1. Photo: Fabio Venni Multi-Agent Simulator for Urban Segregation (MASUS) A Tool to Explore Alternatives for Promoting Inclusive Cities Flávia da Fonseca Feitosa

2. Since 2008, the majority of the world’s population lives in urban areas An Urban Age Source: UN-Habitat, 2007

3. An Urban Age Is this a problem? “Cities are not the problem; They are the solution!” (Jaime Lerner) Potential as engines of development

4. An Urban Age Inclusive Cities Promote growth with equity A place where everyone can benefit from the opportunities cities offer

5. A barrier to the formation of inclusive cities Urban Segregation

6. Obstacles that contribute to perpetuate poverty Impacts of Segregation Policies to minimize segregation demand: A better understanding of the dynamics of segregation and its causal mechanisms

7. Segregation displays many of the hallmarks of complexity Complex Nature of Segregation

8. The Process Matters! Complex Nature of Segregation Require bottom-up simulations  Agent-Based Model Agent-Based Models (ABM) Focus on individual decision-making units (agents), which interact with each other and their environment Natural way to explore the emergence of global structures

9. Multi-Agent Simulator for Urban Segregation Multi-Agent Simulator for Urban Segregation MASUS Scientific tool to explore alternative scenarios of segregation Support planning actions by offering insights about the impact of policy strategiesPurpose Improve the understanding about segregation and its relation with different contextual mechanisms

10. MASUS Conceptual Model

11. São José dos Campos, Brazil São Paulo State Study Area City of São José dos Campos

12. MASUS: Process Schedule

13. Decision-making sub-model ALTERNATIVES 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 with higher utility

14. Decision-making sub-model Nesting Structure of the Model

15. MASUS: Process Schedule

16. Operational Model

17. Simulation Experiments Comparing simulation outputs with empirical data Testing theoretical issues Testing anti-segregation policy strategies

18. Comparison with Empirical Data Initial condition: São José dos Campos in 1991 Import GIS layers (households, environment)Import GIS layers (households, environment) Set parametersSet parameters Run 9 annual cycles Compare simulated results with real data (year 2000)

19. Comparison with Empirical Data Dissimilarity Index (local scale) Initial State (1991) Simulated Data (1991-2000) Real Data (2000) 0.54 0.31 0.15 0.51 0.30 0.19 0.51 0.30 0.19

20. Comparison with Empirical Data Isolation Poor Households (local scale) Initial State (1991) Real Data (2000) 0.54 0.51 Simulated Data (1991-2000)

21. Comparison with Empirical Data Isolation Affluent Households (local scale) Initial State (1991) Real Data (2000) 0.15 0.19 Simulated Data (1991-2000)

22. Testing a theory How does inequality affect 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

23. Testing 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

24. Testing policy strategies Experiment Compare 3 scenarios Scenario 1 no voucher (baseline) Scenario 2 200 – 1700 vouchers Scenario 3 400 – 4200 vouchers Poverty Dispersion vs. Wealth Dispersion Poverty Dispersion: housing vouchers to poor families

25. Testing policy strategies 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 %

26. Testing policy strategies Poverty Dispersion Demands high and continous investment to decrease poverty isolation Poverty Dispersion vs. Wealth Dispersion Slows down the increase in segregation, but does not change the trends

27. Experiment Compare 2 scenarios Scenario 1 (baseline) Scenario 2 new areas for upper classes Urban areas in 1991 Undeveloped areas for upper classes Testing policy strategies Poverty Dispersion vs. Wealth Dispersion Wealth Dispersion: Incentives for constructing residential developments for upper classes in poor regions of the city

28. Testing policy strategies Scenario 1 baseline Scenario 2 new areas for upper classes Dissimilarity Isolation Poor HH Isolation Affluent HH

29. Testing policy strategies Wealth Dispersion Produces long-term outcomes Poverty Dispersion vs. Wealth Dispersion More effective at decreasing large-scale segregation E.g. Dissimilarity 2010 local scale (700m): - 19% large scale (2000m): - 36%

30. Testing policy strategies Wealth Dispersion Positive changes in the spatial patterns of segregation Poverty Dispersion vs. Wealth Dispersion Baseline 2010 Wealth Dispersion 2010

31. Concluding Remarks MASUS: Multi-Agent Simulator for Urban Segregation Virtual laboratory for testing theories and policy approaches on segregation Does not focus on making exact predictions Exploratory tool, framework for assembling relevant information Oriented towards understanding and structuring debates in participative processes of decision support