Complex Dynamics of Urban Systems

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Complex Dynamics of Urban Systems Some
Reflections
David Batten
IIASA, IFS, Temaplan Group CSIRO
David.Batten_at_csiro.au
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Summary

• IIASAs comparative work in the eighties
• Nested Dynamics of Metropolitan Processes and
  Policies
• Cities planned or self-organizing systems?
• Booster theories of selective urban growth
• Large ABMs e.g. TRANSIMS (Albuquerque), EPISIMS
• The new drivers
• Global markets Space versus place, land, water,
  ecosystems
• Climate change GHG emissions, warming, sea rise
• Peak 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 Tasks
• CABM/HEMA, CDUS, integrated mega-models
• Adaptive capacity of Australian cities (Climate
  Adaptation Flagship)
• Fragility of critical infrastructures (with IIASA
  again)

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Nested Dynamics of Metropolitan Processes and
Policies (IIASA)

• Initiated in 1982
• Aims
• 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
• Population
• Housing
• Transportation and infrastructure
• Economy and workplaces
• Institutional management

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Key Subsystems and Interactions
TYPICAL LINKAGE PARAMETERS
SUPPLY SYSTEM (STOCKS)
CAPACITY CHANGES
INTERMEDIATE DEMAND
FINAL DEMAND
Changes in housing capacity location
Dwellings
Housing System
Household size (-)
Population
Changes in transport capacity location
Transport Services
Transport System
Vehicle density ()
Changes in production capacity location
Rate of employment (-)
Workplaces
Production System
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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 gt dyD/dt when xD gt yD
• or
• dxD/dt lt dyD/dt when xD lt 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.

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Planned or Self-Organized?

• For much of the twentieth century, cities were
  thought to be the result of premeditated planning
  alone
• Some 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 brains
• Self-maintaining and self-sustaining
• Self-repairing
• New set of drivers have emerged

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Booster Theories of Urban Growth
Climate, the natural environment and
other attractors
Greater Specialization
Migration and Trade
GROWING CIRCULATION OF GOODS AND
PEOPLE (POSITIVE FEEDBACK LOOP)
Increasing Returns to Scale Agglomeration
Selective Growth of Settlements
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New Drivers of Urban Dynamics?

• Global Markets (How and where we produce)
• Space versus place?
• Resource scarcities e.g. water, energy (see
  below)
• Land degradation
• Threatened ecosystems
• Climate Change (How and where we live/consume)
• GHG emissions and air pollution
• Global warming
• Sea rise
• Peak Oil (How we interact)
• Low emissions transport?
• New ways of moving and interacting?

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Where Next and What Toolkit?

• Human/Climate/Ecosystems Interface
• CSIRO-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 Cities
• Climate Adaptation Flagship (Liveable cities,
  coasts regions)
• Audit of adaptive capacity of Australian cities
  and towns?
• Fragility of Critical Infrastructures
• IIASA (http//www.iiasa.ac.at/Research/FCI/index.h
  tml?sb8)

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Climate Adaptation Flagship

• Theme 2 Liveable cities, coasts and regions
• Our urban and coastal populations are exposed to
  climate change through
• declining water availability
• increasing extreme weather events
• sea 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 scales
• infrastructure planning at larger scales (cities,
  coastal development) that takes into account
  policies, codes, regulation, and demands for
  emergency services
• integration of social, economic and environmental
  analyses to help communities, industry and
  governments adapt to the impacts of climate
  change at regional scales
• human health and diseases, extreme temperatures
  and spatial shifts in vector-borne diseases.

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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.

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I am currently reviewing
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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

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Our NEM as a Complex Adaptive System
Changes in climate and weather forecasts, contribu
te to price volatility and demand uncertainty in
the NEM
Market of Adaptive Agents
Climate Scenarios
Stationary energy accounts for about 60 of all
GHG emissions
GHG Emissions Calculator
Physical Energy Network
Natural System
Socio-Technical System
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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.

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Smart Generator Agent Re-bidding
Tuesday, 24/06/2003
/MWh
Ten price bands
MW
Generating Unit (Thermal coal)
Re-bid stack submitted at 2200 on the previous
day
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Capacity Withholding
Price (/MWh)
Evening peak
04.30
18.00
14.00
22.00
09.30
This 09.30 band was shifted down three times in
the morning via rebids
Quantity Offered (MW)
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An Overview of NEMSIM
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Typical Graphical Output
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Regional Summary Window for GHG
Emissions
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Thank you
David Batten Coordinator, CSIRO
Agent-Based Modelling Working Group CSIRO Marine
Atmospheric Research
David.Batten_at_csiro.au