- 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$
