – followed by a massive population collapse to under 1 billion -

recovering to 1 billion by the end of the 21st century. There are eight major threats to

Human Society, which are “Chill”, “Grill”, “Ill”, “Kill”, “Nil”, “Spill”, “Thrill” and “Till”.

Environmental Wild Card Event Types

Event Type Force Environmental Black Swan Event

1 Natural

Disasters &

Catastrophe

Natural

Forces

Natural disasters occur when extreme magnitude events of stochastic

natural processes cause severe damage to human society. "Catastrophe" is

used about an extreme disaster, although originally both referred only to

extreme events (disaster is from the Latin, catastrophe from Ancient Greek).

Human Activity Cycles - Business, Social, Political, Economic, Historic and

Pre-historic (Archaeology) Waves - may be compatible with, and map onto -

one or more Natural Cycles. Current trends in Human Population Growth

are unsustainable – we are already beginning to run out of Food, Energy

and Water (FEW) – which will first limit, then reverse human population

growth. Ecological stability and sustainability will be preserved – but only at

the expense of the continued, unchecked growth of human populations.

2 Global

Massive

Change

Events

Human

Activity

Anthropogenic Impact (Human Activity) on the natural Environment - Global

Massive Change Events. In their starkest warning yet, following nearly

seven years of new research on the climate, the Intergovernmental Panel on

Climate Change (IPCC) said it was "unequivocal" and that even if the world

begins to moderate greenhouse gas emissions, warming is likely to cross

the critical threshold of 2C by the end of this century. That would have

serious consequences, including sea level rises, heat-waves and changes to

rainfall meaning dry regions get less and already wet areas receive more.



Wild Card Event Types

Type Force Technology Shock Wave Event

3 Technology

Shock Waves

Innovation Technology Shock Waves – Disruptive Technologies: -

Stone – Tools for Hunting, Crafting Artefacts and making Fire

Fire – for Warmth, Cooking and managing the Environment

Agriculture – Neolithic Age Human Settlements

Bronze – Bronze Age Cities and Urbanisation

Ship Building – Communication, Culture and Trade

Iron – Iron Age Empires, Armies and Warfare

Gun-powder – Global Imperialism and Colonisation

Coal – Mining, Manufacturing and Mercantilism

Engineering – Bridges, Boats and Buildings

Steam Power – Industrialisation and Transport

Chemistry – Dyestuff, Drugs, Explosives and Agrochemicals

Internal Combustion – Fossil Fuel dependency

Physics – Satellites and Space Technology

Nuclear Fission – Globalisation and Urbanisation

Digital Communications – The Information Age

Smart Cities of the Future – The Solar Age - Renewable

Energy and Sustainable Societies

Nuclear Fusion – The Hydrogen Age - energy independence -

Inter-planetary travel and discovery, Human Settlements

Space-craft Building – The Exploration Age - Inter-stellar

travel & discovery, Galactic Colonisation, Cities & Urbanisation

Wild Card Event Types Type Force Wild card Event

4 Impact

Event

Gravity Asteroid or comet impact – the odds of an asteroid or comet impact on the

Earth depend on the size of the Object. An Object approximately 15 feet in

diameter hits the Earth once every several months; 35 feet every 10 years; 60

feet every 100 years; 200 feet, or size of the Tunguska impact, every 200

years; 350 feet every several thousand years; 1,000 feet every 50,000 years;

six tenths of a mile every 500,000 years; and 5 to 6 miles across every 100

million years.

5 Thermal

Process

Geo-

Thermal

Energy

“Spill Moments” - Local and Regional Natural Disasters e.g. Andesitic volcanic

eruption at tectonic plate margins – for example, the Vesuvius eruption and ash

cloud destroying the Roman cities of Herculaneum and Pompeii, and Volcanic

eruption / collapse causing Landslides and Tsunamis - Stromboli eruption /

collapse fatally weakening the Minoan Civilisation on Crete, Krakatau eruption

in the 19th Century causing Indonesian Tsunamis, ocean-floor sediment slips

causing in recent years the recent Pacific / Indian Oceanic, and Japanese

Tsunamis – resulting in coastal flooding, inundation and widespread destruction

“Thrill Moments” - Continental or Global Natural Disasters – Extinction-level

Events (ELE) such as the Deccan and Siberian Traps Basaltic Flood Volcanic

Events, Asteroid and Meteorite Impacts, Gamma-ray Bursts from nearby

collapsing stars dying and going Supernova – which have all variously

contributed towards the late Pre-Cambrian “Frozen Globe”, Permian-Triassic

and Cretaceous-Tertiary boundary global mass extinction events.

Wild card Events Type Force Extinction-level Black Swan Event

6 Climate

Change

Human

Activity

Melting of the polar ice-caps, rising sea levels – combined with increased

severity and frequency of extreme weather events – El Nino and La Nina

have already begun to threaten these low-lying coastal cities (New Orleans,

Brisbane). By 2040, a combination of rising sea levels, storm surges of

increased intensity and duration and flash floods – will flood much more

often. Coast, Deltas, Estuaries & River Valleys will flood up to 90km inland

up to 90 km into the interior from the present coast – frequently drowning

many of the major cities along with much of our most productive agricultural

land – and washing away homes and soil in the process. Human Population

Drift to Cities and Urbanisation also drives the destruction of prime arable

land – as it is gobbled up by developers to build even more cities.

Liquid water melted by warm air at the surface of a glacier, runs down sink-

holes to the glacier base where it lubricates the rock / glacier interface –

causing glacier flow surges up to 20 times the normal flow-rate. Increased

glacial flow-rate is usually further aided and by the loss of sea pack ice –

which acts to moderate Glacier flow during cold periods - due to oceanic

temperature rise (oceanic climate forcing). This scenario does satisfy not

the timing requirements of climate change events which occur at the

culmination of a next Bond Cycles – believed to be oceanic climate forcing

phenomena. It does fit in well with the rapid rise in temperature that occurs

at the beginning of the next Bond Cycle – which takes only a few decades

after the culmination of the previous Bond Cycle.

Wild card Events

Type Force Black Swan Event

7 Climate

Change

Event

Solar

Forcing

Climate Change – Dansgaard-Oetcher and Bond Cycles - oceanic climate forcing

cycles consisting of episodes of rapid warming followed by slow cooling have been

traced and plotted over the last 26 cycles – 40,000 years - with metronomic precision

of exact 1,490-years periodicity. Solar orbital cycle variations with periodicities from

20,000 to 400,000-years have also been traced and plotted over many cycles – tens of

millions of years – again with metronomic regularity. These longer-scale Milankovich

Cycles are responsible for Pluvial and Inter-pluvial episodes (Ice Ages) during the

Quaternary period - due to orbital variation causing changes to solar climate forcing.

Global warming—Human Activity has been largely held responsible for the Earth

getting warmer every decade for the last two hundred years – and the rate of warming

has accelerated over the last few decades. The Earth could eventually wind up like its

greenhouse sister, Venus. “Grill” - rapidly rising temperatures such as found in Ice

Age Inter-Glacial episodes (Inter-pluvial Periods) – precipitating environmental and

ecological change under heat stress and drought – causing the disappearance of the

Neanderthal, Soloutrean and Clovis cultures with deforestation, desertification and

drying driving the migration or disappearance of the Anastasia in SW America - along

with the Sahara Desert migrating south and impacting on Sub-Saharan cultures.

.Global cooling— The Earth has dramatically cooled and plunged into Ice Ages on

many occasions throughout Geological History, Earth might eventually change to

resemble its frozen sister, Mars. “Chill” – rapid cooling, e.g. Ice Age Glaciations

(Pluvial Periods) causing the depopulation of Northern Europe in early hominid Eolithic

times and impact of the medieval “mini Ice Age” on Danish settlers in Greenland.


Type Force Wild card Event

5 Global

Massive

Change

Event

Human

Impact

on Eco-

system

FEW - Food, Energy, Water Crisis - as scarcity of Natural Resources (FEW -

Food, Energy, Water) and increased competition to obtain those scarce

resources begins to limit and then reverse population growth, global population

levels will continue expansion towards an estimated 8 or 9 billion human beings

by the middle of this century – then collapse catastrophically to below 1 billion –

slowly recovering and stabilising out again at a sustainable population of about 1

billion human beings by the end of this century.

“Till Moments” - Society’s growth-associated impacts on its own ecological and

environmental support systems, for example intensive agriculture causing

exhaustion of natural resources by the Mayan and Khmer cultures, de-

forestation and over-grazing causing catastrophic ecological damage and

resulting in climatic change – for example, the Easter Island culture, the de-

population of upland moors and highlands in Britain from the Iron Age onwards –

including the Iron Age retreat from northern and southern English uplands, the

Scottish Highland Clearances and replacement of subsistence crofting by deer

and grouse for hunting and sheep for wool on major Scottish Highland Estates

and the current sub-Saharan de-forestation and subsequent desertification by

semi-nomadic pastoralists. Like Samson, will we use our strength to bring down

the temple? Or, like Solomon, will we have the wisdom to match our technology?


Type Force Wild card Event

8 Alien

Contact

Event

Biological

Disease

“Ill Moments” - Contact with a foreign population or alien civilization and their

bio-cloud – bringing along with them their own parasite burden and contagious

diseases (viruses and bacteria) - leading to pandemics to which the exposed

human population has developed little or no immunity or treatment. Examples

include the Bubonic Plague - Black Death - arriving in Europe in ships from Asia,

Spanish Explorers sailing up the Amazon and spreading Smallpox to Amazonian

Basin Indians from the Dark Earth - Terra Prate - Culture and Columbian Sailors

returning to Europe introducing Syphilis from the New World, the Spanish Flu

Pandemic carried home by returning soldiers at the end of the Great War - which

killed more people than did all the military action during the whole of WWI).

9 Alien

Contact

Event

Biological

Predation

“Kill Moments” – Invasion, conquest and genocide by a civilisation with

superior technology, e.g. Roman conquest of Celtic Tribes in Western Europe,

William the Conquerors’ “Harrying of the North” in England, Spanish

conquistadores meet Aztecs and Amazonian Indians in Central and South

America, Cowboys v. Indians across the plains of North America…..

10 Hyper-

space

Event

Quantum

Dynamics

“Nil Moments” – Singularity or Hyperspace Events where the Earth and Solar

System are swallowed up by a rogue Black Hole – or the dimensional fabric of

the whole Universe is ripped apart when two Membranes (Universes) collide in

hyperspace and one dimension set is subsumed into the other – they merge into

a large multi-dimensional Membrane – and split up into two new Membranes?

Recent Historic Wild card Events Wild card Events Surprise Impact Type Trigger

Tay Bridge disaster (1879) – railway bridge collapsed during a

violent storm whilst a passenger train was passing across

High Medium Bridge

Design

Wind

Tacoma Narrows bridge collapse (1940) – road bridge

collapsed in a moderate wind due to “aeroelastic flutter”

High Low Bridge

Design

Wind

Flixborough Chemical Works Disaster (1974) – cyclo-hexane

chemical leak resulting in a hydrocarbon vapour cloud explosion

High Medium Health &

Safety

Equipment

Failure

Chernobyl nuclear disaster (1986) – safety systems shut down

for a technical exercise on the turbine generator – core meltdown

High High Health &

Safety

Human

Error

World Trade Centre (1990) – Wahid terrorist group activity High Medium Security Terrorism

World Trade Centre (2001) – Al Qaida terrorist group activity High High Security Terrorism

Buncefield storage depot (2005) – undetected oil fuel leak

ignited resulting in a hydrocarbon vapour cloud explosion


Safety

Equipment

Failure

Texas City oil refinery explosion (2005) – hydrocarbon cloud

accumulation from a fuel leak - resulting in a vapour explosion


Safety

Equipment

Failure

Gulf of Mexico oil rig explosion (2009) – high pressure methane

blow-back during deep water drilling - resulting in a explosion

High High Health &

Safety

Human

Error

Mumbai Taj Mahal Hotel (2012) – Taliban terrorist group activity High Medium Security Terrorism

Nairobi Shopping Mall (2013) – Al Shabab terrorist group activity High Medium Security Terrorism

Black Swan Events

Black Swan Events

Definition of “Black Swan” Event

• A “Black Swan” Event is a surprise - a random event or occurrence that deviates well beyond

the bounds of what is normally expected of any given situation or set of circumstances, and

which would be extremely difficult or impossible to anticipate, forecast or predict. This term was

popularised by Nassim Nicholas Taleb, a global investment fund manager. Black Swan Events

are any unforeseen, sudden and extreme random events – agent and catalysts of massive

change, Global-level transformation scenarios which occur within the military, political, social,

economic or environmental landscape, having an inordinately low probability of occurrence -

coupled with an extraordinarily high impact when they do occur (Nassim Taleb).

• Horizon Scanning - Black Swan Event Types

– Pandemics - global outbreaks of Disease

– Political, Economic and Social Shock Waves

– Market Supply / Demand and Price Shock Waves

– Global Conflict – War, Terrorism, and Insecurity Shock Waves

• Environment Scanning - Black Swan Event Types


– Human Activity Impact on the Environment – Global Massive Change Events

Black Swan Events

• Black Swan events are typically random and unexpected - characterized by three main

criteria: first, they are surprising, falling outside the realm of usual expectation; second,

they have a major effect (sometimes even of historical significance); and third, with the

benefit of hindsight they are often rationalized as something that could or should have

been foreseen - had all of the facts been available and examined carefully enough.

• One of the chief contexts in which the term Black Swan currently occurs is in economic

and financial, especially in reference to the global economic turmoil of recent years.

Financial analysts have also extended the Black Swan metaphor to talk about grey

swans, events which are possible or known-about, and are potentially extremely

significant, but which are considered by some to be unlikely. Among a group of recently

identified grey swans in the financial domain is the so-called fiscal cliff, a cocktail of tax

increases and spending cuts which could be disastrous for the US economy.

• As an example, the previously highly successful hedge fund Long Term Capital

Management (LTCM) was forced into bankruptcy as a result of the ripple effect caused

by the Russian government's debt default. The Russian government's default

represents a Black Swan Event - because none of LTCM's Risk managers or their

computer models could have reasonably predicted this event , nor any of the Events

subsequent unforeseen impacts, consequences and effects.

http://www.macmillandictionary.com/open-dictionary/entries/grey-swan.htm

http://www.macmillandictionary.com/open-dictionary/entries/grey-swan.htm

http://www.macmillandictionary.com/buzzword/entries/fiscal-cliff

Black Swan Events

• The phrase Black Swan is a metaphor describing an unusual and rare random event

which is totally unanticipated (perhaps because it seemed impossible or because no-one

had considered it before) - which has extreme and far-reaching consequences. This term

is also often used as a descriptive adjective - as in the expression black-swan event.

1. SHOCK - Black Swan Events are a complete and totally unexpected shock to the observer

- the scale of the event falling well outside the bounds of any prior expectations.

2. SEVERE - Black Swan Events have a severe impact, even a historical significance, as a

catalyst of massive change - or as an agent bringing severe global transformation.

3. SUDDEN - Black Swan Events appear suddenly and unfold with an extraordinary pace.

4. DUALITY OF NATURE - Black Swan Events may represent either a potentially

catastrophic threat – or challenge the observer with novel and unexpected opportunities.

5. PARADOX - Black Swan Events are rationalised by hindsight, as at their first appearance

they could or should have been foreseen had the relevant Weak Signals been available

and detected in the background noise, identified correctly, analysed and accounted for.

Fiscal Black Swan Event Types


1 Oil-Price

Shock

Market

forces

Economic cycles and the global recessions that followed have been tightly

coupled with the price of oil since the Oil Price shocks of the 1970s. In the

1980’s, spurred on by these events, economists analysed the relationship

between the price of Oil and economic output in a number of econometric

studies, demonstrating a positive correlation in the US and other industrial

countries between oil prices and industrial output. The Oil Price shocks of

1990 and 2008 had a relatively lower impact on the global economy.

2 Money

Supply

Shock

Market

forces

Contemporary Fiscal Models for the demand and supply of money are either

inconsistent with the adjustment of price levels to expected changes in the

nominal money supply - or demonstrate implausible fluctuations in interest

rates in response to unexpected changes in the nominal money supply.

A new “shock-absorber” model of money demand and supply views money

supply shocks as impacting the synchronisation of purchases and sales of

assets - to create a temporary desire to hold more or less money than would

normally be the case. The shock-absorber variables significantly improve the

modelling of estimated short-run money demand functions in every respect.

3 Sovereign

State Debt

Default

Market

Forces

Whilst Portugal, Italy, Greece, Ireland, Iceland and Spain - even the USA -

might be on the brink of defaulting on its sovereign loans, causing global

markets to plunge and economies to decelerate, there’s nothing particularly

novel about this type of financial crisis – which has occurred many times.

Historic Financial Black Swan Events

Black Swan Events Surprise Impact Trigger Event

The Wall Street Crash (1927) High High Market Forces

The Great Depression (1929-1931) High High Market Forces

Oil Price Shock (1970) High High Arab-Israeli War

Global Recession (1970-1971) High High Market Forces

Oil Price Shock (1978) High High Market Forces



USA Sub-Prime Mortgage Crisis (2008) High High Market Forces

CDO Toxic Asset Crisis (2008) High High Market Forces

Financial Services Sector Collapse (2008) High High Market Forces

Credit Crisis (2008) High High Market Forces

Sovereign Debt Crisis (2008-2014) High High Market Forces

Money Supply Shock (2008) High High Market Forces


Trigger D

USA Sub-Prime Mortgage Crisis

Trigger F

CDO Toxic Asset Crisis

K

E Trigger

K Sovereign

Debt Crisis

B Trigger

I

Money

Supply

Shock

C Trigger

H

Financial

Services

Sector

Collapse

D Trigger

G

L

A Trigger

J

Credit

Crisis

Global

Recession

Black Swan Events

Definition of a “Black Swan” Event

• A “Black Swan” Event is an event or

occurrence that deviates beyond what is

normally expected of any given situation

and that would be extremely difficult to

predict. This term was popularised by

Nassim Nicholas Taleb, a finance

professor and former Investment Fund

Manager and Wall Street trader.

• Black Swan Events – are unforeseen,

sudden and extreme or change events or

Global-level transformation in either the

military, political, social, economic or

environmental landscape. Black Swan

Events have an inordinately low

probability of occurrence - coupled with an

extraordinarily high impact when they do

occur (Nassim Taleb). “Black Swan” Event Cluster or “Storm”

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

1 Smallpox Viral

Biological

Disease

The history of smallpox holds a unique place in medical history. One of the

deadliest viral diseases known to man, it is the first disease to be treated by

vaccination - and also the only disease to have been eradicated from the

face of the earth by vaccination. Smallpox plagued human populations for

thousands of years. Researchers who examined the mummy of Egyptian

pharaoh Ramses V (died 1157 BCE) observed scarring similar to that from

smallpox on his remains. Ancient Sanskrit medical texts, dating from about

1500 BCE, describe a smallpox-like illness. Smallpox was most likely

present in Europe by about 300 CE. – although there are no unequivocal

records of smallpox in Europe before the 6th century CE. It has been

suggested that it was a major component of the Plague of Athens that

occurred in 430 BCE, during the Peloponnesian Wars, and was described

by Thucydides. A recent analysis of the description of clinical features

provided by Galen during the Antonine Plague that swept through the

Roman Empire and Italy in 165–180, indicates that the probable cause was

smallpox. In 1796, after noting Smallpox immunity amongst milkmaids –

Edward Jenner carried out his now famous experiment on eight-year-old

James Phipps, using Cow Pox as a vaccine to confer immunity to Smallpox.

Some estimates indicate that 20th century worldwide deaths from smallpox

numbered more than 300 million. The last known case of wild smallpox

occurred in Somalia in 1977 – until recent outbreaks in Pakistan and Syria.

http://en.wikipedia.org/wiki/Plague_of_Athens

http://en.wikipedia.org/wiki/Peloponnesian_War

http://en.wikipedia.org/wiki/Thucydides

http://en.wikipedia.org/wiki/Galen

http://en.wikipedia.org/wiki/Antonine_Plague



http://en.wikipedia.org/wiki/Roman_Empire

http://en.wikipedia.org/wiki/Italy



2 Bubonic

Plague

Bacterial

Biological

Disease

The Bubonic Plague – or Black Death – was one of the most devastating

pandemics in human history, killing an estimated 75 to 200 million people

and peaking in Europe in the years 1348–50 CE. The Bubonic Plague is a

bacterial disease – spread by fleas carried by Asian Black Rats - which

originated in or near China and then travelled to Italy, overland along the Silk

Road, or by sea along the Silk Route. From Italy the Black Death spread

onwards through other European countries. Research published in 2002

suggests that the Black Death began in the spring of 1346 in the Russian

steppe region, where a plague reservoir stretched from the north-western

shore of the Caspian Sea into southern Russia. Although there were

several competing theories as to the etiology of the Black Death, analysis of

DNA from victims in northern and southern Europe published in 2010 and

2011 indicates that the pathogen responsible was the Yersinia pestis

bacterium, possibly causing several forms of plague. The first recorded

epidemic ravaged the Byzantine Empire during the sixth century, and was

named the Plague of Justinian after emperor Justinian I, who was infected

but survived through extensive treatment. The epidemic is estimated to have

killed approximately 50 million people in the Roman Empire alone. During

the Late Middle Ages (1340–1400) Europe experienced the most deadly

disease outbreak in history when the Black Death, the infamous pandemic

of bubonic plague, peaked in 1347, killing one third of the human population.

http://en.wikipedia.org/wiki/Pandemic

http://en.wikipedia.org/wiki/History_of_the_world

http://en.wikipedia.org/wiki/Common_Era

http://en.wikipedia.org/wiki/Etiology_(medicine)

http://en.wikipedia.org/wiki/Yersinia_pestis



http://en.wikipedia.org/wiki/Plague_(disease)

http://en.wikipedia.org/wiki/Byzantine_Empire

http://en.wikipedia.org/wiki/Plague_of_Justinian

http://en.wikipedia.org/wiki/Justinian_I

http://en.wikipedia.org/wiki/Late_Middle_Ages



3 Malaria Parasitic

Biological

Disease

The Malaria pathogen has killed more humans than any other disease. Human

malaria most likely originated in Africa and has coevolved along with its hosts,

mosquitoes and non-human primates. The first evidence of malaria parasites

was found in mosquitoes preserved in amber from the Palaeogene period that

are approximately 30 million years old. Malaria may have been a human

pathogen for the entire history of the species. Humans may have originally

caught Plasmodium falciparum from gorillas. About 10,000 years ago, a period

which coincides with the development of agriculture (Neolithic revolution) -

malaria started having a major impact on human survival. A consequence was

natural selection for sickle-cell disease, thalassaemias, glucose-6-phosphate

dehydrogenase deficiency, ovalocytosis, elliptocytosis and loss of the Gerbich

antigen (glycophorin C) and the Duffy antigen on erythrocytes because such

blood disorders confer a selective advantage against malaria infection (balancing

selection). The first known description of malaria dates back 4000 years to 2700

B.C. China where ancient writings refer to symptoms now commonly associated

with malaria. Early malaria treatments were first developed in China from

Quinghao plant, which contains the active ingredient artemisinin, re-discovered

and still used in anti-malaria drugs today. Largely overlooked by researchers is

the role of disease and epidemics in the fall of Rome. Three major types of

inherited genetic resistance to malaria (sickle-cell disease, thalassaemias, and

glucose-6-phosphate dehydrogenase deficiency) were all present in the

Mediterranean world 2,000 years ago, at the time of the Roman Empire.

http://en.wikipedia.org/wiki/Coevolution

http://en.wikipedia.org/wiki/Mosquito

http://en.wikipedia.org/wiki/Primate

http://en.wikipedia.org/wiki/Amber

http://en.wikipedia.org/wiki/Paleogene

http://en.wikipedia.org/wiki/Pathogen

http://en.wikipedia.org/wiki/Plasmodium_falciparum

http://en.wikipedia.org/wiki/Plasmodium_falciparum

http://en.wikipedia.org/wiki/Gorilla

http://en.wikipedia.org/wiki/Neolithic_revolution

http://en.wikipedia.org/wiki/Natural_selection

http://en.wikipedia.org/wiki/Sickle-cell_disease



http://en.wikipedia.org/wiki/Thalassaemias

http://en.wikipedia.org/wiki/Glucose-6-phosphate_dehydrogenase_deficiency








http://en.wikipedia.org/wiki/Ovalocytosis

http://en.wikipedia.org/wiki/Elliptocytosis

http://en.wikipedia.org/wiki/Glycophorin_C



http://en.wikipedia.org/wiki/Duffy_antigen_system

http://en.wikipedia.org/wiki/Red_blood_cells

http://en.wikipedia.org/wiki/Blood_disorders

http://en.wikipedia.org/wiki/Balancing_selection

http://en.wikipedia.org/wiki/Balancing_selection

http://en.wikipedia.org/wiki/Genetic_resistance_to_malaria

http://en.wikipedia.org/wiki/Mediterranean




4 Syphilis Bacterial

Biological

Disease

Syphilis - the exact origin of syphilis is unknown. There are two primary

hypotheses: one proposes that syphilis was carried from the Americas to

Europe by the crew of Christopher Columbus, the other proposes that

syphilis previously existed in Europe but went unrecognized. These are

referred to as the "Columbian" and "pre-Columbian" hypotheses. In late 2011

newly published evidence suggested that the Columbian hypothesis is valid.

The appearance of syphilis in Europe at the end of the 1400s heralded

decades of death as the disease raged across the continent. The first

evidence of an outbreak of syphilis in Europe were recorded in 1494/1495

in Naples, Italy, during a French invasion. First spread by returning French

troops, the disease was known as “French disease”, and it was not until

1530 that the term "syphilis" was first applied by the Italian physician and

poet Girolamo Fracastoro. By the 1800s it had become endemic, carried by

as many as 10% of men in some areas - in late Victorian London this may

have been as high as 20%. Invariably fatal, associated with extramarital sex

and prostitution, syphilis was accompanied by enormous social stigma. The

secretive nature of syphilis helped it spread - disgrace was such that many

sufferers hid their symptoms, while others carrying the latent form of the

disease were unaware they even had it. Treponema pallidum, the syphilis

causal organism, was first identified by Fritz Schaudinn and Erich Hoffmann

in 1905. The first effective treatment (Salvarsan) was developed in 1910

by Paul Ehrlich which was followed by the introduction of penicillin in 1943.

http://en.wikipedia.org/wiki/Syphilis

http://en.wikipedia.org/wiki/Americas

http://en.wikipedia.org/wiki/Christopher_Columbus

http://www.sciencemuseum.org.uk/broughttolife/themes/publichealth/sti.aspx

http://en.wikipedia.org/wiki/Naples

http://en.wikipedia.org/wiki/Girolamo_Fracastoro



http://en.wikipedia.org/wiki/Fritz_Schaudinn

http://en.wikipedia.org/wiki/Fritz_Schaudinn

http://en.wikipedia.org/wiki/Erich_Hoffmann

http://en.wikipedia.org/wiki/Salvarsan

http://en.wikipedia.org/wiki/Paul_Ehrlich

http://en.wikipedia.org/wiki/Penicillin



5 Tuberculosis Bacterial

Biological

Disease

Tuberculosis - the evolutionary origins of the Mycobacterium tuberculosis

indicates that the most recent common ancestor was a human-specific

pathogen, which encountered an evolutionary bottleneck leading to

diversification. Analysis of mycobacterial interspersed repetitive units has

allowed dating of this evolutionary bottleneck to approximately 40,000 years

ago, which corresponds to the period subsequent to the expansion of Homo

sapiens out of Africa. This analysis of mycobacterial interspersed repetitive

units also dated the Mycobacterium bovis lineage as dispersing some 6,000

years ago. Tuberculosis existed 15,000 to 20,000 years ago, and has been

found in human remains from ancient Egypt, India, and China. Human

bones from the Neolithic show the presence of the bacteria, which may be

linked to early farming and animal domestication. Evidence of tubercular

decay has been found in the spines of Egyptian mummies, and TB was

common both in ancient Greece and Imperial Rome. Tuberculosis reached

its peak the 18th century in Western Europe with a prevalence as high as

900 deaths per 100,000 - due to malnutrition and overcrowded housing with

poor ventilation and sanitation. Although relatively little is known about its

frequency before the 19th century, the incidence of Scrofula (consumption)

“the captain of all men of death” is thought to have peaked between the end

of the 18th century and the end of the 19th century. With advent of HIV there

has been a dramatic resurgence of tuberculosis with more than 8 million

new cases reported each year worldwide and more than 2 million deaths.




6 Cholera Bacterial

Biological

Disease

Cholera is a severe infection in the small intestine caused by the bacterium

vibrio cholerae, contracted by drinking water or eating food contaminated

with the bacterium. Cholera symptoms include profuse watery diarrhoea and

vomiting. The primary danger posed by cholera is severe dehydration, which

can lead to rapid death. Cholera can now be treated with re-hydration and

prevented by vaccination. Cholera outbreaks in recorded history have

indeed been explosive and the global proliferation of the disease is seen by

most scholars to have occurred in six separate pandemics, with the seventh

pandemic still rampant in many developing countries around the world. The

first recorded instance of cholera was described in 1563 in an Indian medical

report. In modern times, the story of the disease begins in 1817 when it

spread from its ancient homeland of the Ganges Delta in the bay of Bengal

in North East India - to the rest of the world. The first cholera pandemic

raged from 1817-1823, the second from 1826-1837 The disease reached

Britain during October 1831 - and finally arrived in London in 1832 (13,000

deaths) with subsequent major outbreaks in 1841, 1848 (21,000 deaths)

1854 (15,000 deaths) and 1866. Surgeon John Snow – by studying the

outbreak cantered around the Broad Street well in 1854 – traced the source

of cholera to drinking water which was contaminated by infected human

faeces – ending the “miasma” or “bad air” theory of cholera transmission.



7 Poliomyelitis Viral

Biological

Disease

The history of poliomyelitis (polio) infections extends into prehistory.

Ancient Egyptian paintings and carvings depict otherwise healthy people

with withered limbs, and children walking with canes at a young age.[3] It is

theorized that the Roman Emperor Claudius was stricken as a child, and this

caused him to walk with a limp for the rest of his life. Perhaps the earliest

recorded case of poliomyelitis is that of Sir Walter Scott. At the time, polio

was not known to medicine. In 1773 Scott was said to have developed "a

severe teething fever which deprived him of the power of his right leg." The

symptoms of poliomyelitis have been described as: Dental Paralysis,

Infantile Spinal Paralysis, Essential Paralysis of Children, Regressive

Paralysis, Myelitis of the Anterior Horns and Paralysis of the Morning.

In 1789 the first clinical description of poliomyelitis was provided by the

British physician Michael Underwood as "a debility of the lower extremities”.

Although major polio epidemics were unknown before the 20th century, the

disease has caused paralysis and death for much of human history. Over

millennia, polio survived quietly as an endemic pathogen until the 1880s

when major epidemics began to occur in Europe; soon after, widespread

epidemics appeared in the United States. By 1910, frequent epidemics

became regular events throughout the developed world, primarily in cities

during the summer months. At its peak in the 1940s and 1950s, polio would

maim, paralyse or kill over half a million people worldwide every year

http://en.wikipedia.org/wiki/Prehistory

http://en.wikipedia.org/wiki/Ancient_Egypt

http://en.wikipedia.org/wiki/Roman_Emperor

http://en.wikipedia.org/wiki/Claudius

http://en.wikipedia.org/wiki/Sir_Walter_Scott

http://en.wikipedia.org/wiki/Teething

http://en.wikipedia.org/wiki/Epidemic

http://en.wikipedia.org/wiki/Paralysis

http://en.wikipedia.org/wiki/Millennia

http://en.wikipedia.org/wiki/Endemic_(epidemiology)



8 Typhus Bacterial

Biological

Disease

Typhoid fever (jail fever) is an acute illness associated with a high fever that

is most often caused by the Salmonella typhi bacteria. Typhoid may also be

caused by Salmonella paratyphi, a related bacterium that usually leads to a

less severe illness. The bacteria are spread via deposition in water or food

by a human carrier. An estimated 16–33 million cases of typhoid fever occur

annually. Its incidence is highest in children and young adults between 5 and

19 years old. These cases as of 2010 caused about 190,000 deaths up from

137,000 in 1990. Historically, in the pre-antibiotic era, the case fatality rate of

typhoid fever was 10-20%. Today, with prompt treatment, it is less than 1%.

9 Dysentery Bacterial /

Parasitic

Biological

Disease

Dysentery (the Flux or the bloody flux) is a form of gastroenteritis – a type

inflammatory disorder of the intestine, especially of the colon, resulting in

severe diarrhea containing blood and mucus in the feces accompanied by

fever, abdominal pain and rectal tenesmus (feeling incomplete defecation),

caused by any kind of gastric infection. Conservative estimates suggest

that 90 million cases of Bacterial Dysentery (Shigellosis) are contracted

annually, killing at least 100,000. Amoebic Dysentery (Amebiasis) infects

some 50 million people each year, with over 50,000 cases resulting in death.

http://en.wikipedia.org/wiki/Case_fatality_rate

http://en.wikipedia.org/wiki/Gastroenteritis

http://en.wikipedia.org/wiki/Inflammation

http://en.wikipedia.org/wiki/Intestine

http://en.wikipedia.org/wiki/Colon_(anatomy)

http://en.wikipedia.org/wiki/Diarrhea

http://en.wikipedia.org/wiki/Blood

http://en.wikipedia.org/wiki/Mucus

http://en.wikipedia.org/wiki/Feces

http://en.wikipedia.org/wiki/Fever

http://en.wikipedia.org/wiki/Abdominal_pain

http://en.wikipedia.org/wiki/Rectal_tenesmus

http://en.wikipedia.org/wiki/Rectal_tenesmus

http://en.wikipedia.org/wiki/Infection



10 Spanish

Flu

Viral

Biological

Disease

In the United States, the Spanish Flu was first observed in Haskell County,

Kansas, in January 1918, prompting a local doctor, Loring Miner to warn the

U.S. Public Health Service's academic journal. On 4th March 1918, army cook

Albert Gitchell reported sick at Fort Riley, Kansas. A week later on 11th March

1918, over 100 soldiers were in hospital and the Spanish Flu virus had now

reached Queens New York. Within days, 522 men had reported sick at the

army camp. In August 1918, a more virulent strain appeared simultaneously

in Brest, Brittany-France, in Freetown, Sierra Leone, and in the U.S, in Boston,

Massachusetts. It is estimated that in 1918, between 20-40% of the worlds

population became infected by Spanish Flu - with 50 million deaths globally.

11 Future

Viral

Pandemic

infections

Viral

Biological

Disease

What was Learned from Reconstructing the 1918 Spanish Flu Virus

Comparing pandemic H1N1 influenza viruses at the molecular level yields key

insights into pathogenesis – the way animal viruses mutate to cross species.

The availability of these two H1N1 virus genomes separated by over 90 years,

provided an unparalleled opportunity to study and recognise genetic properties

associated with virulent pandemic viruses - allowing for a comprehensive

assessment of emerging influenza viruses with human pandemic potential.

There are only four to six mutations required within the first three days of viral

infection in a new human host, to change an animal virus to become highly

virulent and infectious to human beings. Candidate viral gene pools for future

possible Human Pandemics include Anthrax, Lassa Fever, Rift Valley Fever,

SARS, MIRS, H1N1 Swine Flu (2009) and H7N9 Avian / Bat Flu (2013).

http://en.wikipedia.org/wiki/Haskell_County,_Kansas

http://en.wikipedia.org/wiki/Haskell_County,_Kansas

http://en.wikipedia.org/wiki/Fort_Riley,_Kansas

http://en.wikipedia.org/wiki/Queens

http://en.wikipedia.org/wiki/Brest,_France

http://en.wikipedia.org/wiki/Freetown

http://en.wikipedia.org/wiki/Boston

http://en.wikipedia.org/wiki/Boston

http://www.microbemagazine.org/index.php?option=com_content&view=article&id=2902:what-we-learned-from-reconstructing-the-1918-influenza-pandemic-virus&catid=675:featured&Itemid=896



12 Future

Bacterial

Pandemic

Infections

Bacterial

Biological

Disease

Bacteria were most likely the real killers in the 1918 Flu Pandemic - the vast

majority of deaths in the 1918–1919 influenza pandemic resulted directly from

secondary bacterial pneumonia, caused by common upper respiratory-tract

bacteria. Less substantial data from the subsequent 1957 and 1968 Flu

pandemics are consistent with these findings. If severe pandemic influenza

is largely a problem of viral-bacterial co-pathogenesis, pandemic planning

needs to go beyond addressing the viral cause alone (influenza vaccines and

antiviral drugs). The diagnosis, prophylaxis, treatment and prevention of

secondary bacterial pneumonia - as well as stockpiling of antibiotics and

bacterial vaccines – should be high priorities for future pandemic planning.

13 HIV / AIDS Viral

Biological

Disease

AIDS was first reported in America in 1981 – and provoked reactions which

echoed those associated with syphilis for so long. Many of the earliest cases

were among homosexual men - creating a climate of prejudice and moral

panic. Fear of catching this new and terrifying disease was also widespread

among the public. The observed time-lag between contracting HIV and the

onset of AIDS, coupled with new drug treatments, changed perceptions.

Increasingly it was seen as a chronic but manageable disease. The global

story was very different - by the mid-1980s it became clear that the virus had

spread, largely unnoticed, throughout the rest of the world. The nature of this

global pandemic varies from region to region, with poorer areas hit hardest. In

parts of sub-Saharan Africa nearly 1 in 10 adults carries the virus - a statistic

which is reminiscent of the spread of syphilis in parts of Europe in the 1800s.




Pandemic Black Swan Events Black Swan Pandemic Type / Location Impact Date

Malaria For the entirety of human history,

Malaria has been a pathogen

The Malaria pathogen kills more

humans than any other disease 20 kya – present

Smallpox (Antonine Plague) Smallpox Roman Empire / Italy Smallpox is the 2nd worst killer 165-180

Black Death (Plague of Justinian) Bubonic Plague – Roman Empire 50 million people died 6th century

Black Death (Late Middle Ages) Bubonic Plague – Europe 75 to 200 million people died 1340–1400

Smallpox Amazonian Basin Indians 90% Amazonian Indians died 16th century

Tuberculosis Western Europe, 18th - 19th c 900 deaths per 100,000 pop. 18th - 19th c

Syphilis Global pandemic – invariably fatal 10% of Victorian men carriers 19th century

1st Cholera Pandemic Global pandemic Started in the Bay of Bengal 1817-1823

2nd Cholera Pandemic Global pandemic (arrived in London in 1832) 1826-1837

Spanish Flu Global pandemic 50 million people died 1918

Smallpox Global pandemic 300 million people died Eliminated 20th c

Poliomyelitis Global pandemic Contracted by up to 500,000

persons per year 1950’s/1960’s 1950’s -1960’s

AIDS Global pandemic – mostly fatal 10% Sub-Saharans are carriers Late 20th century






http://en.wikipedia.org/wiki/Italy

http://en.wikipedia.org/wiki/Plague_of_Justinian

http://en.wikipedia.org/wiki/Late_Middle_Ages


//upload.wikimedia.org/wikipedia/commons/4/44/Krakatoa_eruption_2008.jpg

Geo-thermal Black Swan Event Types

Type Force Extinction-level Black Swan Event

1 Geo-

thermal

Process

Thermal

Energy

Plate Tectonics / Continental Drift – Continental Landmass aggregation at either

the Equator or the Poles (Rodinia, Gondwanaland, Pangea etc.) – events linked

with “Snowball Earth” “Global Dessert” and “Stagnant Sea” Extinction Events.

Divergent Plate Boundaries – occur at mid-oceanic ridges, where two tectonic

plates diverge from one another to create new ocean floor. Convergent Plate

Boundaries - Subduction zones are places where two plates, usually an oceanic

plate and a continental plate, collide. In this case, the oceanic plate dives under the

continental plate forming a deep ocean trench just offshore, and Andesitic volcanic

mountain chains, usually about 90-120 kilometres inland on the continental plate

2 Geo-

thermal

Process

Thermal

Energy

Volcanic Plumes (Hot Spots) - 100 million years ago, a plume of hot rock from

the Earth’s mantle burst through the crust in what is today called Siberia. Those

eruptions raged for centuries, spewing out over a quarter million cubic miles of

basalt floods – the Siberian Traps. Then 65 million years ago, another plume of

hot rock from the Earth’s mantle burst through the crust in what is now India.

Eruptions lasted for centuries, spewing out well over a quarter million cubic miles

of lava flows – the Deccan Traps. Along with the Yucatan Peninsula Meteorite

Impact, many Geologists believe these to be a contributory factor – and a few

believe these volcanic episode to be the major cause of Extinction Events, such as

K-T, which killed the dinosaurs 65 million years ago. Another Volcanic Plume

erupted beneath Iceland – causing America (Palisade Basalts) and Europe (Giants

Causeway) to divide, forming the Atlantic Ocean. The next candidate for flood-

basalt volcanism is Yellowstone Park – its magma chamber is filling up right now.

http://en.wikipedia.org/wiki/Mid-oceanic_ridges



http://en.wikipedia.org/wiki/Tectonic_plate


http://en.wikipedia.org/wiki/Subduction_zones



Volcanic Events in Antarctic Ice Cores

Volcanic Event Years of

Eruption

Years in Ice

Core

Depth in Ice

Core (m)

VEI Impact

Pinatubo 1991 1992 – 1993 0.35–0.45 6 (area evacuated by USAF)

Krakatoa 1883 1884 – 1885 16.83 6 Pyroclastic flow / Tsunami

Coseguina 1835 1835 – 1836 20.48

Tambora 1815 1816 – 1818 21.45 7 "Year Without a Summer"

Thompson Island

(South Atlantic)

1809 1809 – 1811 21.64 Thompson Island completely

disappeared above sea level

Kuwae 1453 1453 – 1455 33.58

(Unknown) 1284 1284 – 1286 46.54

Rinjani, Lombok 1257 – 1258 1259 – 1261 48.81 7 Little Ice Age, Europe

Volcanic Cooling Events in Antarctic Ice Cores - Volcanic hotspots erupt over time with regular

periodically – producing the mid-ocean island chains such as Hawaii and Galapagos. Thermal and

debris shocks along with dust clouds that reach far into the atmosphere, releasing nitrous and sulphuric

gases, carbon dioxide and acid rains – can cause massive climate change around the globe. Flood

basalt volcanic episodes may be causes of climatic and biological change - geologists believe this a

contributory factor for the P-T Boundary Event, which killed off 90% of all the living species on Earth.

http://en.wikipedia.org/wiki/Year_Without_a_Summer

http://en.wikipedia.org/wiki/Rinjani

http://en.wikipedia.org/wiki/Lombok

http://en.wikipedia.org/wiki/Little_Ice_Age

Volcanic Hotspot Black Swan Events

Black Swan Events Location / Volcanic System VEI Impact Date

Eyjafjallajökull, Iceland Icelandic Hotspot, Mid-Atlantic Ridge 4 Air Travel in North Europe 2010

Mount Pinatubo Luzon Volcanic Arc, Philippine Islands 6 (area evacuated by USAF) 1991

Mount St. Helens, United States Cascade Volcanic Arc 5 (low population / impact) 1980

Krakatoa Sunda Arc, Indonesia 6 Pyroclastic flow / Tsunami 1883

Mount Tambora, Indonesia Lesser Sunda Islands 7 "Year Without a Summer" 1815

Kolumbo eruption, Santorini South Aegean Volcanic Arc, Greece 6 Pyroclastic flow / Tsunami 1650

Rinjani, Lombok Lesser Sunda Islands 7 Little Ice Age, Europe 1257 / 1258

Ilopango, El Salvador Central America Volcanic Arc 6 End of Mayan Culture 536

Mount Vesuvius, Italy Eurasian/African Plate Boundary 5 Herculaenum, Pompei 79 AD

Minoan eruption at Akrotiri Santorini (Thera), Greece 7 End of Minoan Culture 1610 BC

Yellowstone Caldera Yellowstone hotspot, United States 8 Pleistocene, Quaternary 640 ka

Henry's Fork Caldera Yellowstone hotspot, United States 7 Pleistocene, Quaternary 1.3 Ma

Island Park Caldera Yellowstone hotspot, United States 8 Pleistocene, Quaternary 2.1 Ma

http://en.wikipedia.org/wiki/Eyjafjallaj%C3%B6kull

http://en.wikipedia.org/wiki/Iceland

http://en.wikipedia.org/wiki/Mount_Pinatubo

http://en.wikipedia.org/wiki/Luzon

http://en.wikipedia.org/wiki/Mount_St._Helens

http://en.wikipedia.org/wiki/United_States

http://en.wikipedia.org/wiki/Cascade_Volcanic_Arc

http://en.wikipedia.org/wiki/Krakatoa

http://en.wikipedia.org/wiki/Sunda_Arc



http://en.wikipedia.org/wiki/Indonesia

http://en.wikipedia.org/wiki/Mount_Tambora

http://en.wikipedia.org/wiki/Mount_Tambora

http://en.wikipedia.org/wiki/Indonesia

http://en.wikipedia.org/wiki/Lesser_Sunda_Islands




http://en.wikipedia.org/wiki/Year_Without_a_Summer

http://en.wikipedia.org/wiki/Kolumbo_(volcano)

http://en.wikipedia.org/wiki/Rinjani

http://en.wikipedia.org/wiki/Lombok





http://en.wikipedia.org/wiki/Little_Ice_Age

http://en.wikipedia.org/wiki/Ilopango_(volcano)

http://en.wikipedia.org/wiki/Mount_Vesuvius

http://en.wikipedia.org/wiki/Minoan_eruption

http://en.wikipedia.org/wiki/Akrotiri

http://en.wikipedia.org/wiki/Santorini

http://en.wikipedia.org/wiki/Yellowstone_Caldera

http://en.wikipedia.org/wiki/Yellowstone_hotspot


http://en.wikipedia.org/wiki/Henry's_Fork_Caldera



http://en.wikipedia.org/wiki/Island_Park_Caldera



Geo-thermal Black Swan Events


1 Geo-

thermal

Process

Thermal

Energy

Volcanic Plumes (Hot Spots) - "Hotspots" are the volcanic provinces thought

to be formed by volcanic mantle plumes – which are thought to be caused by

convection columns of hot material that rise from the core-mantle boundary. It

has been suggested that volcanic plumes are fixed in position, and that their

thermal energy causes melting at the base of the Earths’ crust. As the tectonic

plates which form the Earths’ crust moves over hot mantle plumes, the oldest

volcano in the chain is carried further away from the plume and after a while

becomes dormant. Over time - as the Earths’ crust gradually shifts in relation to

the fixed position of the mantle plume - new volcanoes erupt in a fresh position

on the chain. The Hawaiian Islands have been formed in this manner, as well as

the Snake River Plain, with the Yellowstone Caldera – that part of the North

American plate which is currently standing above the Yellowstone mantle plume.

Volcanic hotspots erupt over time with regular periodically – producing the mid-

ocean island chains such as Hawaii and Galapagos. Thermal and debris shocks

along with dust clouds that reach far into the atmosphere, releasing nitrous and

sulphuric gases, carbon dioxide and acid rains – can cause massive climate

change around the globe. Flood basalt volcanic episodes may be causes of

climatic and biological change - geologists believe this a contributory factor for

the P-T Boundary Event, which killed off 90% of all the living species on Earth.

http://en.wikipedia.org/wiki/Hotspot_(geology)

http://en.wikipedia.org/wiki/Mantle_plume

http://en.wikipedia.org/wiki/Hawaiian_Islands

http://en.wikipedia.org/wiki/Snake_River_Plain

http://en.wikipedia.org/wiki/Yellowstone_Caldera

Volcanic Hotspot Map

1. Azores hotspot (1) 2. Balleny hotspot (2) 3. Bowie hotspot (3) 4. Caroline hotspot (4) 5. Cobb hotspot (5) 6. Darfur hotspot (6) 7. Easter hotspot (7) 8. Eifel hotspot (8) 9. Fernando hotspot (9) 10. Galápagos hotspot (10) 11. Guadalupe hotspot (11) 12. Hawaii hotspot (12) 13. Hoggar hotspot (13) 14. Iceland hotspot (14) 15. Jan Mayen hotspot (15) 16. Juan Fernández hotspot 17. Cameroon hotspot (17) 18. Canary hotspot (18) 19. Cape Verde hotspot (19) 20. Kerguelen hotspot (20) 21. Comoros hotspot (21) 22. Lord Howe hotspot (22)

//upload.wikimedia.org/wikipedia/commons/1/19/Hotspots.jpg

http://en.wikipedia.org/wiki/Azores_hotspot

http://en.wikipedia.org/wiki/Balleny_hotspot



http://en.wikipedia.org/wiki/Bowie_hotspot

//en.wikipedia.org/wiki/Caroline_hotspot

http://en.wikipedia.org/wiki/Cobb_hotspot

//en.wikipedia.org/wiki/Darfur_hotspot

http://en.wikipedia.org/wiki/Easter_hotspot

http://en.wikipedia.org/wiki/Eifel_hotspot

//en.wikipedia.org/wiki/Fernando_hotspot

http://en.wikipedia.org/wiki/Gal%C3%A1pagos_hotspot

//en.wikipedia.org/wiki/Guadalupe_hotspot

//en.wikipedia.org/wiki/Hawaii_hotspot

//en.wikipedia.org/wiki/Hoggar_hotspot



//en.wikipedia.org/wiki/Iceland_hotspot

http://en.wikipedia.org/wiki/Jan_Mayen_hotspot




http://en.wikipedia.org/wiki/Juan_Fern%C3%A1ndez_hotspot




//en.wikipedia.org/wiki/Cameroon_hotspot

//en.wikipedia.org/wiki/Canary_hotspot

//en.wikipedia.org/wiki/Cape_Verde_hotspot

http://en.wikipedia.org/wiki/Kerguelen_hotspot

//en.wikipedia.org/wiki/Comoros_hotspot

//en.wikipedia.org/wiki/Lord_Howe_hotspot

Volcanic Hotspot Map

23. Louisville hotspot (23) 24. Macdonald hotspot (24) 25. Marion hotspot (25) 26. Marquesas hotspot (26) 27. Meteor hotspot (27) 28. New England hotspot (28) 29. Society hotspot (38) 30. East Australia hotspot (30) 31. Pitcairn hotspot (31) 32. Raton hotspot (32) 33. Réunion hotspot (33) 34. St. Helena hotspot (34) 35. Samoa hotspot (35) 36. San Felix hotspot (36) 37. Socorro hotspot (37) 38. Tahiti hotspot (38) 39. Tasmanid hotspot (39) 40. Tibesti hotspot (40) 41. Trindade hotspot (41) 42. Tristan hotspot (42) 43. Vema hotspot (43) 44. Yellowstone hotspot (44)

//upload.wikimedia.org/wikipedia/commons/1/19/Hotspots.jpg

http://en.wikipedia.org/wiki/Louisville_hotspot

http://en.wikipedia.org/wiki/Macdonald_hotspot

//en.wikipedia.org/wiki/Marion_hotspot

http://en.wikipedia.org/wiki/Marquesas_hotspot

http://en.wikipedia.org/wiki/Meteor_hotspot

http://en.wikipedia.org/wiki/New_England_hotspot

//en.wikipedia.org/wiki/Society_hotspot

http://en.wikipedia.org/wiki/East_Australia_hotspot

//en.wikipedia.org/wiki/Pitcairn_hotspot

//en.wikipedia.org/wiki/Raton_hotspot

//en.wikipedia.org/wiki/R%C3%A9union_hotspot



//en.wikipedia.org/wiki/St._Helena_hotspot

//en.wikipedia.org/wiki/Samoa_hotspot

//en.wikipedia.org/wiki/San_Felix_hotspot

//en.wikipedia.org/wiki/Socorro_hotspot

//commons.wikimedia.org/w/index.php?title=Tahiti_hotspot&action=edit&redlink=1

//en.wikipedia.org/wiki/Tasmanid_hotspot



//en.wikipedia.org/wiki/Tibesti_hotspot



//en.wikipedia.org/wiki/Trindade_hotspot



//en.wikipedia.org/wiki/Tristan_hotspot

//en.wikipedia.org/wiki/Vema_hotspot



//en.wikipedia.org/wiki/Yellowstone_hotspot

Yellowstone Caldera Map

Volcanic Hot-spots

Volcanic hotspots periodically

erupt - producing thermal and

debris shocks along with dust

clouds that reach far into the

upper atmosphere, releasing

nitrous and sulphuric gases,

carbon dioxide and acid rains -

in turn causing massive climate

change around the globe.

Yellowstone Caldera, Henry's

Fork Caldera, Island Park

Caldera and Heise Volcanic

Field all share a common

magma chamber – beneath the

Yellowstone National Park.

A future candidate for hot-spot

volcanism is quietly building up

right now – with the magma

source beneath Yellowstone

Park filling up with lava - ready

for the next super-volcano......

//upload.wikimedia.org/wikipedia/en/0/0a/Yellowstone_Caldera_map2.JPG

Yellowstone Hotspot Timeline

//upload.wikimedia.org/wikipedia/en/4/40/HotspotsSRP.JPG

Geo-thermal Black Swan Event Types


2 Geo-

thermal

Process

Thermal

Energy

Divergent Plate Boundaries - at the mid-oceanic ridges, two tectonic plates

diverge from one another. New oceanic crust is being formed by fluid basaltic

magma slowly cooling and solidifying. The crust is very thin at mid-oceanic

ridges due to the pull of the tectonic plates. The release of pressure due to the

thinning of the crust leads to adiabatic expansion, and the partial melting of the

mantle causing volcanism and creating new oceanic crust. Most divergent plate

boundaries are at the bottom of the oceans, therefore most volcanic activity is

submarine, forming new seafloor. Black smokers or deep sea vents are an

example of this kind of volcanic activity. Where the mid-oceanic ridge is above

sea-level, volcanic islands are formed from basalt magma, for example, Iceland.

3 Geo-

thermal

Process

Thermal

Energy

Convergent Plate Boundaries - Subduction zones are places where two

plates, usually an oceanic plate and a continental plate, collide. In this case, the

oceanic plate subducts, or submerges under the continental plate forming a

deep ocean trench just offshore. In a process called flux melting, water released

from the subducting plate lowers the melting temperature of the overlying

mantle wedge, creating magma. This magma tends to be very viscous due to its

high silica content, so often does not reach the surface and cools at depth.

When it does reach the surface, a volcano is formed. As the magma is both

viscous (silica) and gaseous (water, carbon dioxide, sulphates and nitrates) –

andesitic magma eruptions are very explosive. Typical examples for this kind of

andesitic volcano are found in the volcanoes in the Pacific Ring of Fire. (the

Rockies and the Andes), and the Mediterranean Basin (Mount Etna).





http://en.wikipedia.org/wiki/Oceanic_crust

http://en.wikipedia.org/wiki/Adiabatic_process

http://en.wikipedia.org/wiki/Mantle_(geology)

http://en.wikipedia.org/wiki/Divergent_boundary

http://en.wikipedia.org/wiki/Divergent_boundary

http://en.wikipedia.org/wiki/Black_smoker

http://en.wikipedia.org/wiki/Iceland




http://en.wikipedia.org/wiki/Flux_melting

http://en.wikipedia.org/wiki/Magma

http://en.wikipedia.org/wiki/Viscous

http://en.wikipedia.org/wiki/Silica

http://en.wikipedia.org/wiki/Pacific_Ring_of_Fire

http://en.wikipedia.org/wiki/Mount_Etna

Divergent Plate Boundaries

World map showing the divergent plate boundaries (OSR – Oceanic Spreading Ridges)

//upload.wikimedia.org/wikipedia/commons/b/b6/Spreading_ridges_volcanoes_map-en.svg

Divergent Plate Boundaries

Convergent Plate Boundaries

• A convergent plate boundary is where two or more tectonic plates collide with each other creating massive crustal movements. The Himalayas were formed by such a collision. Earthquakes and volcanoes are common near convergent boundaries as a result of displacement - pressure, friction, and crustal plate material melting deep in the mantle,

• These diagrams illustrates some differences between the three types of subduction zone: -

1. Continental crust moves under a continental plate. The leading edge of the continental plate margin thrusts up into a horse-shoe shaped mountain range. This forms a high plateau. The Himalayas and the Tibetan plateau are a perfect example of this.

2. Oceanic crust dives under a continental plate. A deep ocean trench forms at the coast, and an arc of mountainous volcanoes forms inland – as seen along the western edge of much of the Americas.

3. Oceanic crust dives under an oceanic plate – with crustal material melting deep in the mantle to form arc-shaped volcanic island chains.

http://simple.wikipedia.org/wiki/Tectonic_plate

http://simple.wikipedia.org/wiki/Himalayas

http://simple.wikipedia.org/wiki/Earthquake

http://simple.wikipedia.org/wiki/Volcano

http://simple.wikipedia.org/wiki/Mantle_(geology)

http://simple.wikipedia.org/wiki/Mountain_range

http://simple.wikipedia.org/wiki/Plateau

http://simple.wikipedia.org/wiki/Himalayas

http://simple.wikipedia.org/wiki/Tibetan_plateau

http://simple.wikipedia.org/wiki/Ocean_trench

http://simple.wikipedia.org/wiki/Coast

http://simple.wikipedia.org/wiki/Volcanoes

http://simple.wikipedia.org/wiki/Americas

//upload.wikimedia.org/wikipedia/commons/8/83/Continental-continental_convergence_Fig21contcont.gif

//upload.wikimedia.org/wikipedia/commons/2/29/Active_Margin.svg

//upload.wikimedia.org/wikipedia/commons/0/0c/Oceanic-oceanic_convergence_Fig21oceanocean.gif

• Case Study • Earthquakes

• Earthquakes are created, for example, in orogenic (mountain building) events, when

adjacent stratigraphic units are laterally compressed and fold over one another

(thrust faulting), or when a single stratigraphic unit situated between two parallel

geological faults becomes laterally stretched and the unit in the fault zone slips down

in relation to the fault planes (normal faulting), or when either Continental or Oceanic

Plate Margins are stuck against each by the forces of friction. Under cumulative

stress, static friction forces are eventually overcome – the Plate Margins suddenly

become mobile and detached, moving relative to each other as the built-up stress is

relieved (Plate Tectonics) – releasing massive amounts of energy in the process.

• Over time stress builds up at the fault-line boundary, eventually overcoming friction

causing the adjacent stratigraphic units to “unzip” dramatically – slip and slide over,

along or away (mid-ocean ridge) from each other - releasing a sequence of energetic

tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from

side-to-side. The P and S waves from the Earthquake propagate rapidly through the

earth in a related and integrated series of waves - but travelling at different speeds.

The first waves to arrive at an observer of the event are vertical (up / down)

disturbances (P-waves) which are followed moments later by a horizontal (side-to-

side) disturbance (S-waves) which have increased magnitude and intensity.

Risk Example – Inter-connected Hazards

A Trigger

A

Plate Tectonics

B Factor

B

Pacific Earthquake

Warning System

Japan is an Andesitic Volcanic Island Chain on the

Asian Continental Plate 100k from the Pacific Plate

Japanese Pacific Earthquake Warning System was developed to give early warning of events

C Trigger

C


Risk Event

Risk Event

D

Related Risk Example – Pacific Earthquake Event March 11, 2011

The Asian Continental Plate slides over the Pacific Plate

Risk Event

Mitigation Factor

The Continental Plate rises 10m, displacing a 10m water

column and the whole of Japan moves 3m towards the east while the east cost of Japan falls 1m relative to sea level

Risk Event

E

P-waves travelling at 800km / hr arrive in Tokyo Earthquake Monitoring Centre

Risk Event

F

S-waves travelling at 400km / hr arrive in Tokyo Earthquake Monitoring Centre

Trigger D

Pacific Earthquake

Event – 11.03.2011

At 5.46am GMT (2.46pm in Japan) a massive

earthquake occurred, registering 9.0 on the

Richter scale – followed over the next few

days by hundreds of smaller after-shocks


Pacific Earthquake Event March 11, 2011

• Case Study • Volcanic Eruptions

• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked

and integrated events which have a common origin or source - in turn generating a

sequence of waves in ascending orders of magnitude. Pyroclastic Volcanic

Eruptions begin with a sequence of Random Events - in this case, it is a sequence

of Earthquakes somewhere deep under an Mountain Chain which is built up from

Andesitic Volcanoes – such as the Andes Mountain Chain.

• The Andes Mountains are parallel with the Pacific Oceanic Plate subduction zone –

an area where the Pacific Oceanic Plate plunges under the South American

Continental Margin. Sediment, sea water and organic remains from the Ocean floor

are carried down towards earth’s mantle and heat up as the Oceanic Plate plunges

deeper into the Earth’s Mantle. Liquids and gases released by this heating cause

the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid.

This liquid then rises through the Earth’s crust and travels towards the surface,

collecting in pools forming Magma Chambers - before finally breaking at the surface

through and erupting as Volcanic Magma.


• When adjacent Continental and Oceanic Plates are stuck together, over time they can

periodically unzip and slide over each other – thus Tectonic Earthquakes are created

causing a sequence of tremors or waves. P-waves oscillate up-and-down, whilst S-waves

oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly

through the earth in a related and integrated series of waves - but travelling at different

speeds. The first waves to arrive at an observer of the event are vertical (up / down)

disturbances (P-waves – 800 km/hr) which are followed moments later by a horizontal

(side-to-side) disturbance (S-waves – 400 km/hr) with increased magnitude and intensity.

• P-waves travel fastest through the earth so they arrive first, as Weak Signals. The faster

P-waves are followed by slower but more dramatic and intense S-waves – Shock Waves –

Strong Signals now indicating what is about to follow. Next in the sequence is the Wild

Card Event. As the volcano erupts, its ash cloud builds up high into the atmosphere.

Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash column

can no longer support its own weight. It collapses in onto itself and plunges down the

slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of the

Pyroclastic wave covers the landscape with layers of suffocating, burning hot ash and

destroys all life in a black cloud covering over everything that lies before it. This is, for

example, what happened in 63 AD when superheated magma beneath Vesuvius erupted

and covered Herculaneum and Pompeii with over twenty metres of rocks and ash.

• Case Study • Tsunami Events

• • CASE STUDY • A Tsunami Event consists of a sequence of linked and integrated

waves in ascending orders of magnitude which have a common origin or source – in this

case, the Random Events begin with a series of chaotic and disruptive Earthquakes

somewhere offshore in a subduction zone at a Continental and Oceanic Plate Margin.

• Earthquakes are formed as the Continental and Oceanic Plates stick together – and then

unzip, causing a sequence of random and chaotic tremors. The P and S waves from the

Earthquake propagate rapidly through the earth as a related and integrated series of

waves travelling at different speeds – the first to arrive are vertical (up / down) wave

disturbances (P-waves) - which can travel directly through the Earths crust, mantle and

core – which are followed by slower but more powerful horizontal (side-to-side) waves (S-

waves) – accompanied by further increases in wave magnitude and intensity – which can

only travel through the Earths crust and mantle, and so “bounces” around the liquid core.

• To sum up, the P and S waves from the Earthquake propagate rapidly through the earth in

a related and integrated series of wave forms travelling at different speeds – the first to

arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal

(side-to-side) disturbance (S-waves) – with further increased magnitude and intensity.

• Case Study • Tsunami Events

• P-waves travel fastest through the earth at 800 km/hr - so they arrive first as Weak

Signals – subliminal messages. These faster P-waves are followed by slower but more

dramatic and intense S-waves at 400 km/hr – these are the Shock Waves - Strong

Signals which are now a firm indication of what is about to come. The next in the

sequence of waves is the first Ocean Wave – up to 10 m in height and 100 km in length

- which arrives at the coastline travelling at 300km/hr. The sea initially draws rapidly

away from the coast so the sea level dramatically falls as the edge of the Tsunami

Wave withdraws water away from the shore – this is the final warning, the Wild Card

Event – this is the last window of opportunity to make good your escape to higher

ground – before finally the Tsunami Wave crashes over the land, sweeping all away .

• The last Wave in the sequence - the Tsunami Wave - crashes over the land, sweeping

everything before it as a catastrophic Black Swan Event. The chaotic and radically

disruptive Tsunami Wave travels through the ocean waters at hundreds of miles per

hour and arrives as the final Black Swan Event. Surging relentlessly inland, threatening

life and shifting boats, buildings and scenery – the catastrophic disturbance of the

Tsunami Wave, a wall of water, arrives as a relentless black wave swallowing up

everything as it sweeps inland carrying with it everything that lies in its path. This type

of Black Swan Event has already occurred twice this century – first, the Indian Ocean

Boxing Day Tsunami and secondly the Pacific Ocean Japanese Tsunami.

Related Risk Example – Japanese Tsunami Event March 11, 2011

Trigger D

Pacific Earthquake Event – 11.03.2011


G

Factor E


Risk Event

Risk Event

H

The Continental Plate rises 10m, displacing a 10m water

column and the whole of Japan moves 3m towards the east while the east cost of Japan falls 1m relative to sea level

Risk Event

I

Japanese Coast-guard Vessel encounters the first 1m Tsunami Wave far out at sea

Risk Event

J

East Coast Flood Defences are overwhelmed by 10m Tsunami

Trigger F

Japanese Tsunami

Event – 11.03.2011

Stress builds up between Eurasian and Pacific Plates

K

E Trigger

K

Narita

Flooding

Disaster

B Trigger

I

Sendai

Flooding

Disaster

C Trigger

H

Fukushima Reactor

Disaster

D Trigger

G

L

Japanese Coastal

Flood Defences A

Trigger J

Fukushima

Flooding

Disaster

Miyagi

Flooding

Disaster

At 5.46am GMT (2.46pm in Japan) a massive

earthquake registered 9.0 on the Richter

scale, triggers a huge tsunami which

devastates Japan's eastern coastline,

Mitigation Factor

Japanese flood defences were re-built @5m above Sea level after the previous Tsunami event in 1967

http://news.sky.com/skynews/Home/World-News/Japan-Tsunami-And-Earthquake---Fears-Grow-Over-Nuclear-Power-Plants/Article/201103215951274


• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked

and integrated events which have a common origin or source - in turn generating a

sequence of waves in ascending orders of magnitude. Pyroclastic Volcanic

Eruptions begin with a sequence of Random Events - in this case, it is a sequence

of Earthquakes somewhere deep under an Mountain Chain which is built up from

Andesitic Volcanoes – such as the Andes Mountain Chain.

• The Andes Mountains are parallel with the Pacific Oceanic Plate subduction zone –

an area where the Pacific Oceanic Plate plunges under the South American

Continental Margin. Sediment, sea water and organic remains from the Ocean floor

are carried down towards earth’s mantle and heat up as the Oceanic Plate plunges

deeper into the Earth’s Mantle. Liquids and gases released by this heating cause

the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid.

This liquid then rises through the Earth’s crust and travels towards the surface,

collecting in pools forming Magma Chambers - before finally breaking at the surface

through and erupting as Volcanic Magma.

Krakatoa Volcanic Eruption Event

• On 27 August 1883, after a day of alarming volcanic activity, Krakatoa (an uninhabited

island in the Sunda Straits between Java and Sumatra, the remains of which are now

widely known as Anak Krakatau) erupted with a force more than ten thousand times

that of the atomic bomb dropped on Hiroshima (Thornton 1). Dutch officials in Java

reported the eruption around the world via undersea telegraph cables.

• This explosion, the loudest noise in historic times, was heard thousands of miles away

and instruments around the world recorded changes in air pressure and sea level. In

the months that followed, newspapers and journals printed vivid accounts of the

spectacular sunsets caused by fine particles that the volcano spewed into the upper

atmosphere and that circled the globe, gradually spreading further north and south.

• Rogier Diederik Marius Verbeek, a Dutch Mining Engineer, published his findings in

the Krakatoa Journal – the first scientific study of a volcanic eruption. Captain Johan

Lindemann, of the ship Governor General Loudon sailing in the Sunda Strait, gave

an eye-witness account of how he survived both the eruption and the tsunami. The

Royal Society formed a special Krakatoa Committee to collect these articles, other

eye-witness testimony, and more precise scientific data (such as barograph readings of

air pressure, temperature records and a series of sketches of the dust cloud) and

analyse the material meticulously in order to publish a thorough report of their findings.

Related Risk Example – Krakatoa Volcanic Eruption Event, 27 August 1883

Sunda Strait Shipping Disaster –

“Governor General Loudon” – ship

saved by action of Captain Lindemann,,

“Berouw” – ran aground 1km inland on

Sumatra - ship and crew total loss

Krakatoa


• In 1883, the volcanic island of Krakatoa erupted without warning. Within a day the

island had virtually disappeared in the loudest explosion ever recorded. The eruption

generated a succession of massive tsunamis that wiped out the Indonesian coastline

and killed over 30,000 people. These waves were three times higher than those seen

on Boxing Day in 2004. And over thirty miles from the volcano, across open ocean,

thousands more were killed by hot ash. The wife of Controller Beyerinck described her

experience on the morning of August 27, when the outermost edges of a pyroclastic

flow enveloped the Sumatra village of Ketimbang

• "Suddenly, it became pitch dark. The last thing I saw was the ash being pushed up through the cracks in the floorboards, like a fountain. I turned to my husband and heard him say in dispair ' Where is the knife?' . . . I will cut all our wrists and then we shall be released from our suffering sooner.' The knife could not be found. I felt a heavy pressure, throwing me to the ground. Then it seemed as if all the air was being sucked away and I could not breathe. . . . I felt people rolling over me . . . No sound came from my husband or children . . . I remember thinking, I want to . . . go outside . . . . but I could not straighten my back . . . I tottered, doubled up, to the door . . . I forced myself through the opening . . . I tripped and fell. I realized the ash was hot and I tried to protect my face with my hands. The hot bite of the pumice pricked like needles . . . Without thinking, I walked hopefully forward. Had I been in my right mind, I would have understood what a dangerous thing it was to . . . plunge into the hellish darkness.....”

Krakatoa

Krakatoa – prior to 1883 Event

Sunda Strait Shipping

Disaster – “Governor

General Loudon” – ship

saved by action of

Captain Lindemann,,

“Berouw” – ran aground

1km inland on Sumatra -

ship and crew total loss

Krakatoa Explosion

Event – 27.11.1883

Krakatoa

//upload.wikimedia.org/wikipedia/commons/3/34/Sunda_strait_map_v3.png

//upload.wikimedia.org/wikipedia/commons/5/5b/Krakatoa_evolution_map-en.gif

http://en.wikipedia.org/wiki/File:Map_krakatau.gif


• Several surveys and mariners' charts were made of Krakotoa,

but the islands were little explored or studied. An 1854 map of

the islands was used in an English chart, which shows some

difference from a Dutch chart made in 1874. In July 1880,

Rogier Verbeek, a Dutch Mining Engineer, made an official

survey of the islands but he was only allowed to spend a few

hours there. He was able to collect samples from several

places, and his investigation proved important in judging the

geological impact of the 1883 eruption

• For over a century geologists have been hard pressed to

explain why so many people died – but through field studies,

experiments and analysis of historical records they think they

have finally found the answers - hugely important because

volcanic activity has returned. Since the 1883 eruption, in

1927, a new island volcano, called Anak Krakatau ("Child of

Krakatoa"), has formed in the caldera, and is now grown to

over half the size of the original volcano. Geologists are

certain that Anak Krakatoa will erupt again. Of considerable

interest to volcanologists is the question of how and when.....

http://en.wikipedia.org/wiki/Surveying

http://en.wikipedia.org/wiki/Mariner

http://en.wikipedia.org/wiki/Nautical_chart

http://en.wikipedia.org/wiki/Rogier_Verbeek




• The magma chamber beneath Krakatoa could have become over-pressurised by volatile

saturation and/or a magma mixing event - which may have triggered the 1883 eruption of

Krakatau. From the beginning of activity on 20 May to the onset of the 22–24 hour-long

climactic phase on 26–27 August, Krakatau produced a discontinuous series of vulcanian

to sub-plinian eruptions. Based on contemporary descriptions, the intensity of these

phases may previously have been underestimated.

• Very rapid displacement of the sea by pyroclastic flows remains the best explanation for

the series of catastrophic sea waves that devastated the shores of the Sunda Straits,

with the last and largest tsunami coinciding with the slumping of half of Rakata cone into

the actively forming caldera, perhaps during a period of great pyroclastic flow production.

• The large audible explosions recorded on 27 August may have been the rapid ejection of

large pulses of magma that collapsed to form pyroclastic flows in the ignimbrite-forming

phase. Co-ignimbrite ash columns rising in the atmosphere immediately after the

generation of each major pyroclastic flow may have contributed to the magnitude of the

air waves. A reappraisal of the eruption in the light of this, in conjunction with the

pressure (air wave) and tide gauge (tsunami) records from Jakarta, suggests that the

relationship between the latter two has been oversimplified in previous studies.


• The most realistic estimate of eruptive volume (magnitude) is about 10 km3 of dacitic

magma. The climax of the eruption began at 1:00 pm on 26 August with a plinian

phase which led into a 5-hour-long ignimbrite-producing phase. Caldera collapse most

probably occurred near the end of the eruption on 27 August, precluding large scale

magma-seawater interaction as a major influence on the eruption column and

characteristics of the pyroclastic deposits.

• Although no one is known to have been killed as a result of the initial explosion, the

tsunamis it generated had disastrous results, killing some 36,000 people and wiping

out a number of settlements, including Telok Batong in Sumatra, and Sirik and

Semarang in Java. An additional 1,000 or so people died from superheated volcanic

ash which literally rushed across the surface of the ocean. Ships as far away as South

Africa rocked as tsunamis hit them, and the bodies of victims were found floating in the

ocean for weeks after the event. There are even numerous documented reports of

groups of human skeletons floating across the Indian Ocean on rafts of volcanic

pumice and washing up on the east coast of Africa up to a year after the eruption.


• The 1883 eruption was amongst the most severe volcanic explosions in modern times

(VEI of 6, equivalent to 200 megatons of TNT - by way of comparison, the biggest bomb

ever made by man, Tsar Bomba, is around 50 megatons). Concussive air waves from

the explosions travelled seven times around the world, and the sky was darkened for

days afterwards. The island of Rakata itself largely ceased to exist as over two thirds of

its exposed land area was blown to dust, and its surrounding ocean floor was drastically

altered. Two nearby islands, Verlaten and Lang, had their land masses increased.

Volcanic ash continues to be a significant part of the geological composition of these

islands.

• It has been suggested that an eruption of Krakatoa may have been responsible for the

global climate changes of 535-536. Additionally, in recent times, it has been argued that

it was this eruption which created the islands of Verlaten and Lang (remnants of the

original) and the beginnings of Rakata - all indicators of that early Krakatoa's caldera

size, and not the long-believed eruption of c. 416, for which conclusive evidence does

not exist.


• The cataclysmic blasts of August 27 generated mountainous tsunamis, up to 40 m tall, that ravaged coastlines across the Sunda Straits. Many of the closest islands were completely submerged. After first being overwhelmed by massive pyroclastic flows Sebesi Island northeast of Krakatau, was inundated by a series of mammoth sea waves. The tsunami waves stripped away all vegetation, washed ~3000 people out to sea, and destroyed all signs of human occupation. Although located at seemingly safe distance, some 80 km east of the Sunda Straits, the low-lying Thousand Islands were buried by at least 2 m of seawater and their inhabitants had to save themselves by climbing trees.

• Eyewitness accounts of the massive waves came from passengers of the Governor General Loudon, who survived the tsunami wave only through the heroic efforts of its Captain, Johan Lindemann. The ship was anchored in Lampong Bay, near the village of Telok Betong when the first of several waves arrived on Monday morning: -

• "Suddenly we saw a gigantic wave of prodigious height advancing toward the seashore with considerable speed. Immediately, the crew . . .managed to set sail in face of the imminent danger; the ship had just enough time to meet with the wave from the front. The ship met the wave head on and the Loudon was lifted up with a dizzying rapidity and made a formidable leap... The ship rode at a high angle over the crest of the wave and down the other side. The wave continued on its journey toward land, and the benumbed crew watched as the sea in a single sweeping motion consumed the town. There, where an instant before had lain the town of Telok Betong, nothing remained but the open sea."


• Tsunami travel times from Krakatau to the Indonesian Coast (Java and Sumatra)

probably varied more than hitherto thought and there need not be a simple correlation

between the initiation times of the explosions and the arrival of the tsunamis. There is

some new evidence, however, that tsunamis in the Sunda Straits and vicinity were

probably influenced by coupling with the steam front and air waves generated by the

Pyroclastic Clouds as they skimmed across the sea. Various hypotheses about the

cause of the tsunamis and explosions are reviewed and it is concluded that the cause

of both is most likely related to the sudden emission of large pulses of magma

interacting with sea water when Krakatoa failed (similar in manner to St Helens in

1981) collapsing the volcano into the sea – which led to formation of the Krakatau

ignimbrite. Some future investigation of sea-floor deposits in the vicinity of Krakatoa

on the floor of the Sunda Strait will inform this debate.....

• The atmospheric dust from the Krakatoa eruption circulated in the upper atmosphere

for years – contributing to the “year without a summer”. The explosion produced

spectacular sunsets throughout the world for many months afterwards, as a result of

sunlight reflected from suspended dust particles ejected by the volcano high into

Earth's atmosphere. Interestingly, researchers in 2004 proposed the idea that the

blood-red sky shown in Edvard Munch's famous 1893 painting “The Scream” is an

accurate depiction of the sky over Norway after the 1883 eruption of Krakatoa.


A Trigger

A

Plate Tectonics

The Philippine Plate slides over the Indian Ocean Plate

C Trigger

B


Risk Event

Risk Event

D

Related Risk Example – Krakatoa Eruption Event, 20 May - 26 August 1883

The magma chamber beneath Krakatoa becomes over-pressurised

by volatile saturation and/or a magma mixing event - which may have contributed to or triggered the 1883 eruption of Krakatau

Risk Event

Risk Event

E

From the beginning of volcanic activity on 20 May

to the onset of the 22–24 hour-long climactic phase

26–27 August, Krakatau produced a discontinuous series of vulcanian to sub-plinian eruption events

Risk Event

F

Caldera collapse most probably occurred near the end of

the eruption on 27 August, precluding large scale magma-

seawater interaction as a major influence on the eruption column and characteristics of the pyroclastic deposits

Trigger G

Krakatoa Eruption

Event – 20.05.1883

26 August 1883 was a day of alarming volcanic

activity on Krakatoa Island in the Sunda Strait

Krakatoa Explosion

Event – 27.11.1883

26 August 1883 was a day of alarming volcanic

activity on Krakatoa Island in the Sunda Strait

G

Trigger F

“Year without

a summer”

Climate Event

Related Risk Example – Krakatoa Disaster Event, 27 August 1883


Trigger F

Krakatoa Tsunami

Event – 27.11.1883

E Trigger

E

Sumatra

Pyroclastic

Disaster B

Trigger B

Java

Flooding

Disaster

C Trigger

C

Sunda Strait

Shipping

Disaster

D Trigger

D

L

A Trigger

A

Sumatra

Flooding

Disaster

Java

Pyroclastic

Disaster

On 27 August 1883, after a day of alarming volcanic activity,

Krakatoa (an uninhabited island in the Sunda Strait between

Java and Sumatra, the remains of which are now widely known

as Anak Krakatau) - erupted with a force more than ten

thousand times that of the atomic bomb dropped on Hiroshima

H

Risk Event

EI

P-waves travelling at 800km / hr cross Sunda Straight and arrive in Java and Sumatra

Risk Event

S-waves travelling at 400km / hr cross Sunda Straight and arrive in Java and Sumatra

Trigger G

Krakatoa Explosion

Event – 27.11.1883

J

Risk Event

E

Pyroclastic Cloud travelling at 500km / hr cross the Sunda Straight and arrive in Java and Sumatra

Risk Event

Tsunami wave travelling at 300km / hr crosses Sunda Straight and arrives in Java and Sumatra

Trigger F

Krakatoa Pyroclastic

Event – 27.11.1883

L

E Factor

F

“Year without

a summer”

Climate Event

Pyroclastic Cloud crosses

Sunda Straight and drives 1st Tsunami Wave Front

Sunda Strait Shipping

Disaster – “Governor General

Loudon” – ship saved by

action of Captain Lindemann,,

“Berouw” – ran aground on

Sumatra - ship + crew total loss

Extinction-level Black Swan Events


Human Survival • "Humanity's survival does not depend on reducing differences to a common identity, but

on learning to live creatively with differences."—Anonymous

• Humans are a resilient species – but survival is not inevitable. If Earth does not attain Type III status in time, a number of the following scenarios could pose a severe challenge - the least problematic being an asteroid impact or global nuclear war. Humanity may survive and even recover from a significant asteroid or comet impact with Earth, regardless of whether or not governments are alert enough to take precautions which offer any significant survival rate. An event like this will not entirely wipe out human existence, only reduce population numbers and significantly set back technological advancement. Human evolution will not have to start over again. Civilization will still have the opportunity to build upon any remaining technology.

• There is a clear and present danger that a Global-level Extinction Event will one day also remove all life on Earth. Major Global-level Extinction Events could be caused by: -

1. Near-by Gamma-ray bursts from dying stars in distant Supernova events or Solar Flares - mass coronal ejections - from various Suns in our own local stellar group.

2. Plate Tectonics / Continental Drift – aggregation of Continental Landmass at either the Equator or the Poles (Rodinia, Gondwanaland, Pangea etc.).is associated with “Snowball Earth” “Global Dessert” and “Stagnant Sea” Extinction Events.

3. Massive Meteorite or Comet strikes on the planet surface – thought to have contributed to the Cretaceous-Tertiary Boundary Event.

4. Major Volcanic Events – the Siberian Traps and Deccan Traps were associated with the Permian-Triassic Boundary Event and the Cretaceous-Tertiary Boundary Event

5. Climate Change – a Global Ice Age was associated with many of the Precambrian Extinction Events.



1 Hyperspace

Event

Quantum

Dynamics

The Collapsing Universe—the Universe could collapse into an internal or

external void, spreading at the speed of light and swallowing everything in

its path. Possible scenarios might include our own universe (membrane)

colliding with another in hyperspace, or collapsing into a different dimension

set (our six-dimensional companion Universe) or into a super-massive Black

Hole - one large enough to destabilize the entire structure of the Cosmos.

Has a Collapsing Universe happened before – and could it happen again?

According to String Theory, our Universe began as a ten-dimensional

membrane – which collapsed into the familiar four-dimensional Space-time

Continuum of our own Universe – along with our companion universe which

contains the remaining set of six further dimensions, all curled up together.

Scenarios for this catastrophe might be found in the ripping and collapsing of

our four-dimensional Universe into another dimension-set (for example, the

six additional dimensions locked into our invisible companion universe) - or

collision with and absorption into, an external universe (another membrane).

Astrophysicists argue much about this Future Scenario and its part in the

Standard Model for the lifecycle and evolution of the Universe - especially in

relation to scenarios for possible conditions prior to the “Big Bang”.

Extinction-level Black Swan Events Type Force Extinction-level Black Swan Event

2 Singularity

Event

Quantum

Dynamics

The Killer Strangelet – a Particle accelerator accident – the Universe

could collapse into an artificially created void, spreading at the speed of light

and swallowing everything in its path. Commentators have speculated that

physicists could accidentally cause this void in a Particle accelerator

experiment which went disastrously wrong, inadvertently creating an

unstable particle – the Killer Strangelet – which quickly collapses into a

most unwelcome mini-black hole. This viewpoint is somewhat speculative –

many Physicists maintain there is little to substantiate this scenario, which is

based on little more than science-fiction - as it would require a lot more

energy to effect than we currently muster in particle experiments on earth.

3 Singularity

Event

Quantum

Dynamics

Black Hole suddenly appears in the Solar System – swallowing up the sun

and planets - thus causing the end of the Solar System as we know it.....

4 Orbital

Disruption

Event

Gravity

Wave

Rogue black holes—it is estimated there are about 10 million black holes in

the Milky Way alone. The real threat is not that one would swallow the Solar

System, but pass close by and disrupt Earth’s orbit just enough to throw it

out of orbit into deep space – to become a cold, lifeless wandering planet



4 Orbital

Disruption

Event

Gravity

Wave

Cosmic Wanderers - Colliding Galaxies— Andromeda, our nearest Galaxy,

is about 250 million light years away and is on a collision course with the Milky

Way. The real threat here is not that the Solar System would be swallowed up

by another Solar System - but that a rogue star could pass close by and disrupt

Earth’s orbit sufficiently to knock it out of orbit and into deep space – or even

hurl our own Solar System out of position towards the edge of the new Galaxy.

Rogue black holes — it is estimated there are about 10 million black holes in

the Milky Way alone. The real threat is not that one would swallow the Solar

System, but pass close by and disrupt Earth’s orbit just enough to throw it out

into deep space. Wandering Stars — it is also estimated there are about 10

million Wandering Stars in the Milky Way, which could also pass close by and

disrupt Earth’s orbit just enough to throw it out into deep space. This has

happened before – early in the Earth’s history a close encounter with a rogue

Wandering Star disrupted the proto planetary orbits – hurling the Gas Giant

Plants away from the Sun, creating the Earth / Moon System, the Kuyper Belt –

a rubble zone and source of meteors where another rocky planet should be

between Mars and Jupiter – and the Oort Cloud, an icy frozen rubble zone far

beyond the planetary orbits, now the main source of icy asteroids and comets.

5 Impact

Event

Gravitation

Attraction

Wandering Planets — it is further estimated there could be another 10 million

exo-planets - expelled from their parent Solar System and are now wandering

freely around our galaxy in deep space. This has happened before – early in

Earths history, proto-planets Earth and Thea collided to form the Earth / Moon.

Based on data from the Hubble Space Telescope, the Milky Way galaxy and Andromeda galaxy are predicted to distort each other with tidal pull in 3.75 billion years, as shown in this illustration.

Andromeda v. Milky Way

• Andromeda is approaching us at more than 250,000 miles per hour – but it will take 4 billion years before it strikes the Milky Way.

• Computer simulations derived from Hubble's data show that it will take an additional two billion years after the encounter for the interacting galaxies to completely merge under the tug of gravity and reshape into the form of a single elliptical galaxy similar to the kind more commonly seen locally in the universe.

• Although the galaxies will plough into each other, stars inside each galaxy are so far apart that they will not collide with other stars during the encounter. However, the stars will be thrown into different orbits around the new galactic centre. Simulations show that our solar system will probably be tossed much further out from the galactic core than it is today.

The Aftermath: Following the collision of the two galaxies, a countless number of stars will be sent spinning into space as

Andromeda and the Milky Way lose their previous forms



6 Impact

Event

Gravitational

Attraction

Asteroid or comet impact – the odds of an asteroid or comet impact on the

Earth depend on the size of the Object. An Object approximately 15 feet in

diameter hits the Earth once every several months; 35 feet every 10 years; 60

feet every 100 years; 200 feet, or size of the Tunguska impact, every 200 years;

350 feet every several thousand years; 1,000 feet every 50,000 years; six tenths

of a mile every 500,000 years; and 5 to 6 miles across every 100 million years.

Any comet or asteroid five miles or over in diameter striking the planet would be

catastrophic for life on Earth, creating an extreme Extinction-level Event (ELE).

During early Geological Time, during the Pre-Cambrian Epoch - the Hadean

Period ended with a Late Heavy Bombardment from space – the impact craters

may still be seen on the surface of the Moon and Mars.

Mass extinction due to Impact Events such as these take place once every 26

million years or so, and may have something to do with the Solar System’s orbit

around the Milky Way (every 250 my). Some Astrophysicists have suggested

that the orbit of the Solar System passes through the Galactic plane accretion

disc once every 125 my. Astrophysicists have speculated that the Kuyper belt,

between Mars and Jupiter, contains asteroids meteor-forming bodies – and the

Oort Cloud, containing Planetoids such as Pluto, thought to exist beyond the

orbit of Neptune may periodically be disturbed by Gravity Waves from nearby

Supernova events, or by close passage to objects from our own Local Stellar

Group when the Solar System reaches specific positions orbiting the Galaxy.



7 Radiation

Event

Gamma

Rays

Supernova Events and Gamma ray Bursts—are the most energetic events in

the universe – Supernova Events are as a result of the collapse of megastars

and Gamma ray bursts possibly a result of the collision and merger of two

collapsed stars. At 1,000 light years away, any Gamma ray burst would appear

as an intense flash, as bright as the Sun. The next thing that we would notice after

the initial bright burst, is the sky turning a beautiful shade of Violet – accompanied

by a dancing bright blue-green Aurora effect as the radiation from such a star

burst interacted with the atmosphere, creating nitrogen oxides that would begin to

consume the ozone layer. Sirius, the Dog Star in the constellation Canis Major is

a Red Giant located within our own local star cluster which one day experience a

Supernova event – and thus presents a real and present danger to life on Earth.

As a result, radiation from the Sun penetrating the atmosphere would eventually

destroy all life. Gamma ray bursts currently observed by astronomers are very

distant, implying rarity – about one per galaxy per hundred years. The next

candidate for a Gamma ray burst in our home galaxy, the Milky Way, is the Red

Giant Betelgeuse in Orion – which Astrophysicists believe will go Supernova

within 500 years. As Betelgeuse is only 500 light years away from us – this may

already have happened and the light from this event is still travelling towards us.

Betelgeuse has lost 10% of its radiance over the last decade – as it shrinks and

begins to collapse into its own core – before rebounding in a massive Supernova.



8 Coronal

Mass

Ejection

Event

Nuclear

Fusion

Giant solar flares—or coronal mass ejections. Within a few hours, a mega

super-flare from the Sun would fry Earth and disintegrate the ozone layer.

Many observers believe that such an event is unlikely, since there is no

direct evidence to suggest this has happened in the past 3.7 billion years.

Others have suggested that Giant solar flares might have been associated

with previous mass-extinction events – particularly the PTB Event.

9 Electro-

magnetic

Event

Magnetic

Force

Reversal of Earth’s magnetic field—has not happened for about 780,000

years. Without the Earths magnetic stability, particle storms and cosmic rays

from the Sun and energetic subatomic particles from deep space would

begin to erode and even strip off the atmosphere as a whole – not just the

ozone layer – as has happened in the past on Mars.

10 Biotech

Disease

Event

Viruses

and Germs

Biotech disaster—scientists continuously create new species through

genetic engineering. Such tampering could backfire and have an adverse

effect on unintended consequences to other species. The misuse of

biotechnology, such as a terrorist groups creating and releasing airborne

virulent strains of Anthrax, Bubonic Plague, Ebola, Flue or HIV – to which

the Human population has no natural resistance - could kill off everyone on

the earth



11 Alien

Contact

Event

Biological

Predation

Invasion and Conquest — not likely, but anything is possible given enough time

and unimaginable motives. An advanced alien civilization might view humanity as

hostile, or as a technological quantum accident waiting to happen on a universal

scale. Perhaps we have something that they want or need.

“Kill Moment” – Invasion, conquest and genocide by a civilisation with superior

technology, e.g. Roman conquest of Celtic Tribes in Western Europe, William the

Conquerors’ “Harrying of the North” in England, Spanish conquistadores meet

Aztecs and Amazonian Indians in Central and South America, Cowboys v.

Indians across the plains of North America – are just a few past examples.

12 Alien

Contact

Event

Biological

Disease

Global Pandemic— If the balance of people coexisting with viruses and germs

gets out of control on a massive scale, contagious diseases could kill off

humanity. Contact with a foreign civilization or alien population and their bio-

cloud - carrying parasites and contagious diseases, leading to pandemics to

which the human population being exposed has developed little or no immunity.

“Ill Moment” – Examples include the Bubonic Plague - Black Death - arriving in

Europe from Asia, Spanish Explorers sailing up the River Amazon and spreading

Smallpox to Amazonian Basin Indians from the Dark Earth - Terra Prate - Culture

and Columbian Sailors returning to Europe introducing Syphilis from the New

World. The worst disease episode in history was the Spanish Flu Pandemic -

carried home by returning soldiers at the end of the Great War – this virus killed

more people than died in all the military action during the whole of WWI.



13 Global

Massive

Change

Event

Human

Impact

on Eco-

system

Ecosystem collapse—Global Massive Change. Certain species (insect

pollinators and insect pollinated plants) could die off under environmental

change pressure and so have a profound impact on humanity as all life on the

planet is connected in a living ecosystem.

Society’s growth-associated impacts on its own ecological and environmental

support systems, for example intensive agriculture causing exhaustion of natural

resources by the Mayan and Khmer cultures, de-forestation and over-grazing

causing catastrophic ecological damage and resulting in climatic change – for

example, the Easter Island culture, the de-population of upland moors and

highlands in Britain from the Iron Age onwards – including the Iron Age retreat

from northern and southern English uplands, the Scottish Highland Clearances

and replacement of subsistence crofting by deer and grouse for hunting and

sheep for wool on major Scottish Highland Estates and the current sub-Saharan

de-forestation and subsequent desertification by semi-nomadic pastoralists

14 Global

Massive

Change

Event

Human

Impact

on Eco-

system

FEW - Food, Energy, Water Crisis - as scarcity of Natural Resources (FEW -

Food, Energy, Water) and increased competition to obtain those scarce

resources begins to limit and then reverse population growth, global population

levels will continue expansion towards an estimated 8 or 9 billion human beings

by the middle of this century – then collapse catastrophically to below 1 billion –

slowly recovering and stabilising out again at a sustainable population of about 1

billion human beings by the end of this century.



15 Global

Massive

Change

Event

Human

Impact on

Eco-

system

Environmental toxins — Society’s growth-associated impacts on its own

ecological and environmental support systems, for example, intensive

industry and agriculture causing the exhaustion and pollution of all natural

resources. Shale Gas Fracking chemicals, industrial and agronomy pollutants

and pesticides, along with various bio-toxins could spell the end for humanity

if any of them were to escape out of control and spread into the Eco-system.

16 Tech

Disaster

Event

Robotics Nanotechnology disaster— autonomous nanotechnology De-construction

micro-robots could escape from their confines after an industrial accident and

spread throughout the Earths’ biosphere, reducing the Eco-system to waste...

17 Tech

Disaster

Event

Robotics Rise of the Machines - Robots take over— autonomous smart robots might

rebel, take over the world and end mankind – either under their own volition,

or through manipulation under remote control by dark external forces.....

18 Global

Warfare

Human

Impact on

Eco-

system

Weapons of Mass Destruction — misuse of biological, chemical or nuclear

weapons is an obvious threat to the future. Ethnic-targeted bio-engineered

weapons devised by terrorists could also wipe out an entire race, population

or nation. Invasion, conquest and genocide by a foreign / alien civilisation

with vastly superior technology, e.g. Roman conquest of Celtic Tribes in

Western Europe, William the Conquerors’ “Harrying of the North” in England,

Spanish conquistadores meet Aztecs and Amazonian Indians in Central and

South America, Cowboys v. Indians across the plains of North America…..

Extinction-level Black Swan Events Type Force Black Swan Event

19 Act of

God

Mass-

delusion

Mass insanity, mass hallucination, mass hysteria and mass hypnosis — as

world-wide physical health improves – so mental health is rapidly declining. 500

million people around the world supposedly suffer from some behavioural,

sociopathic or psychological disorder. By 2040, suicide triggered by manic

depression could be a leading cause of death. The real culprit in all of this could

be the recreational and clinical psychotropic drugs and other mind-bending

agents that we are currently being administered or exposed to. In the face of a

pending worldwide disaster or Extinction-level Event– real or imaginary - mass

insanity, mass hallucination, mass hysteria or mass hypnosis might take

over and the Human population ends itself in a global mass suicide event.....

20 Act of

God

Armageddon Divine intervention— not necessarily by a deity, however. During first contact

with any highly advanced Alien Civilisation, it might initially be misconstrued as a

religious experience – the return of the Messiah or the arrival of the anti-Christ –

and could be a catalyst for the end of the world struggle. In the confusion during

first contact - Religious fanatics belonging to doomsday cults out to persecute or

punish “non-believers” could easily find reason and methods to develop ways

and means to destroy humanity – or be exterminated by Aliens in an uprising.....

21 Act of

God

Creation Someone wakes up and realises it was all just a dream (or only a computer

simulation)—our own reality, or the local four-dimensional version of our own

reality - may not be the most stable form of existence. We might not exist at all -

we could all only be Avatars populating a virtual reality program running within a

computer that is the last thing left in a predominately dying or dead Universe.....


Throughout eternity, all that is of like form comes around again – everything that is the same must return in its own everlasting

cycle.....


Many Economists and Economic Planners have arrived at the same conclusion - that most organisations have not yet widely adopted

sophisticated Business Intelligence and Analytics systems – let alone integrated BI / Analytics and “Big Data” outputs into their core Strategic

Planning and Financial Management processes.....


• Abiliti: is part of a global consortium of Digital Technologies Service Providers / Future Management Strategy Consulting firms for Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / 4G WiFi / Big Data / Social Media / e-Gov Services

• Graham Harris Founder and MD @ Abiliti: Future Systems – Email: (Office)

– Telephone: (Mobile)

• Nigel Tebbutt 奈杰尔泰巴德

– Disruptive Futurist / Data Scientist @ Abiliti: Future Systems – Telephone: +44 (0) 7832 182595 (Mobile)

– +44 (0) 121 445 5689 (Office)

– Email: [email protected] (Private)

Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©


