sdal pires, bianica, riots in an urban slum 140813
DESCRIPTION
In order to understand the relationship between people, physical space, and future change, a diverse set of methods is used that focuses around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA). The intersection between these research areas can be represented through computational social science (CSS), which lies at the foundation of this research as it represents the interdisciplinary science that uses computational modeling and related techniques to study complex social systems. A computational model of the riots that broke-out in an urban slum after the 2007 Kenyan presidential election is used to demonstrate the value of integrating these research areas. Characteristics such as poverty, overpopulation, and a growing youth bulge put urban slums at greater risk for violence. Using empirical data for which to build the landscape and provide agents with unique attributes, an ABM is integrated with SNA and GIS to simulate the outbreak of riots. The model investigates the role individual identity, group identity, and social influence played on the occurrence and intensity of riots. Model results find that the cyclical nature in the emergence and dissolution of rioting is due to positive reinforcement, an effect that can be largely attributed to the agents’ social networks, and thus their interactions and influences through these networks. Riots arise from the interactions between individuals with unique attributes, all within a connected social network over a physical environment. In order to gain a better understanding of the macro-level patterns that emerge, the nonlinear and reinforcing nature of this system is modeled from the bottom-up.TRANSCRIPT
- 1. RiotsinanUrbanSlum:UsingComputationalMethodstoExploreSocialPhenomenaBianicaPiresDepartmentofComputationalSocialScienceGeorgeMasonUniversityAugust14,2014
2. TalkOutline Introduction Background ModelingRiotsinanUrbanSlum Conclusion 3. ComputationalSocialScience AninterdisciplinarysciencethatusescomputationalmodelingandrelatedtechniquestostudycomplexsocialsystemsIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 4. ExampleModelsUrbanRiotsResource-drivenWarOrganizedCrimeTerroristAc:vityTemporalScaleSpa:alScaleMinutesDaysMonthsYearsMacroMesoMicroIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 5. IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 6. Agent-BasedModeling(ABM) ABMssimulateartiQicialsocietiesfromthebottom-up Agents Autonomous Individualswhicharenotcentrallygoverned Heterogeneous Active E.g.Pro-activeorreactive Adaptive BeneQitsofABM Representingcomplexsystems ModelinghumanbehaviorSocialNetworkAnalysis(SNA)GeographicInforma:onSystems(GIS)Source:MacalandNorth(2010)IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 7. LinkingABM,SNA,andGIS SocialprocesseshappenonaphysicallocationandGISkeepstrackofthelocationofevents,activities,andthings SNAstudiestherelationshipsbetweenpeopleandgroups ABMallowsustomodellocalinteractionsthatoccuroverphysicalspaceandthroughsocialnetworksIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 8. ModelingHumanBehaviorinanABM RepresentingbehaviorinanABM ThePECSFrameworkSource:Schmidt(2002)IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 9. WhyStudyRiotsinanUrbanSlum? InternalconQlictsorsmallwarsdominatethetypeofconQlictseenaroundtheworld Poverty,inequities,andunderdevelopment Urbanization Theyouthbulge Resourcescarcityissues Theeffectonthecivilian CallsforanapproachthattakesintoconsiderationtheuniquechallengesandnatureoftheseconQlicts(Lederach,1999)IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 10. Background Kibera,aNairobislum,housesapproximately235,000residentsina3.9kmby1.5kmarea In2008,Kiberabecametheepicenterformuchofthe[post-election]violencethatrockedthecapital(InternationalCrisisGroup,2008) AmodelintegratesABM,SNA,andGIStoexploretheoutbreakofriotsIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 11. TheModelingWorldNon-fixedObjectsLayersResidentsHouseholdsHomes,Businesses,FaciliCes,WaterPointsStructuresPhysicalEnvironmentLayersGIShelpscreatearealisCcEnvironmentforwhichAgentscaninteractandmoveFixedObjectsLayerGISandsurveydatacompletetheEnvironmentParcelsNeighborhoodBoundariesSocioeconomicandsurveydataprovidesAgentswithindividualandhousehold-levelaOributesTransportaConNetworkIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 12. AgentBehavior&thePECSFrameworkSet of motives(1) Provide for household(2) Take care of homeIntensityAnalyzerSet of possible actions(1) Go to work(3) Acquire knowledge(4) Spend time at home(5) Socialize(6) Faith(2) Search for employment(3) Go to school(4) Perform domesticactivities(5) Get water(6) Visit friends(7) Visit religious facility(8) Riot Motivesanddeterminingtheaction-guidingmotiveviatheIntensityAnalyzerIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 13. Maslows(1954)Qive-levelhierarchyofneedsSelf-actualiza:onEsteemBelongingSafetyPhysiologicalAdaptedfromMaslow(1954)Ac:vi:esGotoworkSearchforemploymentGotoschoolPerformdomesCcacCviCesGetwaterAOendreligiousinsCtuConVisitfriendsBuildoutdynamicsocialnetworksTheDailyActivitySchedulerIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 14. TheIdentityModelIden?ty(Standard(Comparator(SelfCesteem( Frustra?on(Input( Output(Symbol(and(Resource(Flows(in(the(Environment(Error(Signal(Behavior(Percep?ons(Reflected(Appraisals(Person(Environment(Aggression(Repeated(Unsuccessful(AEempts(AdaptedfromBurkeandStets(2009)andGreen(2001) StetsandBurke(2000)UniQiedTheoryofIdentityIden::es:DomesCcacCviCesStudentEmployeeEthnicityRioterIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 15. TheSocialInQluenceModel Rumorsplayedamajorroleintheriots Diffusionprocessoccursthroughtheagentslocalinteractionsastheygoabouttheirdailyactivities DisruptioninidentityveriQicationprocess AstructuralapproachtosocialinQluencetheory(Friedkin,2001) Especiallyusefulwhenonlythecommunicationnetworkisavailable SNAtechniquessuchascentralitymeasuresandstructuralequivalenceusedtodetermineagentsQinalopinion Afunctionofinitialopinion,interpersonalties,andsusceptibilitytoinQluenceIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 16. Percep+on' Sensor'Individual&Characteris2cs&TheSocialIn:luenceModel(Friedkin,2001)Ini2al&Opinion&Total&Interpersonal&TheDailyActivityScheduler(Maslow,1954)TheIdentityModel(StetsandBurke,2000)State&Variables&&&& Input&Transi2on&Processes&' 'Cogni+on'Behavior' Actor'Output&Social'Status'Iden2ty& Emo+on' Physis'Standard&Error&Signal&Effects&Final&Opinion&Ac2on&Sequence&Comparator&SelfBesteem&Frustra2onBAggression&Energy&Reservoir&Social&Role&and&Group&Iden2ty&Legend'' Daily&Ac2vity&Scheduler&Iden2ty&Model&Rumor&Propaga2on&and&Social&Influence&Model&IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 17. TheSocialNetworks Evolveasagentsinteractthroughtheirdailyactivities Impacttheactivationofanidentity InQluenceanagentsdecisiontoriotusingcentralitymeasuresandstructuralequivalenceDay0Day1Day2IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 18. IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 19. 0.30%$0.25%$0.20%$0.15%$0.10%$0.05%$0.00%$1$ 2$ 3$ 4$ 5$ 6$ 7$ 8$ 9$ 10$ 11$ 12$ 13$ 14$ 15$ 16$ 17$ 18$ 19$ 20$ 21$ 22$ 23$ 24$ 25$ 26$ 27$ 28$Percent'of'Popula.on'Rio.ng'Day'RiotingDynamicsIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 20. CharacteristicsofRiotersAge$5$to$181No$HH$Discrepancy$ Age$5$to$181HH$Discrepancy$ Age$Over$181No$HH$Discrepancy$ Age$18$and$Over1HH$Discrepancy$0.18%$0.16%$0.14%$0.12%$0.10%$0.08%$0.06%$0.04%$0.02%$0.00%$1$ $2$$ 3$ 4$Propor%on'of'Popula%on'Week'IntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 21. QualityofLife6.4%%6.3%%6.2%%6.1%%6.0%%5.9%%5.8%%5.7%%5.6%%5.5%%5.4%%Educa8on%is%Increased% Employment%is%Increased% Employment%and%Educa8on%is%Increased%Default% 50%% 100%% 200%% 300%%Percent'of'Popula.on'Capacity'Increase'1.4%$1.2%$1.0%$0.8%$0.6%$0.4%$0.2%$0.0%$Educa6on$is$Increased$ Employment$is$Increased$ Employment$and$Educa6on$is$Increased$Default$ 50%$ 100%$ 200%$ 300%$Percent'of'Popula.on'Capacity'Increase'ThepopulationsocializingThepopulationattendingareligiousfacilityIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 22. ModelSummary Modelsresults Thecyclicalpatterninriotoutbreakisduetothereinforcingnatureoftheseenvironments Resultsindicatethatyoutharemoresusceptibletorioting Increasingemploymentandeducationopportunitiesincreasesqualityoflife TheintegrationofABM,SNA,andGIS Diffusionprocessesofarumor Dynamiccreationofsocialnetworksthroughlocalinteractions Applicationoftheorytomodelhumanbehavior Arealworldenvironmentgroundedonempiricaldata SNAandGISfacilitatedtheimplementationoftheagentscognitiveframeworkIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 23. Conclusions Applyingtheoryandstateoftheartmodelingtechniques,amodelofcollectiveactionwasdiscussed OneoftheQirsttointegrateABM,SNA,andGIS,especiallyinrelationtocollectiveaction OneoftheQirstmodelstousethePECSframeworktoimplementagentbehaviorgroundedintheory Serveasbuildingblocksforfurtherwork,especiallyasmoredatabecomesreadilyavailableandcomputationalresourcesbecomecheaper Foundationforothersocialscienceapplications Diseasepropagationthroughdynamicsocialnetworksandoverphysicalspace ThespreadandinQluenceofhealthbehaviors Residentialsettlementpatternsanditsimpactonhomelessness TheimpactofemploymentandeducationonqualityoflifeIntroductionBackgroundModelingRiotsinanUrbanSlumConclusion 24. AcknowledgementsThanksto AndrewCrooks,RobertAxtell,WilliamKennedy,andRichardMedinafortheirvaluedsupportandguidance ClaudioCiofQi-RevillaandGeorgeMasonsUniversityMURIprojectforpartialQinancialsupport 25. Thankyouforlistening!Comments,questions,andsuggestionsarewelcome.Source:hOp://ediCon.cnn.com/2013/08/12/opinion/we-are-watching-african-governments/index.htmlWebsite:Email:[email protected]://css.gmu.edu/pires 26. SupplementaryMaterial 27. ASimpleABMofTrafQicMovement Eachcarfollowsasimplesetofrules: Iftheresacarcloseahead,slowdown Iftheresnocarahead,speedup DemonstrateshowtrafQicjamscanformwithoutanyobviousincident SimplerulescanexplaincomplexphenomenaIntroductionABMIntegratingABM,SNA,GISHumanBehaviorRiotsConclusionSource:NetLogo 28. IntroductionABMIntegratingABM,SNA,GISHumanBehaviorRiotsConclusionSource:hOp://www.youtube.com/watch?v=Suugn-p5C1MNewScienCstArCcle:hOp://technology.newscienCst.com/arCcle/dn13402 29. Agent-BasedModeling(ABM) ABMssimulateartiQicialsocietiesofautonomous,heterogeneous,andinteractingagents Modelinghumanbehavior Theapplicationoftheorytoguidebehavior ThePECSframeworktoimplementbehavior Representingcomplexsystems Modelingatthemicro-levelgeneratesmacro-behaviorsthatmayseemdifferentfromtheirorigins Modelingfromthebottom-upisakeyrequirementforemergenceOffersauniquewaytoaccountforthebehavior,heterogeneity,andinteractions(overphysicalandsocialspaces)ofsocialprocesses 30. GeographicInformationSystems(GIS) GISkeepstrackofwhereandwhenevents,activities,andthingsexist Enablesustobuildongeographicproblems Canrepresenttheworldasaseriesoflayersandobjects Usefulfordevelopingmodelenvironmentsthataregroundedinempiricaldata Usefulforvalidatingmacro-outcomesfrommicro-processes 31. SocialNetworkAnalysis(SNA) SNAstudiestherelationshipbetweenpeople,things,organizations,orevents Canmodeldynamicandevolvingrelationshipsthatarentnecessarilyphysicallynear Usefulforidentifyingkeyactorsanddeterminingsimilarities 32. ModelInitialization Kiberaismadeupof14neighborhoodsthattypicallycontainonedominantethnicgroup HouseholdsettlementdynamicsinmodelinspiredbySchelling(1978)segregationmodelEthnicity&Kikuyu&Luhya&Luo&Kalinjin&Kamba&Kisii&Meru&Mijikenda&Maasai&Turkana&Embu&Other& 33. TheModelingWorld TheEnvironment Empiricaldataisusedtocreatetheenvironmentonarastersurface Eachparcelcancontainonestructure TheAgents ResidentsofKibera HouseholdsareassignedahomebasedontheSchelling(1978)segregationmodelDatasources:theMapKiberaProject(Marras,2008),MapKibera(Hagen,2011)IntroductionABMIntegratingABM,SNA,GISHumanBehaviorRiotsConclusion 34. TheAgent-BasedModelDaily&Ac)vity&Scheduler:&Evaluate(Residents(mo0ves(against(a(set(of(factors(and(determine(the(ac0vity/ac0on(to(perform((Propagate(exogenous(rumor(to(an(ini0al(number(of(Residents(Read(in(spa0al(datasets(and(build(the(environment(Start(of(simula0on(Create(Resident(popula0on(based(on(environment(and(socio>economic(data(Place(Residents(in(Household(units(and(find(a(Home(based(on(neighborhood(preference(and(affordability(Build(model(displays(and(reporters(Schedule(Residents(and(Households(to(update(Create(Facili0es(from(file(and(add(Homes(and(Businesses(Step(Execute(Ac0on(Sequence(Establish(new(rela0onships(or(strengthen(exis0ng(rela0onships(between(interac0ng(Residents(Rumor&Propaga)on:&Residents(that(have(heard(the(rumor(exchange(informa0on(with(other(Residents(while(interac0ng(Iden)ty&Model:&Ac0vate(iden0ty(based(on(individual(characteris0cs(and(daily(ac0vi0es.(Perform(self>verifica0on(process.(Update(display,(graphs,(and(sta0s0cs(Yes(Was(self>verifica0on(process(successful?( No(Go(through(list(of(Residents(and(Households,(ini0alizing(them(in(random(order(un0l(each(is(ac0vated(Increase(Residents(Energy(Reservoir(Did(Resident(hear(rumor?(Decrease(Residents(Energy(Reservoir(and(calculate(aggression(level(Social&Influence&Model:&(Determine(Residents(Final(Opinion(on(rumor(based(on(Total(Interpersonal(Effects(No(Yes(Is(Residents(aggression>level(below(threshold(and(was(Resident(influenced(by(rumor(to(riot?(Yes( Riot(No(Yes( Write(final(report(to(file(End(of(simula0on?( No(Assign(Residents(to(employers(and(schools((Facili0es(and(Businesses),(and(a(School(Class(based(on(individual(characteris0cs(and(capacity(Intensity&Analyzer& 35. SocialInQluenceNetworkTheory InitialOpinion Measureofstructuralequivalence SusceptibilitytoinQluence!! = 1 1 1 + !! !!!!! Wheredi!!,isthedegreecentralityoftheResidentanddisthemeandegreecentralityoftheentirenetwork InterpersonalinQluence!!" = !!!!"/ ! !!", wherecijistheprobabilitythatthereisaninterpersonalattachmentbetweenResidentiandResidentj Finalopiniononissue! ! = !! !!! , whereWisthematrixofinterpersonalinQluence. 36. 0.000025#0.000020#0.000015#0.000010#0.000005#0.000000#1# 2# 3# 4# 5# 6# 7# 8# 9# 10# 11# 12# 13# 14# 15# 16# 17# 18# 19# 20# 21# 22# 23# 24# 25# 26# 27# 28#Chnage'in'Network'Density'Day'RiotingDynamicsIntroductionABMIntegratingABM,SNA,GISHumanBehaviorRiotsConclusion 37. IncreasingEmploymentandEducationConcurrently0.045%$0.040%$0.035%$0.030%$0.025%$0.020%$0.015%$0.010%$0.005%$0.000%$Employment$and$Educa;on$