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Multi-Agent Simulator for Multi-Agent Simulator for Urban Segregation (MASUS)Urban Segregation (MASUS)

A Tool to Explore Alternatives for A Tool to Explore Alternatives for Promoting Inclusive CitiesPromoting Inclusive Cities

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Since 2008, the majority of the world’s population lives in urban Since 2008, the majority of the world’s population lives in urban areasareas

An Urban AgeAn Urban Age

Source: UN-Habitat, 2007


Is this a problem? Is this a problem?

““Cities are not the problem; They Cities are not the problem; They are the solution!”are the solution!”

(Jaime Lerner) (Jaime Lerner)

Potential as engines of developmentPotential as engines of development


Inclusive CitiesInclusive Cities

Promote growth with equity

A place where everyone can benefit from the opportunities cities offer

A barrier to the formation of inclusive A barrier to the formation of inclusive citiescities

Urban Urban SegregationSegregation

Obstacles that Obstacles that contribute to contribute to perpetuate perpetuate

povertypoverty

Impacts of Impacts of SegregationSegregation

Policies to minimize segregation Policies to minimize segregation demand:demand:

A better understanding of the A better understanding of the dynamics of segregation and its dynamics of segregation and its

causal mechanismscausal mechanisms

Segregation displays many of the hallmarks Segregation displays many of the hallmarks of complexityof complexity

Complex Nature of Complex Nature of SegregationSegregation

The Process Matters!The Process Matters!

Complex Nature of Complex Nature of SegregationSegregation

Require bottom-up simulations Agent-Based Model

Agent-Based Models (ABM)Agent-Based Models (ABM)

Focus on Focus on individual decision-making units individual decision-making units (agents), which interact with each other and (agents), which interact with each other and their environmenttheir environment

Natural way to explore the emergence of Natural way to explore the emergence of global structuresglobal structures

MMulti-ulti-AAgent gent SSimulator for imulator for UUrban rban SSegregationegregation

MASUSMASUS

Scientific tool to explore alternative scenarios of segregation

Support planning actions by offering insights about the impact of policy strategies

PurposePurpose

Improve the understanding about segregation and its relation with different contextual mechanisms

MASUS Conceptual MASUS Conceptual ModelModel

São José dos Campos, São José dos Campos, BrazilBrazil

São Paulo StateStudy Area

City of São José dos Campos

MASUS: Process MASUS: Process ScheduleSchedule

Decision-making sub-Decision-making sub-modelmodelALTERNATIVESALTERNATIVES• Not Move

• Move within the same neighborhood

• Move to the same type of neighborhood (n alternatives)

• Move to a different type of neighborhood (m alternatives)

Higher probability to choose alternative Higher probability to choose alternative with higher utilitywith higher utility

Decision-making sub-Decision-making sub-modelmodel

Nesting Structure of the Nesting Structure of the ModelModel

MASUS: Process MASUS: Process ScheduleSchedule

Operational ModelOperational Model

Simulation Simulation ExperimentsExperiments

Comparing simulation outputs with Comparing simulation outputs with empirical dataempirical data

Testing theoretical issuesTesting theoretical issues

Testing anti-segregation policy Testing anti-segregation policy strategiesstrategies

Comparison with Empirical Comparison with Empirical DataData

Initial condition: São José dos Initial condition: São José dos Campos in 1991Campos in 1991• Import GIS layers (households, environment)Import GIS layers (households, environment)

• Set parametersSet parameters

Run 9 annual cyclesRun 9 annual cycles

Compare simulated results with real Compare simulated results with real data (year 2000)data (year 2000)


Dissimilarity Index (local scale)

Initial State (1991)

Simulated Data (1991-2000)

Real Data (2000)

0.54

0.310.15

0.51

0.300.19

0.51

0.300.19


Isolation Poor Households (local scale)


Real Data (2000)

0.54 0.51 0.51



Isolation Affluent Households (local scale)


Real Data (2000)

0.15 0.19 0.19


Testing a theoryTesting a theory

How does inequality affect How does inequality affect segregation? segregation?

Relation between both phenomena has caused controversy in scientific debates

Experiment Compare 3 scenarios 1991-2000

Scenario 1: Previous run (baseline)

Scenario 2: Decreasing inequality

Scenario 3: Increasing inequality

Testing a theoryTesting a theory

Inequality (Gini) Proportion Poor HH Proportion Affluent HH

Scenario 1 (Original) Scenario 2 (Low-Ineq.) Scenario 3 (High-Ineq.)

Dissimilarity Isolation Poor HH Isolation Affluent HH

Testing policy Testing policy strategiesstrategies

Experiment Compare 3 scenarios

Scenario 1 no voucher (baseline)

Scenario 2 200 – 1700 vouchers

Scenario 3 400 – 4200 vouchers

Poverty Dispersion vs. Wealth Poverty Dispersion vs. Wealth DispersionDispersion

Poverty Dispersion: housing vouchers to poor families


Scenario 1 No voucher (baseline)

Scenario 2 200 - 1700 vouchers

(2.3%)

Scenario 3 400 - 4200 vouchers

(5.8%)

Dissimilarity Isolation Poor HH

Isolation Affluent HH

2.3 - 3.5 %5.8 - 10.7%

2.3 - 1.7 %5.8 - 3.4%

2.3 - 5.7 %5.8 - 8.3 %


Poverty DispersionPoverty Dispersion

Demands high and continous investment Demands high and continous investment to decrease poverty isolationto decrease poverty isolation


Slows down the increase in segregation, Slows down the increase in segregation, but does not change the trendsbut does not change the trends

ExperimentExperiment Compare 2 scenariosCompare 2 scenarios

Scenario 1 (baseline)

Scenario 2 new areas for upper classes

Urban areas in 1991Undeveloped areas for upper classes

Testing policy Testing policy strategiesstrategiesPoverty Dispersion vs. Wealth Poverty Dispersion vs. Wealth

DispersionDispersionWealth Dispersion: Wealth Dispersion: Incentives for constructing Incentives for constructing

residential developments for upper classes in poor residential developments for upper classes in poor regions of the cityregions of the city


Scenario 1 baseline

Scenario 2 new areas for

upper classes

Dissimilarity Isolation Poor HH

Isolation Affluent HH


Wealth DispersionWealth Dispersion

Produces long-term outcomesProduces long-term outcomes


More effective at decreasing large-scale More effective at decreasing large-scale segregationsegregation

E.g. Dissimilarity 2010E.g. Dissimilarity 2010

local scale (700m): - 19%local scale (700m): - 19%

large scale (2000m): - 36%large scale (2000m): - 36%


Wealth DispersionWealth Dispersion

Positive changes in the spatial patterns of Positive changes in the spatial patterns of segregationsegregation


Baseline2010

Wealth Dispersion2010

Concluding RemarksConcluding Remarks

MASUS: MASUS: MMulti-ulti-AAgent gent SSimulator for imulator for UUrban rban SSegregationegregation

Virtual laboratory for testing theories and Virtual laboratory for testing theories and policy approaches on segregationpolicy approaches on segregationDoes Does notnot focus on making exact predictions focus on making exact predictions

Exploratory tool, framework for assembling Exploratory tool, framework for assembling relevant informationrelevant information

Oriented towards understanding and Oriented towards understanding and structuring debates in participative structuring debates in participative processes of decision supportprocesses of decision support

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Documents

testing theories

empirical data comparison

real data

simulated data

policy approaches

urban areas

urban agesource

urban ageis