Complex Dynamics of Urban Systems – Some Reflections

David BattenIIASA, IFS, Temaplan Group &

CSIRODavid.Batten@csiro.au

Summary

IIASA’s comparative work in the eightiesNested Dynamics of Metropolitan Processes and Policies

Cities – planned or self-organizing systems?“Booster” theories of selective urban growthLarge ABMs – e.g. TRANSIMS (Albuquerque), EPISIMS

The new driversGlobal markets – Space versus place, land, water, ecosystems Climate change – GHG emissions, warming, sea risePeak oil – Low emissions transport, new ways of interacting?

Where to next and with what toolkit?Nonlinear human/climate/ecosystems interface

CSS Working Groups and Interaction TasksCABM/HEMA, CDUS, integrated mega-models

Adaptive capacity of Australian cities (Climate Adaptation Flagship)Fragility of critical infrastructures (with IIASA again)

Nested Dynamics of Metropolitan Processes and Policies (IIASA)

Initiated in 1982Aims:

To enhance our primitive understanding of interacting metropolitan change processes which are operating at very different speeds (“slow and fast” dynamics)To develop new concepts and tools that could probe beyond familiar lifecycle theories of urbanization, suburbanization and de-urbanization

Approach:Systematic comparison of changes and simultaneous interactions between 5 metropolitan subsystems in about 20 major cities:

PopulationHousingTransportation and infrastructureEconomy and workplacesInstitutional management

Key Subsystems and Interactions

PopulationTransportServices

Dwellings

WorkplacesProduction

System

TransportSystem

HousingSystem

Changes in housingcapacity &location

Changesin transportcapacity &location

Changes inproductioncapacity &location

Rate ofemployment (-)

Vehicle density (+)

Householdsize (-)

SUPPLY SYSTEM

(STOCKS)

CAPACITYCHANGES

INTERMEDIATE DEMAND

TYPICALLINKAGE

PARAMETERS

FINAL DEMAND

Capacity Tensions

Tension signals arise when a state of excess demand or excess supply grows larger, owing to inconsistent directions or speeds of change of the supply and demand components.

e.g. Letting yD denote demand for and xD supply of dwellings at time t, we can formalize the definition of a capacity tension as a state in which:

dxD/dt > dyD/dt when xD > yD

ordxD/dt < dyD/dt when xD < yD

In the eighties, most urban management decisions were seen as necessary responses or adjustments to signals of imbalances and capacity tensions in the urban system.

However, such signals can be misleading if the underlying dynamics are not well understood.

Planned or Self-Organized?

For much of the twentieth century, cities were thought to be the result of premeditated planning aloneSome urban scientists believed that their geographical location and design could even be optimized Views on urban evolution changed in the 80s and 90s:

“Booster” theories – feedback loops (William Cronon)Self-organizing human settlements (Peter Allen)

Cities may behave more like human brainsSelf-maintaining and self-sustainingSelf-repairing

New set of drivers have emerged

“Booster” Theories of Urban Growth

IncreasingReturns to Scale& Agglomeration

GreaterSpecialization

SelectiveGrowth of

Settlements

Migrationand Trade

GROWINGCIRCULATION OF

GOODS AND PEOPLE(POSITIVE FEEDBACK LOOP)

Climate, the natural

environmentand otherattractors

New Drivers of Urban Dynamics?

Global Markets (How and where we produce)Space versus place?Resource scarcities – e.g. water, energy (see below)Land degradationThreatened ecosystems

Climate Change (How and where we live/consume)

GHG emissions and air pollutionGlobal warmingSea rise

Peak Oil (How we interact)Low emissions transport?New ways of moving and interacting?

Where Next and What Toolkit?

Human/Climate/Ecosystems InterfaceCSIRO-CCSS Working Groups and Interaction Tasks

ABM WG (David Batten) + HEMA network (Pascal Perez)e.g. NEMSIM, Rangelands model, Barrier Reef model et al

Complex Dynamics of Urban Systems IT

Mega-models – e.g. TRANSIMS, EPISIMS, EPICAST

Integrating social processes in climate & earth system models (John Finnigan) – possibly involving ABM

Adaptive Capacity of CitiesClimate Adaptation Flagship (Liveable cities, coasts & regions)Audit of adaptive capacity of Australian cities and towns?

Fragility of Critical InfrastructuresIIASA (http://www.iiasa.ac.at/Research/FCI/index.html?sb=8)

Climate Adaptation FlagshipTheme 2: Liveable cities, coasts and regionsOur urban and coastal populations are exposed to climate change through:

declining water availabilityincreasing extreme weather eventssea level rise.

The four focus areas of this Theme of Flagship research are:

new building and infrastructure design, and adaptation of built infrastructure at building, development and urban system scalesinfrastructure planning at larger scales (cities, coastal development) that takes into account policies, codes, regulation, and demands for emergency servicesintegration of social, economic and environmental analyses to help communities, industry and governments adapt to the impacts of climate change at regional scaleshuman health and diseases, extreme temperatures and spatial shifts in vector-borne diseases.

Some Useful References

Michael Batty (2005): Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models and Fractals, MIT Press.Juval Portugali (2000): Self-Organization and the City, Springer Series in Synergetics.David Batten (2000): Discovering Artificial Economics: How Agents Learn and Economies Evolve, Westview Press.Pascal Perez and David Batten (2006): Complex Science for a Complex World: Exploring Human Ecosystems with Agents, ANU ePress.

I am currently reviewing

NEMSIM = National Electricity Market Simulator

Goal: To evolve “would-be” worlds of new agents, new micro-grids and new rules

Simulation is changing the frontiers of science

We can explore “What-if” scenarios of really complex systems

Like cities, our National Electricity Market (NEM) is a Complex Adaptive System

Our NEM as a Complex Adaptive System

Market ofAdaptiveAgents

PhysicalEnergy

Network

Socio-Technical System

ClimateScenarios

GHGEmissionsCalculator

Natural System

Stationaryenergy

accountsfor about 60%

of all GHG emissions

Changes in climate andweather forecasts,

contribute to price volatility

and demanduncertaintyin the NEM

What kind of Simulator is it?

Agent-based simulation (or MAS)

NEM participants are the software agents

Agents’ behaviours programmed via rules

Action evolves in 3 simulated environments

Collective outcomes (and surprises) emerge from the bottom up.

Examples are price volatility, market power, network congestion, regional blackouts and excessive GHG emissions.

Smart Generator Agent: Re-bidding

2 4 6 8 10

2

4

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10

X Axis Title

Y A

xis

Titl

e300.0

350.0

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600.0

650.0

700.0

Tuesday, 24/06/2003

4:00 8:00 12:00 16:00 20:00 4:00 (48 trading intervals)00:00

Generating Unit (Thermal – coal)

MW

Re-bid stack submitted at 22:00 on the previous day

Ten

pri

ce b

ands

0

8.61

18.20

36.00

82.00

136.00

252.00

2200.00

6300.00

9126.00

P1

P2

P3

P4

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P7

P8

P9

P10

$/MWh

Capacity Withholding

-2000

0

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2000 2500 3000 3500

04.30 18.00 14.00 22.00 09.30

Quantity Offered (MW)

Price($/MWh)

This 09.30 band was shifted down three times in the

morning via rebids

Evening peak

An Overview of NEMSIM

Typical Graphical Output

Regional Summary Window for GHG Emissions

Thank you

David BattenCoordinator, CSIRO Agent-Based Modelling Working

GroupCSIRO Marine & Atmospheric Research

David.Batten@csiro.au