Polarization and Protests: Understanding Complex Social ...Complex Social and Political Processes...

1: Introduction 2: SimPolSeg and Geographic Polarization 3: Violent Protests

Polarization and Protests: UnderstandingComplex Social and Political Processes Using

Spatial Data and Agent-Based ModelingSimulations

Lefteris AnastasopoulosPhD, UC Berkeley (Political Science)

MA, Harvard (Statistics)

Democracy Fellow, Ash Center for Democratic Governance and Innovation,Harvard Kennedy School of Government

April 23, 2015


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. Uprotest(G,C,P) = G + P

C3 Interactions in space and time.

















Why agent-based models?

Emergence - Complex micro-level interactions can result ifmacro-level patterns.eg) Residential Segregation (Schelling)Reality is DynamicReal world social processes involve dynamic interactionsbetween individuals.Because we can!Parallelization of code and multi-core computing systemsallow large scale realistic simulations.

















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 centralauthority?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.



G = H(1− L)P = 1− exp[−k(C/A)]N = RP




G = H(1− L)P = 1− exp[−k(C/A)]N = RP




G = H(1− L)P = 1− exp[−k(C/A)]N = RP




G = H(1− L)P = 1− exp[−k(C/A)]N = RP




G − N > 0⇒ AOtherwise⇒ Q

States of the world: A = Active, Q = Quiet .G − N - Strength of grievance vs. subjective likelihood ofarrest.



G − N > 0⇒ AOtherwise⇒ Q

States of the world: A = Active, Q = Quiet .G − N - Strength of grievance vs. subjective likelihood ofarrest.



Predict outbreaks of ethnic cleansing.



Effect of safe haven establishment during ethnic cleansingoutbreaks.


Enter Spatial Data...

ABMs give us general predictions in space and time.Demographic and spatial data at very fine levels ofgeography.Feed real spatial and demographic data into ABM togenerate predictions.Best of both worlds!











Current Projects

SimPolSeg: An Agent-Based Simulation of PoliticalMigration Dynamics and Geographic Polarization.Modeling Violent Protests


Current Projects

SimPolSeg: An Agent-Based Simulation of PoliticalMigration Dynamics and Geographic Polarization.Modeling Violent Protests


Political Migration and Geographic Polarization

Geographic urban-suburban polarization rising since the 1950s.

Highway development + suburbanization speculated to be a majorcause (Nall 2014).



Geographic urban-suburban polarization rising since the 1950s.

Highway development + suburbanization speculated to be a majorcause (Nall 2014).



-.1

-.05

0.0

5.1

% C

hang

e in

Sub

urba

niza

tion

(One

Yea

r)

1970 1980 1990 2000 2010Year

Major highway development and suburbanization ceasedaround 1992...Yet geographic polarization increasing at an increasingrate.



-.1

-.05

0.0

5.1

% C

hang

e in

Sub

urba

niza

tion

(One

Yea

r)

1970 1980 1990 2000 2010Year

Major highway development and suburbanization ceasedaround 1992...Yet geographic polarization increasing at an increasingrate.


Push Migration, Diversity and Demographic Change

Causal estimates suggest that white flight resulting from the SecondGreat Migration was largely responsible for post-warsuburbanization. (Boustan 2010)

White flight responsible for suburbanization and polarization?


Push Migration, Diversity and Demographic Change

Causal estimates suggest that white flight resulting from the SecondGreat Migration was largely responsible for post-warsuburbanization. (Boustan 2010)

White flight responsible for suburbanization and polarization?


Political and Racial Segregation

Schelling showed that weak preferences for similarneighbors create segregated urban spaces.Complete Integration or Segregation of an area dependsupon “tolerance” of residents.


Political and Racial Segregation

Schelling showed that weak preferences for similarneighbors create segregated urban spaces.Complete Integration or Segregation of an area dependsupon “tolerance” of residents.


Political Migration and the Migration-Polarization (MP)Theory


Political Ideology and Schelling Tolerance: MCSUIData

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.












Comfort In Minority Neighborhood: MCSUI

Pr(Comfortc |Ideology ,X ) =1

1 + exp − (αc + βc Ideology + XΓc + mj )(1)

White Respondent Ideology v. Probability of Discomfort, N = 2407


Moving From Minority Neighborhood: MCSUI

Pr(Move|Ideology ,X ) =1

1 + exp − (αc + βc Ideology + XΓc + mj )(2)

White Respondent Ideology v. Prob. of Moving from Any NeighborhoodConditional on Discomfort, N = 1695


GSS 2002 Data: Background

Image presented to Respondents in the 2000 General Social Survey.

Respondents asked to indicate ethnic/racial background of each of the14 houses surrounding them.

Choices were: Asian, Black, Hispanic or White.












GSS 2002 Data: Average Preferred % White byIdeology

Average % White (of 14 surrounding houses) by IdeologyAmong White Respondents.


GSS 2002 Data: Preferred % White by Ideology

%White = α + βIdeology + XΓ + cj + ε

DV: Neighborhood % WhiteVARIABLES Original Coefficients Standardized Coefficients

Ideology 0.036*** 0.192***(0.006)

Age 0.002** 0.136**(0.001)

Education -0.001 -0.014(0.002)

Income 0.000 0.018(0.001)

Sex -0.036 -0.056(0.021)

Marital -0.001 0.004(0.007)

Employment 0.003 0.020(0.005)

Children 0.014** 0.078**(0.007)

Observations 770R-squared 0.138

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1


Political Ideology and Tolerance

ηi =√

Ii − Di

Di =√∑K

k=1∑J

j=1 ρji(E [αjk ]− αji)2

0 < Di < Ii < 1

ηi = ToleranceDi= Social DistanceIi= Ideology (0 = Very Conservative, 1 = Very Liberal)



ηi =√

Ii − Di

Di =√∑K

k=1∑J


0 < Di < Ii < 1




ηi =√

Ii − Di

Di =√∑K

k=1∑J


0 < Di < Ii < 1



Utility of Residing in a Neighborhood

URin = ηimn −m2

n + f (pn, εn) = (√

Ii − Di)mn −m2n + f (pn, εn)

0 ≤ mn ≤ 10 < ηi < 10 < Di < Ii < 1


Utility of Residing in a Neighborhood

URint = (

√Ii − Di)mnt −m2

nt + f (pnt , εnt)


Agent Moving Decisions: When to Move?

If mnt > m∗i and ∃n′ ∈ A s.t. mn′ t ≤ m∗

i :{If UR

nt < URn′ t− δn,n′ Move from n

Else Remain in n

δn,n′ = cost of moving from n to n′.

Euclidean distance between two neighborhood centroids.


Agent Moving Decisions: When to Move?

If mnt > m∗i and ∃n′ ∈ A s.t. mn′ t ≤ m∗

i :{If UR

nt < URn′ t− δn,n′ Move from n

Else Remain in n

δn,n′ = cost of moving from n to n′.

Euclidean distance between two neighborhood centroids.


Agent Moving Decisions: Where to Move?

maxn′

L(URnt ,U

Rn′ t) = (UR

nt − δn,n′ + URn′ t)

2

Choose n′

to maximize utility gain from moving.


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.


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.


Simulation

Variable Symbol Initial ValueNeighborhoods A N = 20

Mean Between Neighborhood Ideology It0 µIt0 = 0.5σI

t0 = 0.1It0 =rtnorm(n=20,mu =0.5,sd=0.1)

Minoritypop Bt0 µBt0 = 5σB

t0 = 5Bt0 = rtnorm(n=20,mu =5,sd=5)

Majoritypop Wt0 µWt0 = 100σW

t0 = 10Wt0 = rtnorm(n=20,mu =100,sd=10)


Simulation Results

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


Simulation Results


Simulation Results

Average neighborhood ideology, t = 0 to t = 151


Simulation Results

Polarization and Segregation BetweenNeighborhoods,t = 0 to t = 151


SimPolSeg 2.0

SimPolSeg2(TractID,MinorityPopT0,MinorityPopT1,WhitePopT0,TotPopT0,DemocratT0,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.


SimPolSeg 2.0




millions of agents.


SimPolSeg 2.0




millions of agents.


SimPolSeg 2.0




millions of agents.


SimPolSeg 2.0: Simulate Political Effects of KatrinaMigration

Over 100,000 African Americans migrated to Harris Countyshortly after Katrina.Profound political effects.



Over 100,000 African Americans migrated to Harris Countyshortly after Katrina.Profound political effects.



Increase in Democratic voting in Harris.Increase in polarization between Harris and surroundingcounties.



Increase in Democratic voting in Harris.Increase in polarization between Harris and surroundingcounties.


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.




















Understanding Violent Protest Outbreaks and Spread:Common Themes

α θd zd ,n

βkψ

wd ,n

K

ND

Extract latent themes preceding violent protests (topics) using LatentDirichlet Allocation (LDA).

Corpus - Collection of Tweets one day before violent protests break out.

Documents - Tweets, one, three and five hours before protests breakout.

Common themes - Use topic chains to find topic similarity for eachprotest.



α θd zd ,n

βkψ

wd ,n

K

ND







α θd zd ,n

βkψ

wd ,n

K

ND







α θd zd ,n

βkψ

wd ,n

K

ND






Understanding Violent Protest Outbreaks: Model andSimulations

P(A) =f (G,N,C,Di,i ′ ,Di,s)

P(Q) =P(A′) = 1− P(A)

Model agent behavior building on Epstein (2002).P(A) = probability of protesting, P(Q) = probability of notprotesting.Traditional Components: G = Grievance, N = likelihoodof being arrested, C = law enforcement.Spatial Components: Di,i ′ = distance between agent andlikeminded agents. Di,s = distance between agent and siteof protest s.




P(Q) =P(A′) = 1− P(A)





P(Q) =P(A′) = 1− P(A)





P(Q) =P(A′) = 1− P(A)




Model protest spread as a spatial diffusion process from publictransportation hubs or central meeting nodes.


Understanding Violent Protest Outbreaks: Test ModelUsing 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 fromstations and test model.








Geocoded #Ferguson on November 24, 2014 - 6:30(CT)






Questions and Comments Appreciated!

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