Agent-based models and social simulation

Gilberto CâmaraTiago CarneiroPedro Andrade

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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

White cells attact a cancer cell (cooperative activity)

Foto: National Cancer Institute, EUA http://visualsonline.cancer.gov/

Information flows in planet Earth

Mass and energy transfer between points in the planet

Complex adaptative systems

How come that a city with many inhabitants functions and exhibits patterns of regularity?

How come that an ecosystem with all its diverse species 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

Agent: flexible, interacting and autonomous

An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

Agent-Based Modelling

Goal

Environment

Representations

Communication

ActionPerception

Communication

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

Natural agents, artificial environment

Artificial agents, artificial environment Artificial agents, natural environment

Natural Agents, natural environment

fonte: Helen Couclelis (UCSB)

Four types of agents

Natural agents, artificial environment

Artificial agents, artificial environment Artificial agents, natural environment

Natural Agents, natural environment

fonte: Helen Couclelis (UCSB)

e-science Engineering Applications

BehavioralExperiments

Descriptive Model

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 model1. No central autority2. Each bird reacts to its neighbor3. Model based on bottom up

interactionshttp://www.red3d.com/cwr/boids/

Bird Flocking: Reynolds Model (1987)

www.red3d.com/cwr/boids/

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

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?

SegregationSegregation is an outcome of individual choices

But high levels of segregation indicate mean that people are prejudiced?

Schelling’s Model of Segregation

< 1/3

Micro-level rules of the game

Stay if at least a third of neighbors are “kin”

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 neighboursWhite cells change to black if there are X or more black

neighboursBlack 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

Agen

t

Spa

ce

Space Agent

TerraME: nature-society modelling

T. Carneiro, P. Andrade, et al., “An extensible toolbox for modeling nature-society interactions”. Enviromental Modelling and Software, 2013 (Two PhDs).

Nature represented in cellular spaces, society represented as agents

Geometry

Cellular SpaceSocial Network

Object

Types in TerraLib ecosystem: new tools, new types

Coverage

Time Series Trajectory Event

Agent

2002

2010

2014

CellAgent

forEachAgent forEachCell

forEachCellforEachRelative forEachNeighbor

forEachAgent

CellularSpaceSociety

Group Trajectory

DBMS

agents = cell:getAgents()if #(agents) == 0 then -- empty agent:leave(oldcell) agent:enter(cell)end

Agents within cells

Society

上海宋ABC

ACA

AACCCC

BBC

CBB

CAC

BBA

CCB

CBA

AAA

BAB

createAgent = function(capital) return Agent { capital = capital, -- ... }end

data = {}data[1] = 100; data[2] = 50; data[3] = 25mag = Society(createAgent, data)

mag = Society(createAgent, 50)

capital = 100 capital = 50 capital = 25

Society

function createAgent (capital) person = Agent { init = function (self), -- ...

}end

data = {}data[1] = 100; data[2] = 50; data[3] = 25mag = Society(createAgent, data)

mag = Society(createAgent, 50)

capital = 100 capital = 50 capital = 25

Society

CCC

BBC

CBB

CAC

BBA

CCB

CBA

ABC

ACA

AAC

AAA

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

Group

forEachAgent(mag, function(agent) agent.capital = agent.capital + 100end)

capital = 200 capital = 150 capital = 125

capital = 100 capital = 50 capital = 25

Traversing the Society

Emergence

source: (Bonabeau, 2002)

“Can you grow it?” (Epstein; Axtell; 1996)

Epstein (Generative Social Science)

If you didn´t grow it, you didn´t 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

Scientific method

Science proceeds by conjectures and refutations (Popper)

Explanation and Generative Sufficiency

Macrostructure

Spatial segregationBird flocking

Agent modelA1

Agent modelA2

Agent modelA3

?

Refutation

Conjectures

?

Explanation and Generative Sufficiency

Macrostructure

Occam´s razor:"entia non sunt multiplicanda praeter necessitatem", or

"entities should not be multiplied beyond necessity".

Agent modelA1

Agent modelA2

?

Explanation and Generative Sufficiency

Macrostructure

Popper´s view"We prefer simpler theories to more complex ones

because their empirical content is greater and because they are better testable"

Agent modelA1

Agent modelA2

?

Explanation and Generative Sufficiency

Macrostructure

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.

Agent modelA1

Agent modelA2

?

Urban Growth in Latin American cities:exploring urban dynamics through agent-based simulation

Joana Xavier Barros

2004

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

São Paulo - Brasil Caracas - Venezuela

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.‐ .

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 approachTime + 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 parameter2. high income agent –can locate anywhere ‐3. medium income agent –can locate anywhere except on high‐ ‐

income places4. 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

multiple initial seeds ‐resembles certain characteristics of metropolitan areas

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.

1000 cells 2000 cells 3000 cells 4000 cells 5000 cells 6000 cells 7000 cells

Cidade latino-americana

10 40 50 d = 3steps = 2steps2 = 4

steps3 = 2decayStartPoint = 800consolidationLimit = 600

Cidade EUA e Europa

40 50 10 d = 2

steps = 2steps2 = 7

steps3 = 8decayStartPoint = 400

consolidationLimit = 400

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 malhaposição 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.