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Dynamic Models of Segregation

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Presentation on theme: "Dynamic Models of Segregation"— Presentation transcript:

1 Dynamic Models of Segregation
Thomas C. Shelling Reviewed by Hector Alfaro September 30, 2008

2 SUMMARY

3 Goal Study segregation that results from discriminatory individual behavior. Results useful for any twofold analysis: Black and white Male and female Students and faculty Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

4 Motivation Segregation may be organized or unorganized May occur from
Religion Language of communication Color Correlations Church  Neighborhoods Difficult to find integrated neighborhoods. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

5 Methods Two experiments Spatial Proximity Model
Bounded-Neighborhood Model Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

6 Spatial Proximity Model
Two types of individuals: stars and zeros Dissatisfied individuals denoted by dot over individual. Neighborhood definitions vary, relative to individuals. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

7 Spatial Proximity Model
Results Equilibrium reached. Random sequences yield 5 groupings with 14 members 7-8 groupings with 9-10 members Order does not matter Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

8 Spatial Proximity Model
Two-dimensional model Order can vary Top left to bottom right Center outward Results Segregation occurs regardless of order Extreme ratios lead to minority forming large clusters, disrupting majority. Increasing neighborhood size  increases segregation Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

9 Spatial Proximity Model
Integration exhibits phenomena: Requires more complex patterns Minority is rationed Dead space forms its own clusters Left – upper & lower limits Right – will settle Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

10 Bounded-Neighborhood Model
Neighborhoods are defined. An individual is either in or out. Information is perfect, but intentions not known. Most tolerant white Both satisfied Median white Least tolerant white Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

11 Bounded-Neighborhood Model
Results Only one stable equilibrium: all white or all black. Can vary tolerance slope for more intersection Can limit population to find more points of equilibrium. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

12 Bounded-Neighborhood Model
Results Can study integration by interpreting results differently. Producing equilibriums requires large perturbations (like changing population size) or concerted actions. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

13 Contributions Can make predictions on changes to neighborhoods based on models. Tipping phenomenon: new minority entering an established majority cause earlier residents to evacuate. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

14 ANALYSIS

15 Strengths Broad study, results apply to any two groups one wishes to compare. Models are easy to change and results may be easily reproduced: changing number of neighbors, satisfied/dissatisfied conditions, etc. Results may be interpreted differently: segregation v. integration.

16 Strengths Tolerance in bounded-neighborhood model is a relative measure – indicative of reality. Results may be manipulated to achieve equilibrium.

17 Weaknesses Just a model, not based on studies of the population.
Perhaps too broad, makes it inapplicable to real life. Spatial proximity versus bounded neighbor model not really comparing apples to apples: comparing interactions in multiple neighborhoods versus one neighborhood.

18 Weaknesses Claim that we can study integration by reinterpreting the results: methods chosen particularly to study segregation. Different methods need be employed to study integration. Ways to reach equilibrium are not practical: large perturbations nor concerted actions happen often in reality.

19 Weaknesses Schelling admits no allowance for:
Speculative behavior Time lags Organized action Misperception Information is not always perfect Tipping studies outdated. Models cannot handle complex interactions. Thomas Schelling (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1,

20 Comparison to CAS Cellular Automata Conway’s Game of Life Overall
Directly related to the linear distribution model. Conway’s Game of Life Much like the spatial proximity model. Overall Set of simple rules defined that result in complex behavior Emergent patterns occur. Stephen Wolfram (1983). Cellular Automata. Los Alamos Science, 9, 2-21. Martin Gardner (1970). Mathematical Games. The fantastic combinations of John Conway's new solitaire game "life." Scientific American, 223, , October 1970.

21 Comparison to CAS Prisoner’s Dilemma
Indirect correlation: cooperation and defection may be compared to tolerance of an individual. Further studies could superimpose the payoff matrix into Schelling’s segregation models. Robert Axelrod (1980). Effective choice in the Prisoner's Dilemma. Journal of Conflict Resolution, 24:1, 3-25.

22 Comparison to CAS Schelling’s system exhibits:
Emergence Multiple agents Simple agents Iteration No adaptation, variation. Research looking for unorganized individual behavior into collective results.


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