Spatial Dynamical Modelling with TerraME Lectures 4: Agentbased modelling

1
Spatial Dynamical Modelling with TerraME
Lectures 4 Agent-based modelling

• Gilberto Câmara

2
Agent-based modelling with TerraME
3
What are complex adaptive systems?
4
Agent
An agent is any actor within an environment, any
entity that can affect itself, the environment
and other agents.

• Agent flexible, interacting and autonomous

5
Agents autonomy, flexibility, interaction
Synchronization of fireflies
6
Agents autonomy, flexibility, interaction
football players
7
Agent-Based Modelling
Goal
Gilbert, 2003
8
Agents are

• Identifiable and self-contained
• Goal-oriented
• Does not simply act in response to the
  environment
• Situated
• Living in an environment with which interacts
  with other agents
• Communicative/Socially aware
• Communicates with other agents
• Autonomous
• Exercises control over its own actions

9
Bird Flocking

• No central authority Each bird reacts to its
  neighbor
• Bottom-up not possible to model the flock in a
  global manner. It is necessary to simulate the
  INTERACTION between the individuals

10
Bird Flocking Reynolds Model (1987)
Cohesion steer to move toward the average
position of local flockmates Separation steer
to avoid crowding local flockmates Alignment
steer towards the average heading of local
flockmates
www.red3d.com/cwr/boids/
11
Agents changing the landscape
12
Characteristics of CA models (1)

• Self-organising systems with emergent
  properties locally defined rules resulting in
  macroscopic ordered structures. Massive amounts
  of individual actions result in the spatial
  structures that we know and recognise

13
Characteristics of CA models (1)

• Wolfram (1984) 4 classes of states
• (1) homogeneous or single equilibrium
• (2) periodic states
• (3) chaotic states
• (4) edge-of-chaos localised structures, with
  organized complexity.

14
Bird Flocking

• Reynolds Model (1987)
• http//ccl.northwestern.edu/netlogo/models/Flockin
  g
• Animation example

15
Swarm
16
Repast
17
Netlogo
18
Netlogo
19
TerraME
20
Development of Agent-based models in TerraME
21
Emergence
Can you grow it? (Epstein Axtell 1996)
source (Bonabeau, 2002)
22
Epstein (Generative Social Science)

• If you didnt grow it, you didnt explain its
  generation
• Agent-based model ? Generate a macro-structure
• Agents properties of each agent rules of
  interaction
• Target macrostruture M that represents a
  plausible pattern in the real-world

23
Scientific method
Science proceeds by conjectures and refutations
(Popper)
24
Explanation and Generative Sufficiency
Conjectures
Agent model A1
Macrostructure
?
Agent model A2
Spatial segregation Bird flocking
Refutation
?
Agent model A3
25
Explanation and Generative Sufficiency
Agent model A1
Macrostructure
?
Agent model A2
Occams razor "entia non sunt multiplicanda
praeter necessitatem", or "entities should not
be multiplied beyond necessity".
26
Explanation and Generative Sufficiency
Agent model A1
Macrostructure
?
Agent model A2
Poppers view "We prefer simpler theories to more
complex ones because their empirical content is
greater and because they are better testable"
27
Explanation and Generative Sufficiency
Agent model A1
Macrostructure
?
Agent model A2
Einsteins rule The supreme goal of all theory
is to make the irreducible basic elements as
simple and as few as possible without having to
surrender the adequate representation of a single
datum of experience" "Theories should be as
simple as possible, but no simpler.
28
TerraME extension for agent-based modelling
ForEachAgent function(agents, func,
event) nagents table.getn(agents) for i 1,
nagents do func (agentsi,(event)) end end Re
plicate function(agent, nagents) ag for
i 1, nagents do agi agent() agi.id
i end return ag end (contained in file
agent.lua)
29
ABM example
Urban Dynamics in Latin American cities an
agent-based simulation approach Joana Barros
30
Latin American cities

• High speed of urban growth (urbanization)
• Poverty spontaneous settlements
• Poor control of policies upon the development
  process
• Spatial result fragmented set of patches, with
  different morphological patterns often
  disconnected from each other that mutate and
  evolve in time.

31
Peripherization
Process in which the city grows by the addition
of low-income residential areas in the peripheral
ring. These areas are slowly incorporated to
the city by spatial expansion, occupied by a
higher economic group while new low-income
settlements keep emerging on the periphery..
São Paulo - Brasil
Caracas - Venezuela
32
Urban growth
Peripherization in Latin America (Brazil)
Urban sprawl in United States
Urban sprawlin Europe (UK)
33
Research question

• How does this process happen in space and time?
• How space is shaped by individual decisions? ?
  Complexity approach
• Time Space ? automata model
• Social issues ? agent-based simulation)

34
The Peripherisation Model

• Four modules
• Peripherisation module
• Spontaneous settlements module
• Inner city processes module
• Spatial constraints module

35
Peripherization moduls

• reproduces the process of expulsion and expansion
  by simulating the residential locational
  processes of 3 distinct economic groups.
• assumes that despite the economic differences all
  agents have the same locational preferences. They
  all want to locate close to the best areas in the
  city which in Latin America means to be close to
  high-income areas
• all agents have the same preferences but
  different restrictions

36
Peripherization module rules

• 1. proportion of agents per group is defined as a
  parameter
• 2. high-income agent can locate anywhere
• 3. medium-income agent can locate anywhere
  except on high-income places
• 4. low-income agent can locate only in the
  vacant space
• 5. agents can occupy another agents cell then
  the latter is evicted and must find another

37
Peripherization module rules
38
Peripherization module rules
Spatial pattern the rules do not suggests that
the spatial outcome of the model would be a
segregated pattern Approximates the spatial
structure found in the residential locational
pattern of Latin American cities multiple
initial seeds -resembles certain characteristics
of metropolitan areas
39
Comparison with reality

• Maps of income distribution for São Paulo, Brazil
  (census 2000)
• Maps A and B quantile breaks (3 and 6 ranges)
• Maps C and D natural breaks (3 and 6 ranges)
• No definition of economic groups or social classes

40
TerraME extension for agent-based modelling
ForEachAgent function(agents, func,
event) nagents table.getn(agents) for i 1,
nagents do func (agentsi,(event)) end end Re
plicate function(agent, nagents) ag for
i 1, nagents do agi agent() agi.id
i end return ag end (contained in file
agent.lua)