Polarization and Protests: Understanding Complex Social and Political Processes Using Spatial Data and Agent-Based Modeling Simulations

Transcription

1 Polarization and Protests: Understanding Complex Social and Political Processes Using Spatial Data and Agent-Based Modeling Simulations Lefteris Anastasopoulos PhD, UC Berkeley (Political Science) MA, Harvard (Statistics) Democracy Fellow, Ash Center for Democratic Governance and Innovation, Harvard Kennedy School of Government April 23, 2015

2 What are agent-based models? 1 Agents - Entities with goals and preferences. 2 Utility - Preferences usually expressed in the form of a utility function. Eg. U protest (G, C, P) = G + P C 3 Interactions in space and time.

3 What are agent-based models? 1 Agents - Entities with goals and preferences. 2 Utility - Preferences usually expressed in the form of a utility function. Eg. U protest (G, C, P) = G + P C 3 Interactions in space and time.

4 What are agent-based models? 1 Agents - Entities with goals and preferences. 2 Utility - Preferences usually expressed in the form of a utility function. Eg. U protest (G, C, P) = G + P C 3 Interactions in space and time.

5 What are agent-based models? 1 Agents - Entities with goals and preferences. 2 Utility - Preferences usually expressed in the form of a utility function. Eg. U protest (G, C, P) = G + P C 3 Interactions in space and time.

6 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

7 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

8 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

9 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

10 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

11 Why agent-based models? Emergence - Complex micro-level interactions can result if macro-level patterns. eg) Residential Segregation (Schelling) Reality is Dynamic Real world social processes involve dynamic interactions between individuals. Because we can! Parallelization of code and multi-core computing systems allow large scale realistic simulations.

12 Example: Modeling Civil Violence (Epstein 2002) G = H(1 L) P = 1 exp[ k(c/a)] N = RP Simple ABM of civil violence w profound implications. What factors determine rebellion against a central authority? G = Grievance, H = Hardship, L = Legitimacy. P = Prob of arrest. C/A = Cop to protester ratio. N = Product of risk aversion R and arrest prob. P.

13 Example: Modeling Civil Violence (Epstein 2002) G = H(1 L) P = 1 exp[ k(c/a)] N = RP Simple ABM of civil violence w profound implications. What factors determine rebellion against a central authority? G = Grievance, H = Hardship, L = Legitimacy. P = Prob of arrest. C/A = Cop to protester ratio. N = Product of risk aversion R and arrest prob. P.

14 Example: Modeling Civil Violence (Epstein 2002) G = H(1 L) P = 1 exp[ k(c/a)] N = RP Simple ABM of civil violence w profound implications. What factors determine rebellion against a central authority? G = Grievance, H = Hardship, L = Legitimacy. P = Prob of arrest. C/A = Cop to protester ratio. N = Product of risk aversion R and arrest prob. P.

15 Example: Modeling Civil Violence (Epstein 2002) G = H(1 L) P = 1 exp[ k(c/a)] N = RP Simple ABM of civil violence w profound implications. What factors determine rebellion against a central authority? G = Grievance, H = Hardship, L = Legitimacy. P = Prob of arrest. C/A = Cop to protester ratio. N = Product of risk aversion R and arrest prob. P.

16 Example: Modeling Civil Violence (Epstein 2002) G = H(1 L) P = 1 exp[ k(c/a)] N = RP Simple ABM of civil violence w profound implications. What factors determine rebellion against a central authority? G = Grievance, H = Hardship, L = Legitimacy. P = Prob of arrest. C/A = Cop to protester ratio. N = Product of risk aversion R and arrest prob. P.

17 Example: Modeling Civil Violence (Epstein 2002) G N > 0 A Otherwise Q States of the world: A = Active, Q = Quiet. G N - Strength of grievance vs. subjective likelihood of arrest.

18 Example: Modeling Civil Violence (Epstein 2002) G N > 0 A Otherwise Q States of the world: A = Active, Q = Quiet. G N - Strength of grievance vs. subjective likelihood of arrest.

19 Example: Modeling Civil Violence (Epstein 2002) Predict outbreaks of ethnic cleansing.

20 Example: Modeling Civil Violence (Epstein 2002) Effect of safe haven establishment during ethnic cleansing outbreaks.

21 Enter Spatial Data... ABMs give us general predictions in space and time. Demographic and spatial data at very fine levels of geography. Feed real spatial and demographic data into ABM to generate predictions. Best of both worlds!

22 Enter Spatial Data... ABMs give us general predictions in space and time. Demographic and spatial data at very fine levels of geography. Feed real spatial and demographic data into ABM to generate predictions. Best of both worlds!

23 Enter Spatial Data... ABMs give us general predictions in space and time. Demographic and spatial data at very fine levels of geography. Feed real spatial and demographic data into ABM to generate predictions. Best of both worlds!

24 Enter Spatial Data... ABMs give us general predictions in space and time. Demographic and spatial data at very fine levels of geography. Feed real spatial and demographic data into ABM to generate predictions. Best of both worlds!

25 Current Projects SimPolSeg: An Agent-Based Simulation of Political Migration Dynamics and Geographic Polarization. Modeling Violent Protests

26 Current Projects SimPolSeg: An Agent-Based Simulation of Political Migration Dynamics and Geographic Polarization. Modeling Violent Protests

27 Political Migration and Geographic Polarization Geographic urban-suburban polarization rising since the 1950s. Highway development + suburbanization speculated to be a major cause (Nall 2014).

28 Political Migration and Geographic Polarization Geographic urban-suburban polarization rising since the 1950s. Highway development + suburbanization speculated to be a major cause (Nall 2014).

29 Political Migration and Geographic Polarization % Change in Suburbanization (One Year) Year Major highway development and suburbanization ceased around Yet geographic polarization increasing at an increasing rate.

30 Political Migration and Geographic Polarization % Change in Suburbanization (One Year) Year Major highway development and suburbanization ceased around Yet geographic polarization increasing at an increasing rate.

31 Push Migration, Diversity and Demographic Change Causal estimates suggest that white flight resulting from the Second Great Migration was largely responsible for post-war suburbanization. (Boustan 2010) White flight responsible for suburbanization and polarization?

32 Push Migration, Diversity and Demographic Change Causal estimates suggest that white flight resulting from the Second Great Migration was largely responsible for post-war suburbanization. (Boustan 2010) White flight responsible for suburbanization and polarization?

33 Political and Racial Segregation Schelling showed that weak preferences for similar neighbors create segregated urban spaces. Complete Integration or Segregation of an area depends upon tolerance of residents.

34 Political and Racial Segregation Schelling showed that weak preferences for similar neighbors create segregated urban spaces. Complete Integration or Segregation of an area depends upon tolerance of residents.

35 Political Migration and the Migration-Polarization (MP) Theory

36 Political Ideology and Schelling Tolerance: MCSUI Data DV 1: Would you feel comfortable in the (7%/20%/33%/53% minority) neighborhood? DV 2: If uncomfortable, would you be willing to move out of the (7%/20%/33%/53% minority) neighborhood? IV Political ideology.

37 Political Ideology and Schelling Tolerance: MCSUI Data DV 1: Would you feel comfortable in the (7%/20%/33%/53% minority) neighborhood? DV 2: If uncomfortable, would you be willing to move out of the (7%/20%/33%/53% minority) neighborhood? IV Political ideology.

38 Political Ideology and Schelling Tolerance: MCSUI Data DV 1: Would you feel comfortable in the (7%/20%/33%/53% minority) neighborhood? DV 2: If uncomfortable, would you be willing to move out of the (7%/20%/33%/53% minority) neighborhood? IV Political ideology.

39 Comfort In Minority Neighborhood: MCSUI Pr(Comfort c Ideology, X) = exp (α c + β cideology + XΓ c + m j ) (1) White Respondent Ideology v. Probability of Discomfort, N = 2407

40 Moving From Minority Neighborhood: MCSUI Pr(Move Ideology, X) = exp (α c + β cideology + XΓ c + m j ) (2) White Respondent Ideology v. Prob. of Moving from Any Neighborhood Conditional on Discomfort, N = 1695

41 GSS 2002 Data: Background Image presented to Respondents in the 2000 General Social Survey. Respondents asked to indicate ethnic/racial background of each of the 14 houses surrounding them. Choices were: Asian, Black, Hispanic or White.

42 GSS 2002 Data: Background Image presented to Respondents in the 2000 General Social Survey. Respondents asked to indicate ethnic/racial background of each of the 14 houses surrounding them. Choices were: Asian, Black, Hispanic or White.

43 GSS 2002 Data: Background Image presented to Respondents in the 2000 General Social Survey. Respondents asked to indicate ethnic/racial background of each of the 14 houses surrounding them. Choices were: Asian, Black, Hispanic or White.

44 GSS 2002 Data: Average Preferred % White by Ideology Average % White (of 14 surrounding houses) by Ideology Among White Respondents.

45 GSS 2002 Data: Preferred % White by Ideology %White = α + βideology + XΓ + c j + ɛ DV: Neighborhood % White VARIABLES Original Coefficients Standardized Coefficients Ideology 0.036*** 0.192*** (0.006) Age 0.002** 0.136** (0.001) Education (0.002) Income (0.001) Sex (0.021) Marital (0.007) Employment (0.005) Children 0.014** 0.078** (0.007) Observations 770 R-squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

46 Political Ideology and Tolerance η i = I i D i D i = K J k=1 j=1 ρ ji(e[α jk ] α ji ) 2 0 < D i < I i < 1 η i = Tolerance D i = Social Distance I i = Ideology (0 = Very Conservative, 1 = Very Liberal)

47 Political Ideology and Tolerance η i = I i D i D i = K J k=1 j=1 ρ ji(e[α jk ] α ji ) 2 0 < D i < I i < 1 η i = Tolerance D i = Social Distance I i = Ideology (0 = Very Conservative, 1 = Very Liberal)

48 Political Ideology and Tolerance η i = I i D i D i = K J k=1 j=1 ρ ji(e[α jk ] α ji ) 2 0 < D i < I i < 1 η i = Tolerance D i = Social Distance I i = Ideology (0 = Very Conservative, 1 = Very Liberal)

49 Utility of Residing in a Neighborhood U R in = η im n m 2 n + f (p n, ɛ n ) = ( I i D i )m n m 2 n + f (p n, ɛ n ) 0 m n 1 0 < η i < 1 0 < D i < I i < 1

50 Utility of Residing in a Neighborhood U R int = ( I i D i )m nt m 2 nt + f (p nt, ɛ nt )

51 Agent Moving Decisions: When to Move? If m nt > mi and n A s.t. m n t m i : { If U R nt < U R δ n t n,n Move from n Else Remain in n δ n,n = cost of moving from n to n. Euclidean distance between two neighborhood centroids.

52 Agent Moving Decisions: When to Move? If m nt > mi and n A s.t. m n t m i : { If U R nt < U R δ n t n,n Move from n Else Remain in n δ n,n = cost of moving from n to n. Euclidean distance between two neighborhood centroids.

53 Agent Moving Decisions: Where to Move? max n L(U R nt, U R n t ) = (UR nt δ n,n + U R n t )2 Choose n to maximize utility gain from moving.

54 SimPolSeg 1.0 SimPolSeg(Neighborhood, MinorityPop0, MinorityPop1, WhitePop, TotPop, Ideology, Sims) Initial SimPolSeg 1.0 software takes arguments above. Simulates political effects of change in minority population.

55 SimPolSeg 1.0 SimPolSeg(Neighborhood, MinorityPop0, MinorityPop1, WhitePop, TotPop, Ideology, Sims) Initial SimPolSeg 1.0 software takes arguments above. Simulates political effects of change in minority population.

56 Simulation Variable Symbol Initial Value Neighborhoods A N = 20 Mean Between Neighborhood Ideology I t0 µ I t 0 = 0.5 σt I 0 = 0.1 I t0 =rtnorm(n=20,mu =0.5,sd=0.1) Minoritypop B t0 µ B t 0 = 5 σt B 0 = 5 B t0 = rtnorm(n=20,mu =5,sd=5) Majoritypop W t0 µ W t 0 = 100 σt W 0 = 10 W t0 = rtnorm(n=20,mu =100,sd=10)

57 Simulation Results Movers as Percent of Total Population,t = 0 to t = 151

58 Simulation Results

59 Simulation Results Average neighborhood ideology, t = 0 to t = 151

60 Simulation Results Polarization and Segregation Between Neighborhoods,t = 0 to t = 151

61 SimPolSeg 2.0 SimPolSeg2(TractID, MinorityPopT 0, MinorityPopT 1, WhitePopT 0, TotPopT 0, DemocratT 0, RepublicanT 0, Sims, Lat, Long) SimPolSeg 2.0 under development. New Features : 1 Inputs real Census tract data and demographics. 2 Parallelized code to efficiently handle interactions between millions of agents.

62 SimPolSeg 2.0 SimPolSeg2(TractID, MinorityPopT 0, MinorityPopT 1, WhitePopT 0, TotPopT 0, DemocratT 0, RepublicanT 0, Sims, Lat, Long) SimPolSeg 2.0 under development. New Features : 1 Inputs real Census tract data and demographics. 2 Parallelized code to efficiently handle interactions between millions of agents.

63 SimPolSeg 2.0 SimPolSeg2(TractID, MinorityPopT 0, MinorityPopT 1, WhitePopT 0, TotPopT 0, DemocratT 0, RepublicanT 0, Sims, Lat, Long) SimPolSeg 2.0 under development. New Features : 1 Inputs real Census tract data and demographics. 2 Parallelized code to efficiently handle interactions between millions of agents.

64 SimPolSeg 2.0 SimPolSeg2(TractID, MinorityPopT 0, MinorityPopT 1, WhitePopT 0, TotPopT 0, DemocratT 0, RepublicanT 0, Sims, Lat, Long) SimPolSeg 2.0 under development. New Features : 1 Inputs real Census tract data and demographics. 2 Parallelized code to efficiently handle interactions between millions of agents.

65 SimPolSeg 2.0: Simulate Political Effects of Katrina Migration Over 100,000 African Americans migrated to Harris County shortly after Katrina. Profound political effects.

66 SimPolSeg 2.0: Simulate Political Effects of Katrina Migration Over 100,000 African Americans migrated to Harris County shortly after Katrina. Profound political effects.

67 SimPolSeg 2.0: Simulate Political Effects of Katrina Migration Increase in Democratic voting in Harris. Increase in polarization between Harris and surrounding counties.

68 SimPolSeg 2.0: Simulate Political Effects of Katrina Migration Increase in Democratic voting in Harris. Increase in polarization between Harris and surrounding counties.

69 Understanding Violent Protest Outbreaks Goals: 1 Discover common themes leading up to violent protests using topic models of Twitter data. 2 Simulate spatial diffusion, spread and containment of protest activity using an agent-based model. 3 Test model using geocoded Tweets and public transportation data.

70 Understanding Violent Protest Outbreaks Goals: 1 Discover common themes leading up to violent protests using topic models of Twitter data. 2 Simulate spatial diffusion, spread and containment of protest activity using an agent-based model. 3 Test model using geocoded Tweets and public transportation data.

71 Understanding Violent Protest Outbreaks Goals: 1 Discover common themes leading up to violent protests using topic models of Twitter data. 2 Simulate spatial diffusion, spread and containment of protest activity using an agent-based model. 3 Test model using geocoded Tweets and public transportation data.

72 Understanding Violent Protest Outbreaks Goals: 1 Discover common themes leading up to violent protests using topic models of Twitter data. 2 Simulate spatial diffusion, spread and containment of protest activity using an agent-based model. 3 Test model using geocoded Tweets and public transportation data.

73 Understanding Violent Protest Outbreaks and Spread: Common Themes K ψ βk α θd zd,n wd,n N D Extract latent themes preceding violent protests (topics) using Latent Dirichlet Allocation (LDA). Corpus - Collection of Tweets one day before violent protests break out. Documents - Tweets, one, three and five hours before protests break out. Common themes - Use topic chains to find topic similarity for each protest.

74 Understanding Violent Protest Outbreaks and Spread: Common Themes K ψ βk α θd zd,n wd,n N D Extract latent themes preceding violent protests (topics) using Latent Dirichlet Allocation (LDA). Corpus - Collection of Tweets one day before violent protests break out. Documents - Tweets, one, three and five hours before protests break out. Common themes - Use topic chains to find topic similarity for each protest.

75 Understanding Violent Protest Outbreaks and Spread: Common Themes K ψ βk α θd zd,n wd,n N D Extract latent themes preceding violent protests (topics) using Latent Dirichlet Allocation (LDA). Corpus - Collection of Tweets one day before violent protests break out. Documents - Tweets, one, three and five hours before protests break out. Common themes - Use topic chains to find topic similarity for each protest.

76 Understanding Violent Protest Outbreaks and Spread: Common Themes K ψ βk α θd zd,n wd,n N D Extract latent themes preceding violent protests (topics) using Latent Dirichlet Allocation (LDA). Corpus - Collection of Tweets one day before violent protests break out. Documents - Tweets, one, three and five hours before protests break out. Common themes - Use topic chains to find topic similarity for each protest.

77 Understanding Violent Protest Outbreaks: Model and Simulations P(A) =f (G, N, C, D i,i, D i,s ) P(Q) =P(A ) = 1 P(A) Model agent behavior building on Epstein (2002). P(A) = probability of protesting, P(Q) = probability of not protesting. Traditional Components: G = Grievance, N = likelihood of being arrested, C = law enforcement. Spatial Components: D i,i = distance between agent and likeminded agents. D i,s = distance between agent and site of protest s.

78 Understanding Violent Protest Outbreaks: Model and Simulations P(A) =f (G, N, C, D i,i, D i,s ) P(Q) =P(A ) = 1 P(A) Model agent behavior building on Epstein (2002). P(A) = probability of protesting, P(Q) = probability of not protesting. Traditional Components: G = Grievance, N = likelihood of being arrested, C = law enforcement. Spatial Components: D i,i = distance between agent and likeminded agents. D i,s = distance between agent and site of protest s.

79 Understanding Violent Protest Outbreaks: Model and Simulations P(A) =f (G, N, C, D i,i, D i,s ) P(Q) =P(A ) = 1 P(A) Model agent behavior building on Epstein (2002). P(A) = probability of protesting, P(Q) = probability of not protesting. Traditional Components: G = Grievance, N = likelihood of being arrested, C = law enforcement. Spatial Components: D i,i = distance between agent and likeminded agents. D i,s = distance between agent and site of protest s.

80 Understanding Violent Protest Outbreaks: Model and Simulations P(A) =f (G, N, C, D i,i, D i,s ) P(Q) =P(A ) = 1 P(A) Model agent behavior building on Epstein (2002). P(A) = probability of protesting, P(Q) = probability of not protesting. Traditional Components: G = Grievance, N = likelihood of being arrested, C = law enforcement. Spatial Components: D i,i = distance between agent and likeminded agents. D i,s = distance between agent and site of protest s.

81 Understanding Violent Protest Outbreaks: Model and Simulations Model protest spread as a spatial diffusion process from public transportation hubs or central meeting nodes.

82 Understanding Violent Protest Outbreaks: Test Model Using Data Ferguson and Arab Spring protests in Egypt as test cases. Ferguson acquittal announced around 9:22(ET)/8:22 (CT)/6:22 (PT). Use BART data andgeocoded Tweets to explore protest diffusion from stations and test model.

83 Understanding Violent Protest Outbreaks: Test Model Using Data Ferguson and Arab Spring protests in Egypt as test cases. Ferguson acquittal announced around 9:22(ET)/8:22 (CT)/6:22 (PT). Use BART data andgeocoded Tweets to explore protest diffusion from stations and test model.

84 Understanding Violent Protest Outbreaks: Test Model Using Data Ferguson and Arab Spring protests in Egypt as test cases. Ferguson acquittal announced around 9:22(ET)/8:22 (CT)/6:22 (PT). Use BART data andgeocoded Tweets to explore protest diffusion from stations and test model.

85 Geocoded #Ferguson on November 24, :30 (CT)

86 Geocoded #Ferguson on November 24, :30 (CT)

87 Geocoded #Ferguson on November 24, :30 (CT)

88 Questions and Comments Appreciated!