Agent-based models and
social simulation
Gilberto Câmara
Tiago Carneiro
Pedro Andrade
Licence: Creative Commons By Attribution Non Commercial Share Alike
http://creativecommons.org/licenses/by-nc-sa/2.5/
Where does this image come from?
Where does this image come from?
Map of the web (Barabasi)
(could be brain connections)
Information flows in Nature
Ant colonies live in a chemical world
Conections and flows are universal
Yeast proteins
(Barabasi and Boneabau,
SciAm, 2003)
Scientists in Silicon Valley
(Fleming and Marx, Calif Mngt
Rew, 2006)
Information flows in the brain
Neurons transmit electrical information, which generate conscience
and emotions
Information flows generate cooperation
Foto: National Cancer Institute, EUA
http://visualsonline.cancer.gov/
White cells attact a cancer cell (cooperative activity)
Information flows in planet Earth
Mass and energy transfer between points in the planet
Complex adaptative systems
How come that an ecosystem
with all its diverse species
functions and exhibits patterns of
regularity?
How come that a city with many
inhabitants functions and
exhibits patterns of regularity?
What are complex adaptive systems?
Systems composed of many interacting parts that
evolve and adapt over time.
Organized behavior emerges from the simultaneous
interactions of parts without any global plan.
What are complex adaptive systems?
Universal Computing
Computing studies information
flows in natural systems...
...and how to represent and
work with information flows in
artificial systems
Agents as basis for complex systems
An agent is any actor within an environment, any entity
that can affect itself, the environment and other agents.
Agent: flexible, interacting and autonomous
Agent-Based Modelling
Representations
Goal
Communication
Communication
Action
Perception
Environment
Gilbert, 2003
Agents: autonomy, flexibility, interaction
Synchronization of fireflies
Why is it interesting?



Structure

structure is emergent from agent interaction

this can be directly modeled
Agency

agents have goals, beliefs and act

this can be directly modeled
Dynamics

things change, develop, evolve

agents move (in space and social location) and learn

these can be directly modeled
Source: (Gilbert, 2006)
Is it qualitative or quantitative?
Agent-based models can handle all types of data



quantitative attributes

age

size of organization
qualitative

ordinal or categorical (e.g. ethnicity),

relational (e.g. I am linked to him and her)
vague

A sends B a message about one time in three
Source: (Gilbert, 2006)
It has been used in different areas of science

economy

sociology

archaeology

ecology

linguistics

political sciences

...
Source: http://www.leggmason.com/thoughtleaderforum/2004/conference/transcripts/arthur_trans.asp
Agents changing the landscape
An individual, household, or institution that takes specific actions
according to its own decision rules which drive land-cover change.
Four types of agents
Artificial agents, artificial environment
Artificial agents, natural environment
Natural agents, artificial environment
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
Four types of agents
e-science
Artificial agents, artificial environment
Behavioral
Experiments
Natural agents, artificial environment
Engineering Applications
Artificial agents, natural environment
Descriptive Model
Natural Agents, natural environment
fonte: Helen Couclelis (UCSB)
Is computer science universal?
Modelling information flows in nature is computer science
http://www.red3d.com/cwr/boids/
Bird Flocking (Reynolds)
Example of a computational model
1. No central autority
2. Each bird reacts to its neighbor
3. Model based on bottom up
interactions
http://www.red3d.com/cwr/boids/
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/
Agents moving
Agents moving
Agents moving
Schelling segregation
model
Segregation
Some studies show that most people prefer
to live in a non-segregated society.
Why there is so much segregation?
Segregation
Segregation is an outcome of individual choices
But high levels of segregation indicate mean that
people are prejudiced?
Schelling’s Model of Segregation
Micro-level rules of the game
Stay if at least a
third of neighbors
are “kin”
< 1/3
Move to random
location otherwise
Schelling’s Model of Segregation
Schelling (1971) demonstrates a theory to explain
the persistence of racial segregation in an
environment of growing tolerance
If individuals will tolerate racial diversity, but will
not tolerate being in a minority in their locality,
segregation will still be the equilibrium situation
Schelling Model for Segregation
Start with a CA with “white” and “black” cells (random)
The new cell state is the state of the majority of the
cell’s Moore neighbours
White cells change to black if there are X or more black
neighbours
Black cells change to white if there are X or more white
neighbours
How long will it take for a stable state to occur?
Schelling’s Model of Segregation
Tolerance values above 30%: formation of ghettos
ABM in TerraME:
Types and Functions
TerraME: nature-society modelling
Agent
Agent
Space
Space
Nature represented in cellular spaces, society represented as agents
T. Carneiro, P. Andrade, et al., “An extensible toolbox for modeling nature-society
interactions”. Enviromental Modelling and Software, 2013 (Two PhDs).
Types in TerraLib ecosystem: new tools, new types
2014
Time Series
Trajectory
Cellular Space
2010
Agent
Event
Social Network
Object
2002
Geometry
Coverage
forEachRelative
forEachCell
Agent
forEachNeighbor
Cell
forEachAgent
forEachAgent
forEachCell
Society
CellularSpace
Group
Trajectory
DBMS
Agents within cells
agents = cell:getAgents()
if #(agents) == 0 then -- empty
agent:leave(oldcell)
agent:enter(cell)
end
Society
CBB
CAC
CBA
CCB
ACA
CCC
AAC
BBC
ABC
AAA
上海宋
BBA
BAB
Society
createAgent = function(capital)
return Agent {
capital = capital,
-- ...
}
end
data = {}
data[1] = 100; data[2] = 50; data[3] = 25
mag = Society(createAgent, data)
capital = 100
capital = 50
capital = 25
mag = Society(createAgent, 50)
Society
function createAgent (capital)
person = Agent {
init = function (self),
-- ...
}
end
data = {}
data[1] = 100; data[2] = 50; data[3] = 25
mag = Society(createAgent, data)
capital = 100
capital = 50
mag = Society(createAgent, 50)
capital = 25
Group
CBB
CAC
CBA
CCB
ACA
CCC
AAC
BBC
AAA
BBA
ABC
BAB
Group
g = Group{mag,
function(agent)
return agent. capital > 40
end,
function(a1, a2)
return a1.capital > a2.capital
end
}
capital = 100
capital = 50
capital = 25
Traversing the Society
capital = 100
capital = 50
capital = 25
forEachAgent(mag, function(agent)
agent.capital = agent.capital + 100
end)
capital = 200
capital = 150
capital = 125
Emergence
“Can you grow it?” (Epstein; Axtell; 1996)
source: (Bonabeau, 2002)
Epstein (Generative Social Science)

If you didn´t grow it, you didn´t explain its
generation

Agent-based model  Generate a macrostructure

Agents = properties of each agent + rules of
interaction

Target = macrostruture M that represents a
plausible pattern in the real-world
Scientific method
Science proceeds by conjectures and refutations (Popper)
Explanation and Generative Sufficiency
Conjectures
Agent model
A1
Macrostructure
?
Agent model
A2
Agent model
A3
Spatial segregation
Bird flocking
?
Refutation
Explanation and Generative Sufficiency
Agent model
A1
Macrostructure
?
Agent model
A2
Occam´s razor:
"entia non sunt multiplicanda praeter necessitatem", or
"entities should not be multiplied beyond necessity".
Explanation and Generative Sufficiency
Agent model
A1
Macrostructure
?
Agent model
A2
Popper´s view
"We prefer simpler theories to more complex ones
because their empirical content is greater
and because they are better testable"
Explanation and Generative Sufficiency
Agent model
A1
Macrostructure
?
Agent model
A2
Einstein´s 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.
Urban Growth in Latin American cities:
exploring urban dynamics through
agent-based simulation
Joana Xavier Barros
Latin American cities




High rates of urban growth (rapid urbanization)
Poverty + spontaneous settlements (slums)
Poor control of public policies on urban development
Fragmented urban fabric with different and disconnected
morphological patterns that evolve and transform over time.
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
Urban growth
“Urban sprawl” in
United States
“Urban sprawl”in
Europe (UK)
Peripherization in Latin
America (Brazil)
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)
Model: Growth of Latin American cities
Peripherisation module
Spontaneous settlements module
Inner city processes module
Spatial constraints module
Peripherization module
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

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 agent’s cell: then the latter is
evicted and must find another
Peripherization module: rules
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
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
Processos intra-urbanos – exercícios de simulação


Estudo comparativo entre dois padrões diferentes de desenvolvimento
urbano: urban sprawl nas cidades dos EUA e Europa e o crescimento urbano
das cidades latino-americanas.
Objetivo de testar hipóteses e teorias sobre processos intra-urbanos de
transformação urbana em áreas residenciais e verificar a aplicabilidade
dessas teorias para cidades de diferentes culturas.
Cidade latino-americana
10 40 50
d=3
steps = 2
steps2 = 4
1000 cells
Cidade EUA e Europa
steps3 = 2
decayStartPoint = 800
consolidationLimit = 600
2000 cells
steps3 = 8
decayStartPoint = 400
consolidationLimit = 400
3000 cells
4000 cells
5000 cells
6000 cells
40 50 10
d=2
steps = 2
steps2 = 7
7000 cells
Processos intra-urbanos – exercícios de simulação



Processos de filtragem, decadência do centro, e movimento das elites em direção
ao anel periférico são de natureza semelhante em ambas cidades.
O padrão locacional espacial reverso parece ser causado por uma combinação de
diferenças em grau em processos de natureza similar.
As diferenças na composição das sociedades urbanas de cada país parece exercer
um grande impacto no resultado desses processos no padrão espacial urbano de
localização residencial.
Módulo de Barreiras Espaciais

Introduz barreiras espaciais ao modelo de simulação
Barreiras espaciais  corpos de água, áreas com altas declividades, or qualquer outra
área onde a urbanização é impossível

Implementação feita através da introdução de “áreas cinzas” como condição inicial.

Agent’s rules:


Agentes não se assentam ou caminham em sobre as áreas cinzas
Para cada movimento que os agentes fazem em direção a uma nova célula, eles checam se a nova
posição é uma célula cinza ou não, e caso seja, eles retornam as suas posições iniciais e modificam
suas direções para evitar que retornem para a mesma célula.
Exercícios com Barreiras Espaciais

Objetivo: testar os impactos das barreiras espaciais nas tendências de desenvolvimento
espacial mostradas pelo modelo nos experimentos anteriores, e verificar como essas
tendências podem ser relacionadas com a realidade. ]

Mostra como a simples introdução de áreas inatingíveis dentro da malha pode moldar o
desenvolvimento espacial de maneiras tão diferentes.
Exercícios com Módulo de Periferização
posição inicial dos agentes no centro da malha
posição inicial dos agentes na célula-semente
Exercícios com Barreiras Espaciais
Exercícios com Módulo de Processos Intra-urbanos
posição inicial dos agentes no centro da malha
posição inicial dos agentes na célula-semente
importância das barreiras espaciais para que a simulação produza um padrão mais
realístico.
 o papel das barreiras espaciais para o entendimento da morfologia urbana.
Comparação com a realidade
Mapas of distribuição de
renda para Porto Alegre.
(Censo 2000)
Mapas A e B: quantile breaks (3
and 6 ranges)
Mapas C e D: natural breaks (3
and 6 ranges)
Não trabalhamos com
definição de grupos de renda.