MODELLING AND SIMULATION APPROACHES FOR TECHNO-SOCIETAL

PHENOMENA

Yannis Charalabidis

Aggeliki Androutsopoulou

University of the Aegean

“We lack systematic evidence as to whether social science studies of policy interventions, and steps to increase their relevance to and use for policy making, are having the results

their sponsors hope for. . . .”

[Knowledge and Policy: The Uncertain Connection, 1978,

National Research Council, U.S.]

Financial Crisis Poverty Unemployment System risk Sustainability

Climate change Energy sufficiency Shortage of natural resources Crisis Management (Floods,

Earthquakes) Pollution

Urbanization Pandemics (virus, physical

diseases) Ageing population Migration patterns Cities Crime Terrorism

GLOBAL SOCIETAL CHALLENGES

Social

EnvironmentalEconomic

GLOBAL SYSTEM SCIENCE

① identify problems and challenges for societies

② measure their magnitude and seriousness

③ review alternative policy interventions

④ systematically assess the consequences of policy

interventions

⑤ evaluate the policy results, using factual information

MODELLING OBJECTIVES

• Reduce the uncertainty of complex heterogeneous techno-

societal phenomena

• Prediction of social indicators, demographics, assessment

of socio-economic impacts

• Formalisation of processes/mechanisms/behaviours

• Collect evidence for effective transparent decision making

• Provision of a holistic view of our connected world

MODELLING APPROACHES

High

Abstraction

Less Details

Macro Level

Middle

Abstraction

Medium Details

Meso Level

Low Abstraction

More Details

Micro Level Individual objects, exact sizes, distances, velocities, timings, …

Agent

Based

Active objects

Individual

behavior rules

Direct or indirect

interaction

Environment

models

Discrete

Event

Entities (passive

objects)

Flowcharts and/or

transport networks

Resources

Aggregates, Global Casual Dependencies, Feedback Dynamics, …

System Dynamics

Levels (aggregates)

Stock-and-Flow Diagrams

Feedback loops

Dynamic System

• Physical state variables

• Block diagrams and/or algebraic-

differential equations

ContinuousDiscrete

System-

Level

Process-

Centric Individual-

Centric

EXAMPLE: THE ICT DIFFUSSION MODEL(modell ing and prediction of cit izens

acquiring broadband access)

broadband

coverage projects

broadband

coveragecoverage

rate

broadband

users

potentialinternet

users

broadband

take up rate

TECHNOLOGY

FACTOR

cost of

access

internetaccess at

homeaccess rate

internet

usersusage rate

digitally

skilled

peopleliteracy rate

need to usephysical

disabilities

perception of

benefits

fear about

safety

LITERACY

FACTORtraining

e-awareness

noninternet

users

potential

coverage

digitally

illiterate

peoplepotentialinternetaccess

access pointsCOVERAGE

CAGR availability of

access points

Private

investment

non

broadband

usersnon bb(pstn)

takeup rate

<Time>

EXAMPLE: FORECASTING PARTICIPATION IN ONLINE POLICY DELIBERATION

MODELLING CHALLENGES

• Interventions in one area often have implications

on other domains through subtle, and hidden

connectivity

• Hard to construct models of adequate depth to

cover complex interlinked societal challenges

• Hard to capture behavioral aspects and can have

unprecedented effects

A PROPOSITION ON A HOLISTIC MODELLING APPROACH

• A Hybrid Collaborative Modelling approach supported by a

web platform for:

• Reuse and combination of different interoperable models and model

types

• Effective data handling and integration of multiple sources

• Capturing and processing of societal factors, together with widely

adopted socio-economic indicators

• Supporting advanced modelling techniques state of the art dynamic

simulation approaches

• Combination with social behavior factors, social participation and

crowdsourcing initiatives

ARCHITECTURE OF A HYBRID MODELLING AND SIMULATION PLATFORM

PLATFORM FUNCTIONALITIES

• Storage and retrieval of different types of interoperable models

• Easy to use graphical interface for the design of simulation models by

non modelling experts

• Online workspace for asynchronous collaboration or during Group

Model Building sessions

• Import and Export capabilities of models in machine readable formats

(e.g. XMILE)

• Visualisation and export of simulation results e.g. graphs or

standardised result files that can be processed by third party tools.

• API exposure to third party applications

PLATFORM PRINCIPLES

• Usability

• Scalability

• Adaptability

• Interoperability (with existing systems and

infrastructures)

• Sustainability

• Platform independence

• Maintenance of the system (modifications and

upgrades)

KEY ISSUES – THE PROCESS

• Study phenomena

• Define Models (find the main qualities and indicators)

• Describe Models in machine-understandable format

• Combine and reuse models and algorithms

• Find Enough Data for model instantiation

• Provide computation power to run and maintain

• Simulate

• Produce analytics

• Forecast

• Create narratives

CONCLUSIONS

• There is a need for (big) data-based social phenomena modelling, towards understanding and

forecasting behavior

• Several methods and tools of modelling and simulation exist, not interoperable to each other and

rather “passive”

• New platforms for modelling and simulation need to

• support knowledge bases with models for different phenomena,

• combine macro-micro simulation

• allow for interoperable combination of different models

• cater for the dynamic input of citizens (probing)

• If we succeed in modelling, we might start prognosing social phenomena or even find the right

solutions to societal challenges

Yannis CharalabidisAssociate Professor

Head, In format ion Systems LaboratoryDepar tment of In format ion

and Communicat ion Systems EngineeringUnivers i ty of The Aegean

yannisx@aegean.gr www.charalabid is .gr

@yannisc