copyCopyrightJASSS

RobertAguirreandTimothyNyerges(2014)

AnAgent-BasedModelofPublicParticipationinSustainabilityManagement

JournalofArtificialSocietiesandSocialSimulation 17(1)7lthttpjassssocsurreyacuk1717htmlgt

Received04-Sep-2012Accepted02-Jun-2013Published31-Jan-2014

Abstract

Thisarticlereportsonanagent-basedsimulationofpublicparticipationindecisionmakingaboutsustainabilitymanagementAgentsweremodeledassociallyintelligentactorswhocommunicateusingasystemofsymbolsThegoalofthesimulationwasforagentstoreachconsensusaboutwhichsituationsintheirregionalenvironmenttochangeandwhichonesnottochangeaspartofageodesignprocessforimprovingwaterqualityinthegreaterPugetSoundregionAsopposedtostudyingself-organizingbehavioratthescaleofalocalcommonsourinterestwasinhowonlinetechnologysupportstheself-organizingbehaviorofagentsdistributedoverawideregionalarealikeawatershedorriverbasinGeographically-distributedagentsinteractedthroughanonlineplatformsimilartothatusedinonlinefieldexperimentswithactualhumansubjectsWeusedafactorialresearchdesigntovarythreeinterdependentfactorseachwiththreedifferentlevelsThethreefactorsincluded1)thesocialandgeographicdistributionofagents(localregionalinternationallevels)2)abundanceofagents(lowmediumhighlevels)and3)diversityofpreconceptions(blankslateclonesocialactorlevels)WeexpectedthatincreasingthesocialandgeographicdistributionofagentsandthediversityoftheirpreconceptionswouldhaveasignificantimpactonagentconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingswhichwetraceallthewaybacktoourconceptualmodelandatheoreticalgapinsustainabilityscienceThetheoryofself-organizingresourceusersdoesnotspecifyhowagroupofsocialactorspreconceptionsaboutasituationisinterdependentwiththeirsocialandgeographicorientationtothatsituationWediscusstheresultsoftheexperimentandconcludewithprospectsforresearchonthesocialandgeographicdimensionsofself-organizingbehaviorinsocial-ecologicalsystemsspanningwideregionalareas

KeywordsSocialActorsPublicParticipationDecisionMakingSustainabilityManagementGeodesignGeographicInformationSystems(GIS)

TheThreeDomainsofSustainabilitySustainabilityScienceSustainabilityInformationScienceandSustainabilityManagement11 Awidelyacceptedtheoryisthatwhenpeoplearelefttotheirowndevicestheywillsimplyconsumetheresourcesattheirdisposalanddeterioratetheirenvironmentunlessgovernmentsimposeacontrol

systemtopreventanunavoidabletragedyofthecommonsOneoftheinterestingdevelopmentsinsustainabilityscienceistheemergenceofanalternatetheoryBasedonextensivecasestudiessustainabilitysciencefindsthathumanresourceusersmaysometimesself-organizeasacontrolsystemtomakesurethatthesocial-ecologicalsystemofwhichtheyareadependentpartremainsresilientinawaytheyprefer(Ostrom2009Agrawal2001)Systemstheoristshavelongheldthatcomplexsystemsexhibitthecapacitytoevolveinternalcontrolsystemsandessentiallyself-regulate(BennetandChorley1978)Infactsustainabilityscienceandenvironmentalhistoryhavebothshownthroughhistoricalcasestudiesthatgovernmentinterventionintotheself-organizingcapabilitiesofanotherwiseresilientsocial-ecologicalsystemsometimesacceleratesitsdeterioration(Earle1988)

12 Ostrom(2009)highlightedtensubsystemvariablesexplainingself-organizingbehaviorleadingtoasustainablesocial-ecologicalsystemOfparticularinteresttousarefoursubsystemvariablesthatscalethesocialandgeographicdimensionsofadecisionmakingsituationindifferentwaysThefourvariablesofinterestincludethesizeoftheresourcesystemthenumberofuserstheamountofknowledgesharingamongdifferentresourceusersmentalmodelsandfinallythelevelofimportanceoftheresourcetoeachuserTheprobabilitythatagroupofresourceuserswillself-organizeasacontrolsystemishigherwhenthesesubsystemvariablesfallwithinacertainrangeForexampleitisunlikelythatresourceuserswillself-organizeinsystemsspanningverylargeareasbecauseoftheburdensofmanagingextensiveflowsofresourcesOntheotherhanditisalsounlikelythatresourceuserswillself-organizeoververysmallareasthattypicallydonotgenerateflowsofsubstantialvalueInsumsustainabilityscienceholdsthatgiventhesizeoftheareaandthenumberofresourceusersthemorethatresourceusersareabletosharetheirmentalmodelsaboutthepreferredattributesofthesystemofwhichtheyareadependentpartandthemoreimportanttheresourceorecosystemserviceistotheusersthemselvesthemorelikelyasetofresourceuserswillinvesttimeandenergyinmanagingtheattributesoftheirsystemtomaintainapreferredstateoridentity

Figure1Threeknowledgedomainsofsustainability

13 Whenitcomestotheactualdesignandtestingofinformationtechnologyplatformstosupportself-organizingbehavioramonggeographically-distributedresourceusersonecanturntosustainabilityinformationscienceFigure1illustratesthethreeknowledgedomainsofsustainabilityincludingsciencetechnologyandmanagement(Katesetal2001Clark2007)Whilenotmeanttobemutuallyexclusivethesedomainsdescribethreekindsofexpertiseatworkinassessingwhatisknownaboutasocial-ecologicalsystemanddecidingwhethertointerveneAssessmentandinterventionisanorganizationalprocesswithmultiplerolesandfeedbackloopsHoweveratminimumtheprocessinvolvesatleastthreeactivitiesincludingmeasuringthepropertiesofreal-lifeelementsofasocial-

httpjassssocsurreyacuk1717html 1 16102015

ecologicalsystemsecondlyprocessingthosemeasurementsintoinformationandthenreasoningaboutrelationshipsbetweenelementstoexplaintheapparentcharacterstateoridentityofasystemasawholeandthenthirdlygeneratinganinformedunderstandingorconsensusabouthowtomanagecertainrelationshipssoastoensurethatthepreferredattributesofthesystemasawholeremainresilienttodisturbanceandchangeoverlongperiodsoftimeIdeallyanorganizationalprocessofassessmentandinterventioninvolvespublicparticipationandtakesintoconsiderationallaffected

parties[1]Regardlessofwhetherornotthetermsustainabilityisembracedasetofactivitiesaimedatchanginganexistingsituationintoamorepreferredonethatincludesfuturegenerationsasaffectedpartiessoastomeetpresentneedswithoutcompromisingtheabilityoffuturegenerationstomeettheirneedsrepresentsaspecialclassofgeodesignworkwecallsustainabilitymanagement(WECD1987)

14 Thepracticeofsustainabilityinvolvesatleastthreeoverlappingworkactivitiestodescribeassessandmanagetheresilienceofasocial-ecologicalsystem(WalkerandSalt2012)Ontheonehanddescribingasystemrequiresconceptualworkandliteracywiththemostenduringideasaboutsustainabilityandresilience(egseeAgrawal2001Beisneretal2003Cumming2011Liuetal200720072009)WecallthisexpertiseinthedomainofsustainabilityscienceOntheotherhandworkactivitiesspentmanagingasystemrequireahostofskillsrangingfromperformingtechnology-supportedworktodisplayingpersonalandprofessionalcompetenciesworkinginanorganizationalsettinglikeapublicagencyWecallthisexpertiseinthedomainofsustainabilitymanagementInbetweenthesetwoliesaspecialbodyofknowledgefocusedonthedesigntestingandimplementationofgeospatialinformationcapableofmodelingasocial-ecologicalsysteminsideofacomputerinordertobetterrepresentthepotentialconsequencesofchangingexistingsituationsintomorepreferredones(egKerstenetal2000Hiltyetal2005Campagna2006NRC2005Klinskyetal2010NRC2012)Wecallthislastbodyofknowledgeexpertiseinthedomainofsustainabilityinformationscience

15 Oneofthedilemmasfacedbyexpertsinsustainabilityinformationscienceisthatprovidinginformationforadecisionmakingsituationcansometimesdomoreharmthangooddrowningpeopleinaseaofinformationorgeneratingconflictsandconfusionbecausetheinformationprovideddoesnotmatchpreexistingconceptionshardenedbyexposuretodifferentinformation(NRC1996NRC2005)ThesechallengeswerewhatledHerbertSimon(19761981)tocallforascienceofinformationprocessingandascienceoftheartificialSimonsoughtageneralsetofrelationsdeterminingsuccessorbreakdowninanyworkflowmixingtwoverydifferentkindsofinformationprocessorsiepeoplewithdifferentlevelsofexpertiseontheonehandandcomputersontheotherInadditiontoextensiveargumentsinfavorofagent-basedmodelingSimonscallsforresearchhaveinspiredworkonsocialintelligencehuman-computer-human-interactionandsocial-computationalsystemsInterestinwhathasbeencalledparticipatorygeographicinformationscience(JankowskiandNyerges2001)hasbeensimilarlymotivatedOverthepastdecaderesearchersinparticipatorygeographicinformationsciencehavetriedtounderstandhowlargegroupsofpeoplecanusegeographicinformationtechnologytoaddressexistingspatialproblemsandimprovefuturewell-beingindecisionmakingsituationsallocatingpublicfundsforlandusetransportationandwaterresourcemanagement(NyergesandJankowski2010)LikewisegeodesignhasemergedasawayofthinkingabouthowtointegrateGISandmethodslikeagent-basedmodelingtoprovideinformationaboutchanginganexistingsituationintoapreferredonewherethespatialscaleofinterestspansbeyondneighborhoodsandurbangrowthareastowatershedandbasins(Steinitz2012)

16 EnhancingoverlapsbetweenthethreedomainsofsustainabilityisapracticalgoalPractitionersofsustainabilitymanagementregardlessoftheirchosensubstantiveareashouldbewell-versedinthemethodsofsustainabilityinformationscienceandtheconceptsofsustainabilityscienceTothatendtherearenowprofessionalgraduateprogramsliketheProfessionalMastersPrograminGIS(PMPGIS)forsustainabilitymanagementattheUniversityofWashingtonTheProfessionalMastersPrograminGISforsustainabilitymanagementattheUniversityofWashingtontakesthegreaterPugetSoundregionasalarge-scalefieldlaboratoryorcommonstoexploretheuseofmethodslikeagent-basedmodelingforsustainabilityscienceandsustainabilitymanagementSpeakingaboutthedriverspressuresstateimpactresponse(DPSIR)conceptualframeworktheWashingtonStateAcademyofSciences(2012)recentlystatedIfthemillionsofpeopleinthePugetSoundregioncouldberepresentedbyoneindividualmdashoronecollectivemindmdashthentheassumptionsthatunderpintheDPSIRmodelmightbearealisticrepresentationofinteractionsbetweenhumansandtheenvironmenthellipHumancommunitieshoweverarenotsimplythesumofatomisticindividualshellip[and]nosimplemodelcanmapsocietalcharacteristicsonenvironmentalpressuresInresponsetosentimentslikethataboveposedbytheWashingtonStateAcademyofSciencesweintegratedGISwithanagent-basedmodeltosimulatehowself-organizingbehaviormightemergeamongasociallyandgeographicallydiversesetofagentsfromthegreaterPugetSoundregionusingageodesignplatform

17 TheremainderofthepaperproceedsasfollowsInSection2wedescribethepropertiesofagentsandtheagent-basedmodelofpublicparticipationinageodesigndecisionmakingprocessInSection3wepresentourfactorialresearchdesigncallingfor27experimentaltreatmentsvaryingthesocialandgeographicdistributionofagentsthenumberofagentsandthediversityofagentpreconceptionsInSection4wepresentourfindingsfrom18ofthe27originallyplannedtreatmentsWeconcludeinSection5withfutureprospectsfordesigntestingandimplementationofagent-basedmodelingandonlineplatformsinthestudyandenablingofself-organizingbehavioramongsocialactorsgivenacommonresourcearea

ModelinganAgentObjectforPublicParticipationinDecisionMaking

21 Ourinterestinagent-basedmodelingcomesfromhavingworkedwithactualhumansubjectsintwofieldexperimentsoneconcerningregionaltransportationplanninginthecentralPugetSoundregionandtheothertheregionalimpactsofglobalclimatechangeontheOregoncoastEssentiallyanexperimentalresearchdesigninvolvinghundredsorthousandsofhumansubjectsrepeatedoverawidely-distributedareawouldbeimpossibleEsrisAgentAnalystisparticularlyinterestingforfutureeducationalpurposesgivenitsintegrationwithArcGISusingamiddlewareapproachandaprogramminglanguagecalledNotQuitePython(Brownetal2005Johnston2013)HoweverforthesimulationinthisarticlewechoseaJava-basedapplicationcalledAnyLogicbasedonourimpressionsofitscustomerandtechnicalsupportfornewusersathoroughtestofitsgraphicaluserinterfaceandfunctionalityandthefactthatitwaspromotedasoneoftheonlysystemsdesignedtoworkwithGISsoftwareandexternaldatabaseswhilesupportingsystemdynamicsdiscrete-eventandagent-basedmodeling

22 Webegantheprocessofdesigningandbuildingasimulationbyconsideringasinglecommon-sensenarrativestatement

Peoplemakedecisionsaboutsubstantivethingssuchascoursesofactionaimedatchangingexistingsituationsintosustainableonesthroughaprocessofparticipatorygroupinteraction

Similartosemanticmodelingorentity-relationshipmodelingweproceededbyparsingthenarrativestatementaboveintobasicentitiesandrelationshipsForexampleanygeneralorabstractnounthatfunctionsasasubjectobjectorpartofanounphrasecoulddescribeaclassofentityorrelationshipVerbsadjectivesandotherpartsofspeechcoulddescribeactionsorstatesofentitiesandrelationshipsWemadeabstractwordsdescribingreal-worldentitiesmorespecificbydistinguishingsubstantivelyrelevantclassesorsubtypesandmoreconcretebygivingentitiespropertiesorattributesbasedonarealisticdomainofvaluesAnimportantcaveatinconceptualmodelingisthatwhencarriedtologicalextremesmakingelementsandrelationshipsmorespecificandconcretedoesnotnecessarilyresultinamorerealisticcomputationalsimulationparticularlywhenitcomestomodelingcomplexsystemsApragmaticapproachbasedonasimplelinearmodelmayproduceacceptableresultswhencomparedwithrealityevenwhentheentitiesandrelationshipsinthemodeldonotfaithfullyrepresentwhatwewouldassumetobethetruecomplexityoftheentitiesandrelationshipsinthesystemunderinvestigation(BennetandChorley1978)

23 Parsingthesentenceaboveintoitscomponentpartsofspeechsuggestedfiveprincipalentitiesorrelationshipstoconsiderfortheagent-basedmodeloutlinedinbold

24 ThefirstentitytoconsiderispeoplethesubjectofthesentencewhichwedistinguishassocialactorentitieswithdifferentmentalmodelsTakingthewordsmakedecisionsthemainverbanditsobjectsocialactorentitiesusetheirmentalmodelstothinklearnandmakedecisionsthroughaprocessofanalysisanddeliberationusingsymbolsofcommunicationThewordssubstantivethingsanounphraserightafterthemainverbrepresentswhatsocialactorentitiesarethinkinglearningormakingdecisionsaboutthroughtheiruseofsymbolsreferringtoanysetofreal-lifeentitiesandrelationshipscomposingasituationwithinthesocial-ecologicalsystemofwhichthesocialactoritselfisacomponentpartForhumansocialactorsasituationrepresentsanyreal-lifesocial-ecologicalrelationshiptowhichthatsocialactoralsohasacertainsocialandgeographicorientationorstakeSocialactorsmaybedirectusersorharvestersofsometangibleresourceproducedbyasituationortheymaybeanindirectbeneficiaryofanintangibleresourceecosystemserviceorsocialsavingsproducedbyasituationThefourthpotentialelementofthesimulationcomesfromthewordsaprocessofparticipatorygroupinteractionanothernounphraseTheparticipatorygroupprocesswasmodeledasagentsfilteringsortingandreasoningabouteachothersuseofsymbolsthroughanonlineplatformspecificallydevelopedtosupportthesix-stepprocesstypicallyconvenedingeodesign(Steinitz2012)Thefifthandlastelementcomesfromthewordsinaspatialandtemporalcontextanounphraseweaddedattheendincurlybracketsinparttosimplycovereverythingelsebutalsoasawayofjustifyinguseofasimulatedclient-servereventlogasourprimarysetofobservationsasdescribedinAguirreandNyerges(2011)andNyergesandAguirre(2011)

httpjassssocsurreyacuk1717html 2 16102015

Figure2AnagentactiveobjectclasswhosepropertiesstatesandbehaviorsareimplementedinAnyLogicasparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsorpresentations

Figure3AnexampleofastatechartinUMLforRealTime(UML-RT)usedtoimplementagentstatesandtransitions

httpjassssocsurreyacuk1717html 3 16102015

Figure4AnexampleofanAnyLogicactionchartusedtoimplementagentsinteractionswithsymbols

25 AfterparsinganarrativestatementintomodelelementsweimplementedsocialactoragentsasanactiveobjectclassinAnyLogicWithinthatactiveobjectclasswedefinedagentpropertiesstatesandbehaviorsusingthesoftwarefeaturesofAnyLogicincludingparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsandpresentations(seeAnyLogic2013)Thereareanumberofstandardsfordocumentinganagent-basedmodeltoensureitsreproducibilitySuchstandardsincludeentity-relationshipdiagramsUnifiedModelingLanguage(UML)diagramsvariousotherobject-oriented(OO)diagrammingtechniquesandtheOverviewDesignconceptsandDetails(ODD)protocolforagent-basedmodels(Grimmetal2010Polhill2008)FormattersofeaseofproductionanddetailwedocumentedthephysicalimplementationofthemodelitselfwiththedocumentationtoolsavailableinAnyLogicTheAnyLogicdocumentationtoolslistthecompletedescriptionsofallmodelelementsegparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsgraphicsetcinPDFDOCXorHTMLformforeaseofdistribution

26 Figure1isaschematicrepresentationdescribinghowagentswereimplementedinAnyLogicasanactiveobjectclassStatechartsweremodeledusingcomputableUnifiedModelingLanguageforRealTime(UML-RT)diagramsFigure2isanillustrativeexampleoftheUML-RTstatechartusedtospecifyandimplementagentbehavioralstatesandrulesfortransitionsbetweenstatesduringthesimulationForinstanceinFigure2afteranagenttransitionsfromastateofbeingloggedintotheonlineplatform(stateA)tobeingactive(stateA1)tobeingreadytocreatedeliberativecontentintheformofavotepostorreply(stateA1basmarkedwithanasterisk)consequentlytheyenteranactionchartthatdetermineswhatkindofdeliberativebehaviortheywilllikelytakeActionchartsarestructuredprogrammingblocksthatimplementcodesnippetsusinggraphicalJavaoperatorsFigure3isanexampleofanactionchartimplementingvotingbehaviorforasocialactoragentoperatinginanexecutive(EX)mentalmodelwhichitselfwasimplementedasaJavacollectionIntheactionchartinFigure3thereisanequalchancetheagentwilleithervoteinfavorofsituationsthatbestmatchtheirpreconceptionsorvoteagainstthosethatleastmatchtheirpreconceptionsFurtherexamplesinthepaperprovideillustrativeexamplesofagentobjectvotingbehaviorwhereasfulldetailsaboutstatecharttransitionrulesandactionchartalgorithmsusedinthesimulationareavailableinourmodeldocumentation

ResearchDesignforaSimulatedOnlineFieldExperiment

31 AprimeconcerninexperimentalresearchislimitingthenumberofvariablesbeingconsideredallatonceForexampleinafactorialresearchdesignthenumberofdifferenttreatmentsrequiredequalsthecross-productofthenumberofinterdependentfactorsbeingconsideredBasedonthetheoryofself-organizingbehaviorinsustainabilitysciencewetookfoursubsystemvariablesofinterestincludingsizeoftheresourcesystemthenumberofuserstheamountofknowledgesharingamongdifferentresourceusersmentalmodelsandthelevelofimportanceoftheresourcetoeachuserandthendevelopedthreesimplesetsofagent-basedproperties

SocialampGeographicPropertiesAgentshaveacertainsocialandgeographicorientationtosituationsintheirenvironmentConceptualPropertiesAgentscarrypreconceptionsorganizedintomentalmodelswhichtheyusetoreasonaboutsituationsintheirenvironmentSymbolicPropertiesAgentsaresociallyintelligentandcancommunicatetheirpreconceptionstooneanotherusingasystemofsymbols

32 Eachsetofpropertieswerefurthercategorizedintothreelevelsandanumberofqualificationshadtobemadewhenitcametoimplementingthepropertiesofagentobjectsinarelationaldatabaseintegratedwiththeagent-basedmodel(Figure6)explainedinmoredetailbelowThususingafactorialresearchdesignaftercross-tabulatingthreeinterdependentfactorseachwiththreedifferentlevelstheresultwas27experimentaltreatmentsnotincludingparametervariationexperimentsandreplicationexperimentstoevaluaterandomeffects

SocialampGeographicPropertiesofAgents

33 Ourfirsttaskwastocreateapopulationofagentswithsocialandgeographicpropertiesandthensettargetvaluesforrecruitingacertainnumberoftheseagents(lowmediumandhigh)fromwithintheboundariesofregionalareasrepresentingaresourcesystem(localregionalandinternational)WeestablishedtheboundariesrepresentinglocalregionalandinternationalareasusingacombinationofpoliticaljurisdictionsanddrainageareasandthenusedArcGIStogenerateapopulationofpotentialagentsinWashingtonStateandBritishColombiaCanada(Figure4)ThelocalscaleforthesimulationwasanareaformedbytheninecountiesintersectingthewatershedsofthegreaterPugetSoundregionofWashingtonStateincludingtheCityofSeattleandKingCountyencompassing228strata(ZCTAs)withapopulationof37millionpeopleintheyear2000Theregionalscaleforthesimulationwasanareacreatedbythe85majorwatersheds(areasconformingtoan8-digitHUCorUSGShydrologicunitcodeandCanadianequivalents)contributingtothewaterbodydefinedastheSalishSeawhichencompassed804strata(ZCTAsandCSDs)withatotalpopulationof71millionFinallytheinternationalscalewasWashingtonStateandBritishColumbiaencompassing1423strata(ZCTAsandCSDs)withatotalpopulationof98millionTorepresentapopulationofagentsweusedcountsfromthemosteasilyavailableyeartheyear2000enumeratedinzipcodetabulationareas(ZCTAs)intheUnitedStatesandcensussubdivisions(CSDs)inCanadaWethenusedthecentroidsofeachZCTAandCSDasthecoordinatelocationforeachagentobjectinstanceinthesamewayweusedself-reportedzipcodeinformationtorepresentthelocationofhumansubjectsinpriorexperiments(NyergesandAguirre2011AguirreandNyerges2011)Lastlywesettargetvaluesforlowmediumandhighnumbersofagentsatapproximately25100and1000respectively

Figure5Mapsillustratingthethreedifferentscalesofagentdistribution(localregionalinternational)usedintheexperiment

httpjassssocsurreyacuk1717html 4 16102015

Figure6MapshowingadetailedviewoftheregionalscaleofthesimulationThegrayarearepresentscoastalandfluvialdrainagebasinsemptyingintotothewaterbodydefinedastheSalishSeaThetotalpopulationofeachgeographicstrata(ZCTAsandCSDs)availableforsamplingarerepresentedasproportionalsizesymbolsAffectedpartypreferencesofsocialactorsarerepresentedasa

colorrangefromblue(moreorientedtothecoast)tored(lessorientedtothecoast)

34 Gastiletal(2007)suggestusingaCitizenJuryrecruitmentstrategyusingsmallrandomly-selectedgroupsasrepresentativeoflargerpopulations(seealsoFerguson2007)TheJeffersonCenter(2009)similarlyusedrandomlysampledparticipantsasrepresentativegroupsonthebasisofdemographiccharacteristicsOtherauthorsadvocatenon-randomlysampledgroupsofparticipantspointingoutfromsomewhatanecdotalevidencethatparticipationworkedbestwhenparticipantswerenominatedbytheircommunitytorepresenttheirpreferencesorbeliefs(CarsonandMartin2002Rayner2003)Stillotherspointouttherealityofonlinesituationsintermsofbeingstuckwithnon-randomlyselectedparticipantsakasamplesofconveniencewhicharenotlikelytoberepresentativeofanyparticulargrouporgeographicarea(KonstanandChen2007)

35 Ourrecruitmentstrategywasbasicallytouseageographically-stratifiedsampleandcreatethreelevelsofagentabundance(highmediumlow)usingamodelstwoformsofpoliticalrepresentationintheUnitedStatesCongressTorecruitthelowlevelofapproximately25fromourpopulationweusedamodelsimilartopoliticalrepresentationtheUSSenatebyselectingoneagentfromeachmajorsubdivision(egcountyorwatershed)beginningwiththemostpopulatedZCTAorCSDTorecruitmediumandhighlevelsofapproximately100and1000agentsweusedadifferentmodelmorelikethecongressionaldistrictsintheUSHouseofRepresentativesselectingagentsproportionaltothepopulationofeachminorsubdivision(egzipcodetabulationareaorCanadiancensussubdivision)

36 AsnotedagentsusesymbolstocommunicatetheirmentalmodelsaboutsituationsintheirenvironmentForhumansocialactorentitiesasituationisanysetofsocial-ecologicalentitiesorrelationshipstowhichthesocialactorhasanindividualsocialandgeographicorientationAsocialactorsorientationwithrespecttothosereferentsmightbeperceivedintermsofadirectbenefitorresourceproducedbythatsituationoritmightbeperceivedasanindirectparallelorinducedbenefitorservicederivedfromasituationLikewiseasocialactorsorientationmaybebasedontheirperceptionofadirectorindirectbenefitfromasituationoralternativelyintermsofthatsocialactorsoccupationintermsofapublicagencysjurisdictionoverasituationMentalmodelshavebeenoflongstandinginterestinsustainabilityscience(egseeMathevetetal2011)Howeverlessinfluentialinsustainabilitysciencearegeohistoricalsocialscienceperspectivesthatdemonstratethecontemporarysocialandpoliticalmanifestationsstemmingfromthelong-terminfluenceofsocialandgeographiciemaritime-commercialversusterritorial-administrativeorientationtoeverydayflowsofgoodsandmaterialspeoplefinanceandinformation(Fox19711980Braudel1972)Discussionofthegeohistoricalsocialscienceliteratureisbeyondtheintentofthisarticlebutitbearsmentionintermsofcallsforreunifyingsocialandbehavioralsciencewithsocialtheoryincomputationalcognitivemodeling(Conte2002)NonethelesswithsuchgeneraltheoreticalinsightsinmindweusedGIStocalculatearudimentarysocial-geographicorientationorlevelofaffectednesswithrespecttothegreaterPugetSoundandSalishSearegionasaproductofdistancefromthecoastmultipliedbyelevationabovesealevel(seetheattributeORIENTATIONinFigure6)

Figure7SchematicrepresentationoftherelationaldatabaseusedinthesimulationrepresentingsomeofthekeytablesandattributesoftheagentobjectclassSeeFigure8foravisualizationofthementalmodeltables

ConceptualPropertiesofAgents

37 Agentsoperatedwithoneofthreemodeswithrespecttotheirpreconceptions(blankslateclonesocialactor)Atthefirstlevelagentsoperateinblankslatemode(Figure7)InblankslatemodeagentsbeginwithnopreconceptionsaboutanythingbeingneutralwithrespecttoeverysituationregardlessofthementalmodelAtthesecondlevelagentsoperateinclonemodeInclonemode

httpjassssocsurreyacuk1717html 5 16102015

agentsbegintheexperimentwithdifferentpreconceptionsdependingonthesituationbuttheyallhaveexactlythesamepreconceptionstothesamesituationsAtthethirdandmostdiverselevelofpreconceptionsagentsoperateinfullsocialactormodeInsocialactormodeeachagentobjectinstancecarriesadifferentsetofpreconceptionsforeachsituationAgentmentalmodelswereintegratedwiththeagent-basedmodelusingarelationaldatabase

38 InhisclassicstudyoforganizationaldecisionmakingThompson(1967)suggestedtherearetwokindsofuncertaintieswhenpeoplemakedecisionsaboutchanginganexistingsituationintoapreferredoneOnekindofuncertaintysurroundsbeliefsaboutthecauseandeffectrelationsthatproducethecurrentsituationormightproduceapreferredsituationinthefutureTheotherkindofuncertaintyisaboutpreferencesaboutwhichfutureoutcomesaremoredesirable(seealsoLaveandDowlatabadi1993)ElaboratingonThompsons(1967)twokindsofuncertaintywedevelopedthreedifferentkindsofsocialactorpreconceptionsinvolvingbeliefspreferencesorassessmentsWebasedourchoiceofthreekindsofpreconceptionsonbroadsummariesofthedecisionmakingliteraturethattypicallyidentifythreekindsofsocialactorswithslightlydifferentpreconceptions(egNRC19962005)inadditiontooccasionalcasestudiesaboutparticipatorydecisionmakingforsustainabilitymanagementthatconfirmthreesocialactormentalmodels(egDelgadoetal2009)

39 AgentscarrythreekindsofpreconceptionsThefirstkindofpreconceptionistheaffectedparty(AP)orstakeholderpublicmentalmodelthatlooksatasituationfromtheperspectiveofthedesirabilityofchangingsomeexistingsituationintoamorepreferredone(ieintolerableundesirableacceptabledesirableandindispensable)Anotherkindofpreconceptionisthetechnicalspecialist(TS)mentalmodelthatlooksatasituationintermsofbeliefsabouttheplausibilitythatsomesetofcauseandeffectrelationsproducedthecurrentlyexistingsituationorcouldproducesomefuturesituation(ieunimaginableimplausibleconceivableplausibleandcertain)Finallythethirdkindofpreconceptionwastheexecutive(EX)mentalmodelthatlooksatchangingtheexistingsituationtoafuturesituationfromtheperspectiveoffeasibilityassessment(ieunrealisticinfeasiblepossiblefeasibleandpractical)

310 Figure8illustrateshoweachsocialactormentalmodelwascontrolledusingadistinctcolorpatterninarasterdatastructureInthecaseoftheaffectedparty(AP)preconceptionsdifferedfromlowerrighttoupperleftinthiscasefromalowpreconceptioncoloredredrepresentinganintolerablesituationtothehighestpreconceptioncoloredgreenrepresentinganindispensablesituationWebuiltintoourassumptionsthatexecutiveswillgenerallyattempttobalanceaffectedpartyandtechnicalspecialistpreconceptionswhenassessingthefeasibilityofanyparticularprojectprogramorplanaimedatchanginganexistingsituationintoapreferredoneThustheexecutivementalmodelwascalculatedusingrastermathematicsinGISbasedonthetechnicalspecialistmentalmodelandtheaverageofallpreferencesoftheaffectedpartieswithinthejurisdictionalboundaryareatheexecutiveissupposedtorepresentegacountyAsnotedwealsocreatedfourdifferentlevelsofexpertiseforeachagentobjectoperatinginsocialactormodeinordertofurtherdifferentiatewithinaffectedparty(AP)technicalspecialist(TS)andexecutivedecisionmaker(EX)mentalmodelsonthebasisoftheirlevelofaffectednessexpertiseandauthorityresultinginatotalof12differentmentalmodels(seeFigure7)

311 ThethreesetsofsocialactorpreconceptionsdonotdefinethreedifferentagentsForexampleinrealitythesamehumansocialactormaytendtoreasonforthemostpartusingtheiraffectedpartypreferencesbutattimesmayswitchmentalmodelsandconsiderthesamesituationbasedontheirbeliefsorassessmentsTheinterestingcomplexitywhenitcomestotheinteractionsofthesementalmodelsiswhensituationsaredeemedindispensablebyaffectedpartiesbutonlyconceivablebytechnicalspecialistsandinfeasiblebyexecutivesInotherwordsthesamesocialactormaypreferacertainfuturesituationbutmayalsoattheverysametimeunderstandthattheirownpreferencesareunlikelygiventhetimeandresourcesneeded

312 ThuseveryinstanceoftheagentobjectclasscarriesallthreepreconceptionsHowevereachinstanceoftheagentobjectclassalsocarriesauniqueprobabilityortendencytofavoronesetofpreconceptionsoverothersatanygiventimesimilartoafuzzysetForexampleanagentmighthaveanaffectedpartyprobability(AP_PROBinFigure7)of075atechnicalspecialistprobabilityof02(TS_PROBinFigure7)andanexecutiveprobabilityof005(EX_PROBinFigure7)Thereforethisparticularagentwilltendtoreasonaboutasituationbasedontheiraffectedpartypreferencesonaveragethreeoutofeveryfourtimestheyencounterasymbolandrespondaccordinglywhenvotingpostingorreplyingintheonlineplatform

313 Inrealhumansubjectspreconceptionsareoftenmeasuredinordinallevelsofmeasurementfromaquestionnaireorsimilarself-reportmeasureaskingparticipantstoranktheiragreementordisagreementonaLikert-typeitemscaleOriginallyweassignedagentspriorpreconceptionsintherelationaldatabaseasintegerswithpermissiblevaluesrangingfrom1to5correspondingtofiveLikert-typecategoriesWethenconvertedthemtorealnumberseganormalizedrealnumberscalerangingfromhighlynegative(000)tohighlypositive(100)similartopersonalprobabilities(Kahnemanetal1982)inordertostorethemasJavacollectionsinAnyLogicalthoughitbecomesquestionablewhetherpreconceptionsshouldbestoredusingrealnumbervaluesmoreprecisethanthenearesttenthofadecimalpoint

314 Similartootherapproachesthathaveattemptedtoorganizethementalmodelsofsometimesverylargepopulationsofagents(VogtandDivina20052007ChaoqingandPeuquet2009Vogt2009)westructuredmentalmodelsasarasterorgriddatastructureinaGISUsingthementalmodeldatastructureinFigure8tovisualizeagentpreconceptionsthebalanceofgreenversusredcolorpatternsreflectsthebalanceofinfluencebetweenaffectedpartypreferencestechnicalspecialistbeliefsandexecutiveassessmentsForexampleinFigure8thecolorpatternintheaffectedpartymentalmodelcarriedbyeachagentrangesfrommostpreferred(green)toleastpreferred(red)inagenerallyupperlefttolowerrightcolorgradientrepresentingdifferentpreferencesofmorecoastalversusmoreinteriororientatedagents(seealsoORIENTATIONinFigure7)Thecolorpatterninthetechnicalspecialistmentalmodelcarriedbyeachagentrangesfromleastbelievable(red)tomostbelievable(green)infourdistincthotspots(Figure8)Finallyinasomewhatmorecomplicatedschemethecolorpatternintheexecutivementalmodelcarriedbyeachagentrangesfromleastfeasible(red)tomostfeasible(green)bybalancingonthehandtheaffectedpartypreconceptionsofagentsfromtheexecutivespoliticaljurisdictionandontheotherhandthebasetechnicalspecialistpreconceptionsAsnotedtheexecutivementalmodelofwhatismostfeasibleisliterallyamathematicalcompromisebetweenwhatismorepreferredbytheaffectedpartieswithintheexecutivejurisdictionversuswhatismorebelievableaccordingtothetechnicalexperts(Figure8)Ideallyanyvisualanalystcanlookatacolorpatternandvisualdetectpossiblysupportedbysimplespatialstatisticsifanexperimentaloutcomewasinfluencedmorebyaffectedpartypreferencestechnicalspecialistbeliefsorabalancingofthetwobyexecutiveassessments

Figure8Social-actorsmentalmodelasvisualizedinaGISasarasterdatastructure

ChangesintheConceptualPropertiesofAgents

315 EachinstanceoftheagentobjectclasscarriesauniquecapacitytoupdateitspreconceptionsbylearningfromotheragentsandexperiencingconceptualchangeAccordingtoBayesiantheoriesoflearningthedegreetowhichapersonbelievesapropositionistruedependsonthepriorpreconceptionsthatapersonhasinthetruthofthepropositionandtheevidencecollectedtoinvestigatethatproposition(Dempster1968KingandGolledge1969GolledgeandStimson1997DaviesWithers2002CatenacciandGiupponi2010)TheBayesiantheoryoflearningcanbemathematicallydescribedasafunctionofexistingpreconceptions(Heckerman1996Robinson2003)theinherentcredibilityofaparticularelementofinformation(Flach1999)andtheavailabilityorexposuretoapieceofinformationbyeachparticipant(Acemogluetal2010)Weassumedthatthementalmodeltowhichtheagentwasmosthighly-orientedwouldbemoreresistanttoupdatingieamentalmodelbuiltupoverlongperiodsofexposuretocredibleinformationInotherwordsifanagentwaslikelytoreasonwithanaffectedpartymentalmodelthenthatagentobjectwouldcarryaproportionallylowprobabilitytoupdatetheiraffectedpartymentalmodelTocalculateconceptualchangeandlearningweusedtheLaplacian-correctedBayesianalgorithmbasedonitssuccessfulimplementationasaSPAMfilteringalgorithm(seeRobinson2003)ThealgorithmweusedcodedasanactionchartinAnyLogicupdatedanagentspreconceptionsinthesamemannerthatabasicSPAMfilterworksbasedonthecredibilityofthemessageandrepeatedexposuretocertainelementsofamessage(Robinson2003)AfterallofanagentspreconceptionsareupdatedtonewvaluesasspecifiedbyouralgorithmbysubtractingthedifferencesbetweentheimmediatelypriorandthenewlyupdatedvaluesofamentalmodelwewereabletocalculateanagentobjectsconceptualchangeWhenwesumallindividualagentconceptualchangesoverthecourseoftheentiredecisionsituationwecalledthatsumameasureofsociallearning

316 Whatdeterminesifahumansocialactorwillactuallylearntherebyupdatingtheirpreconceptionsandundergoingaconceptualchangeremainsamatteroftheoreticaldebatewithinthecognitivesciences(Chateretal2006a2006b2006c)andagent-basedsimulationsaswell(Lempert2002Ramanath2004Sun2006Kimetal2010BarreteauandLe2011Kim2011Squazzoni2012)ItisalreadyunderstoodthatBayesiantheoriesoflearningareverysensitivetothesimplifyingassumptionsresearchersmakeaboutpreconceptions(DaviesWithers2002)NotsatisfiedthatwecouldprovidetheanswertothesetheoreticalandmethodologicalquestionswedecidedthatwewouldconductaparametervariationexperimentthatvariedthelevelofchangeeachagentobjectinstancecouldundergoAglobalconceptualchangevalueof00meantthatallagentspossessedarigidmentalmodelthatneverchangedwhereasavalueof10meantthatanygivenagentwasallowedtoexperienceconceptualchangeaccordingtoauniqueagent-basedprobabilityforexperiencingconceptualchange(egAP_LEARNinFigure7)

SymbolicPropertiesofAgents

317 Sociallyintelligentagentscommunicatetheirpreconceptionstooneanotherusingasystemofsymbols(Conte2002)Anumberoftheoreticalandphilosophicalperspectivesabouthowactorsinteractandinfluenceoneanotherthroughcommunicationandlanguagelikesemioticssymbolicinteractionismorthephilosophyofmindpointtotheimportanceofreasoningaboutsymbolsthatstandforaconceptinonesmindasappliedtoasetofreferentsintheworld(PeirceNDSperber19851990Auspitz1994Hilpinen1995Sowa2000ManciniandShum2006Sowa2006Hilpinen2007)Interestinglyatleastoneassessmentsuggeststhatsimulationtoolsarelackingwhenitcomestoviewingorvisualizinginformationexchangesbetweenagentsinanagent-basedmodel(Ralambondrainyetal2007)

httpjassssocsurreyacuk1717html 6 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

Figure2AnagentactiveobjectclasswhosepropertiesstatesandbehaviorsareimplementedinAnyLogicasparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsorpresentations

Figure3AnexampleofastatechartinUMLforRealTime(UML-RT)usedtoimplementagentstatesandtransitions

httpjassssocsurreyacuk1717html 3 16102015

Figure4AnexampleofanAnyLogicactionchartusedtoimplementagentsinteractionswithsymbols

25 AfterparsinganarrativestatementintomodelelementsweimplementedsocialactoragentsasanactiveobjectclassinAnyLogicWithinthatactiveobjectclasswedefinedagentpropertiesstatesandbehaviorsusingthesoftwarefeaturesofAnyLogicincludingparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsandpresentations(seeAnyLogic2013)Thereareanumberofstandardsfordocumentinganagent-basedmodeltoensureitsreproducibilitySuchstandardsincludeentity-relationshipdiagramsUnifiedModelingLanguage(UML)diagramsvariousotherobject-oriented(OO)diagrammingtechniquesandtheOverviewDesignconceptsandDetails(ODD)protocolforagent-basedmodels(Grimmetal2010Polhill2008)FormattersofeaseofproductionanddetailwedocumentedthephysicalimplementationofthemodelitselfwiththedocumentationtoolsavailableinAnyLogicTheAnyLogicdocumentationtoolslistthecompletedescriptionsofallmodelelementsegparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsgraphicsetcinPDFDOCXorHTMLformforeaseofdistribution

26 Figure1isaschematicrepresentationdescribinghowagentswereimplementedinAnyLogicasanactiveobjectclassStatechartsweremodeledusingcomputableUnifiedModelingLanguageforRealTime(UML-RT)diagramsFigure2isanillustrativeexampleoftheUML-RTstatechartusedtospecifyandimplementagentbehavioralstatesandrulesfortransitionsbetweenstatesduringthesimulationForinstanceinFigure2afteranagenttransitionsfromastateofbeingloggedintotheonlineplatform(stateA)tobeingactive(stateA1)tobeingreadytocreatedeliberativecontentintheformofavotepostorreply(stateA1basmarkedwithanasterisk)consequentlytheyenteranactionchartthatdetermineswhatkindofdeliberativebehaviortheywilllikelytakeActionchartsarestructuredprogrammingblocksthatimplementcodesnippetsusinggraphicalJavaoperatorsFigure3isanexampleofanactionchartimplementingvotingbehaviorforasocialactoragentoperatinginanexecutive(EX)mentalmodelwhichitselfwasimplementedasaJavacollectionIntheactionchartinFigure3thereisanequalchancetheagentwilleithervoteinfavorofsituationsthatbestmatchtheirpreconceptionsorvoteagainstthosethatleastmatchtheirpreconceptionsFurtherexamplesinthepaperprovideillustrativeexamplesofagentobjectvotingbehaviorwhereasfulldetailsaboutstatecharttransitionrulesandactionchartalgorithmsusedinthesimulationareavailableinourmodeldocumentation

ResearchDesignforaSimulatedOnlineFieldExperiment

31 AprimeconcerninexperimentalresearchislimitingthenumberofvariablesbeingconsideredallatonceForexampleinafactorialresearchdesignthenumberofdifferenttreatmentsrequiredequalsthecross-productofthenumberofinterdependentfactorsbeingconsideredBasedonthetheoryofself-organizingbehaviorinsustainabilitysciencewetookfoursubsystemvariablesofinterestincludingsizeoftheresourcesystemthenumberofuserstheamountofknowledgesharingamongdifferentresourceusersmentalmodelsandthelevelofimportanceoftheresourcetoeachuserandthendevelopedthreesimplesetsofagent-basedproperties

SocialampGeographicPropertiesAgentshaveacertainsocialandgeographicorientationtosituationsintheirenvironmentConceptualPropertiesAgentscarrypreconceptionsorganizedintomentalmodelswhichtheyusetoreasonaboutsituationsintheirenvironmentSymbolicPropertiesAgentsaresociallyintelligentandcancommunicatetheirpreconceptionstooneanotherusingasystemofsymbols

32 Eachsetofpropertieswerefurthercategorizedintothreelevelsandanumberofqualificationshadtobemadewhenitcametoimplementingthepropertiesofagentobjectsinarelationaldatabaseintegratedwiththeagent-basedmodel(Figure6)explainedinmoredetailbelowThususingafactorialresearchdesignaftercross-tabulatingthreeinterdependentfactorseachwiththreedifferentlevelstheresultwas27experimentaltreatmentsnotincludingparametervariationexperimentsandreplicationexperimentstoevaluaterandomeffects

SocialampGeographicPropertiesofAgents

33 Ourfirsttaskwastocreateapopulationofagentswithsocialandgeographicpropertiesandthensettargetvaluesforrecruitingacertainnumberoftheseagents(lowmediumandhigh)fromwithintheboundariesofregionalareasrepresentingaresourcesystem(localregionalandinternational)WeestablishedtheboundariesrepresentinglocalregionalandinternationalareasusingacombinationofpoliticaljurisdictionsanddrainageareasandthenusedArcGIStogenerateapopulationofpotentialagentsinWashingtonStateandBritishColombiaCanada(Figure4)ThelocalscaleforthesimulationwasanareaformedbytheninecountiesintersectingthewatershedsofthegreaterPugetSoundregionofWashingtonStateincludingtheCityofSeattleandKingCountyencompassing228strata(ZCTAs)withapopulationof37millionpeopleintheyear2000Theregionalscaleforthesimulationwasanareacreatedbythe85majorwatersheds(areasconformingtoan8-digitHUCorUSGShydrologicunitcodeandCanadianequivalents)contributingtothewaterbodydefinedastheSalishSeawhichencompassed804strata(ZCTAsandCSDs)withatotalpopulationof71millionFinallytheinternationalscalewasWashingtonStateandBritishColumbiaencompassing1423strata(ZCTAsandCSDs)withatotalpopulationof98millionTorepresentapopulationofagentsweusedcountsfromthemosteasilyavailableyeartheyear2000enumeratedinzipcodetabulationareas(ZCTAs)intheUnitedStatesandcensussubdivisions(CSDs)inCanadaWethenusedthecentroidsofeachZCTAandCSDasthecoordinatelocationforeachagentobjectinstanceinthesamewayweusedself-reportedzipcodeinformationtorepresentthelocationofhumansubjectsinpriorexperiments(NyergesandAguirre2011AguirreandNyerges2011)Lastlywesettargetvaluesforlowmediumandhighnumbersofagentsatapproximately25100and1000respectively

Figure5Mapsillustratingthethreedifferentscalesofagentdistribution(localregionalinternational)usedintheexperiment

httpjassssocsurreyacuk1717html 4 16102015

Figure6MapshowingadetailedviewoftheregionalscaleofthesimulationThegrayarearepresentscoastalandfluvialdrainagebasinsemptyingintotothewaterbodydefinedastheSalishSeaThetotalpopulationofeachgeographicstrata(ZCTAsandCSDs)availableforsamplingarerepresentedasproportionalsizesymbolsAffectedpartypreferencesofsocialactorsarerepresentedasa

colorrangefromblue(moreorientedtothecoast)tored(lessorientedtothecoast)

34 Gastiletal(2007)suggestusingaCitizenJuryrecruitmentstrategyusingsmallrandomly-selectedgroupsasrepresentativeoflargerpopulations(seealsoFerguson2007)TheJeffersonCenter(2009)similarlyusedrandomlysampledparticipantsasrepresentativegroupsonthebasisofdemographiccharacteristicsOtherauthorsadvocatenon-randomlysampledgroupsofparticipantspointingoutfromsomewhatanecdotalevidencethatparticipationworkedbestwhenparticipantswerenominatedbytheircommunitytorepresenttheirpreferencesorbeliefs(CarsonandMartin2002Rayner2003)Stillotherspointouttherealityofonlinesituationsintermsofbeingstuckwithnon-randomlyselectedparticipantsakasamplesofconveniencewhicharenotlikelytoberepresentativeofanyparticulargrouporgeographicarea(KonstanandChen2007)

35 Ourrecruitmentstrategywasbasicallytouseageographically-stratifiedsampleandcreatethreelevelsofagentabundance(highmediumlow)usingamodelstwoformsofpoliticalrepresentationintheUnitedStatesCongressTorecruitthelowlevelofapproximately25fromourpopulationweusedamodelsimilartopoliticalrepresentationtheUSSenatebyselectingoneagentfromeachmajorsubdivision(egcountyorwatershed)beginningwiththemostpopulatedZCTAorCSDTorecruitmediumandhighlevelsofapproximately100and1000agentsweusedadifferentmodelmorelikethecongressionaldistrictsintheUSHouseofRepresentativesselectingagentsproportionaltothepopulationofeachminorsubdivision(egzipcodetabulationareaorCanadiancensussubdivision)

36 AsnotedagentsusesymbolstocommunicatetheirmentalmodelsaboutsituationsintheirenvironmentForhumansocialactorentitiesasituationisanysetofsocial-ecologicalentitiesorrelationshipstowhichthesocialactorhasanindividualsocialandgeographicorientationAsocialactorsorientationwithrespecttothosereferentsmightbeperceivedintermsofadirectbenefitorresourceproducedbythatsituationoritmightbeperceivedasanindirectparallelorinducedbenefitorservicederivedfromasituationLikewiseasocialactorsorientationmaybebasedontheirperceptionofadirectorindirectbenefitfromasituationoralternativelyintermsofthatsocialactorsoccupationintermsofapublicagencysjurisdictionoverasituationMentalmodelshavebeenoflongstandinginterestinsustainabilityscience(egseeMathevetetal2011)Howeverlessinfluentialinsustainabilitysciencearegeohistoricalsocialscienceperspectivesthatdemonstratethecontemporarysocialandpoliticalmanifestationsstemmingfromthelong-terminfluenceofsocialandgeographiciemaritime-commercialversusterritorial-administrativeorientationtoeverydayflowsofgoodsandmaterialspeoplefinanceandinformation(Fox19711980Braudel1972)Discussionofthegeohistoricalsocialscienceliteratureisbeyondtheintentofthisarticlebutitbearsmentionintermsofcallsforreunifyingsocialandbehavioralsciencewithsocialtheoryincomputationalcognitivemodeling(Conte2002)NonethelesswithsuchgeneraltheoreticalinsightsinmindweusedGIStocalculatearudimentarysocial-geographicorientationorlevelofaffectednesswithrespecttothegreaterPugetSoundandSalishSearegionasaproductofdistancefromthecoastmultipliedbyelevationabovesealevel(seetheattributeORIENTATIONinFigure6)

Figure7SchematicrepresentationoftherelationaldatabaseusedinthesimulationrepresentingsomeofthekeytablesandattributesoftheagentobjectclassSeeFigure8foravisualizationofthementalmodeltables

ConceptualPropertiesofAgents

37 Agentsoperatedwithoneofthreemodeswithrespecttotheirpreconceptions(blankslateclonesocialactor)Atthefirstlevelagentsoperateinblankslatemode(Figure7)InblankslatemodeagentsbeginwithnopreconceptionsaboutanythingbeingneutralwithrespecttoeverysituationregardlessofthementalmodelAtthesecondlevelagentsoperateinclonemodeInclonemode

httpjassssocsurreyacuk1717html 5 16102015

agentsbegintheexperimentwithdifferentpreconceptionsdependingonthesituationbuttheyallhaveexactlythesamepreconceptionstothesamesituationsAtthethirdandmostdiverselevelofpreconceptionsagentsoperateinfullsocialactormodeInsocialactormodeeachagentobjectinstancecarriesadifferentsetofpreconceptionsforeachsituationAgentmentalmodelswereintegratedwiththeagent-basedmodelusingarelationaldatabase

38 InhisclassicstudyoforganizationaldecisionmakingThompson(1967)suggestedtherearetwokindsofuncertaintieswhenpeoplemakedecisionsaboutchanginganexistingsituationintoapreferredoneOnekindofuncertaintysurroundsbeliefsaboutthecauseandeffectrelationsthatproducethecurrentsituationormightproduceapreferredsituationinthefutureTheotherkindofuncertaintyisaboutpreferencesaboutwhichfutureoutcomesaremoredesirable(seealsoLaveandDowlatabadi1993)ElaboratingonThompsons(1967)twokindsofuncertaintywedevelopedthreedifferentkindsofsocialactorpreconceptionsinvolvingbeliefspreferencesorassessmentsWebasedourchoiceofthreekindsofpreconceptionsonbroadsummariesofthedecisionmakingliteraturethattypicallyidentifythreekindsofsocialactorswithslightlydifferentpreconceptions(egNRC19962005)inadditiontooccasionalcasestudiesaboutparticipatorydecisionmakingforsustainabilitymanagementthatconfirmthreesocialactormentalmodels(egDelgadoetal2009)

39 AgentscarrythreekindsofpreconceptionsThefirstkindofpreconceptionistheaffectedparty(AP)orstakeholderpublicmentalmodelthatlooksatasituationfromtheperspectiveofthedesirabilityofchangingsomeexistingsituationintoamorepreferredone(ieintolerableundesirableacceptabledesirableandindispensable)Anotherkindofpreconceptionisthetechnicalspecialist(TS)mentalmodelthatlooksatasituationintermsofbeliefsabouttheplausibilitythatsomesetofcauseandeffectrelationsproducedthecurrentlyexistingsituationorcouldproducesomefuturesituation(ieunimaginableimplausibleconceivableplausibleandcertain)Finallythethirdkindofpreconceptionwastheexecutive(EX)mentalmodelthatlooksatchangingtheexistingsituationtoafuturesituationfromtheperspectiveoffeasibilityassessment(ieunrealisticinfeasiblepossiblefeasibleandpractical)

310 Figure8illustrateshoweachsocialactormentalmodelwascontrolledusingadistinctcolorpatterninarasterdatastructureInthecaseoftheaffectedparty(AP)preconceptionsdifferedfromlowerrighttoupperleftinthiscasefromalowpreconceptioncoloredredrepresentinganintolerablesituationtothehighestpreconceptioncoloredgreenrepresentinganindispensablesituationWebuiltintoourassumptionsthatexecutiveswillgenerallyattempttobalanceaffectedpartyandtechnicalspecialistpreconceptionswhenassessingthefeasibilityofanyparticularprojectprogramorplanaimedatchanginganexistingsituationintoapreferredoneThustheexecutivementalmodelwascalculatedusingrastermathematicsinGISbasedonthetechnicalspecialistmentalmodelandtheaverageofallpreferencesoftheaffectedpartieswithinthejurisdictionalboundaryareatheexecutiveissupposedtorepresentegacountyAsnotedwealsocreatedfourdifferentlevelsofexpertiseforeachagentobjectoperatinginsocialactormodeinordertofurtherdifferentiatewithinaffectedparty(AP)technicalspecialist(TS)andexecutivedecisionmaker(EX)mentalmodelsonthebasisoftheirlevelofaffectednessexpertiseandauthorityresultinginatotalof12differentmentalmodels(seeFigure7)

311 ThethreesetsofsocialactorpreconceptionsdonotdefinethreedifferentagentsForexampleinrealitythesamehumansocialactormaytendtoreasonforthemostpartusingtheiraffectedpartypreferencesbutattimesmayswitchmentalmodelsandconsiderthesamesituationbasedontheirbeliefsorassessmentsTheinterestingcomplexitywhenitcomestotheinteractionsofthesementalmodelsiswhensituationsaredeemedindispensablebyaffectedpartiesbutonlyconceivablebytechnicalspecialistsandinfeasiblebyexecutivesInotherwordsthesamesocialactormaypreferacertainfuturesituationbutmayalsoattheverysametimeunderstandthattheirownpreferencesareunlikelygiventhetimeandresourcesneeded

312 ThuseveryinstanceoftheagentobjectclasscarriesallthreepreconceptionsHowevereachinstanceoftheagentobjectclassalsocarriesauniqueprobabilityortendencytofavoronesetofpreconceptionsoverothersatanygiventimesimilartoafuzzysetForexampleanagentmighthaveanaffectedpartyprobability(AP_PROBinFigure7)of075atechnicalspecialistprobabilityof02(TS_PROBinFigure7)andanexecutiveprobabilityof005(EX_PROBinFigure7)Thereforethisparticularagentwilltendtoreasonaboutasituationbasedontheiraffectedpartypreferencesonaveragethreeoutofeveryfourtimestheyencounterasymbolandrespondaccordinglywhenvotingpostingorreplyingintheonlineplatform

313 Inrealhumansubjectspreconceptionsareoftenmeasuredinordinallevelsofmeasurementfromaquestionnaireorsimilarself-reportmeasureaskingparticipantstoranktheiragreementordisagreementonaLikert-typeitemscaleOriginallyweassignedagentspriorpreconceptionsintherelationaldatabaseasintegerswithpermissiblevaluesrangingfrom1to5correspondingtofiveLikert-typecategoriesWethenconvertedthemtorealnumberseganormalizedrealnumberscalerangingfromhighlynegative(000)tohighlypositive(100)similartopersonalprobabilities(Kahnemanetal1982)inordertostorethemasJavacollectionsinAnyLogicalthoughitbecomesquestionablewhetherpreconceptionsshouldbestoredusingrealnumbervaluesmoreprecisethanthenearesttenthofadecimalpoint

314 Similartootherapproachesthathaveattemptedtoorganizethementalmodelsofsometimesverylargepopulationsofagents(VogtandDivina20052007ChaoqingandPeuquet2009Vogt2009)westructuredmentalmodelsasarasterorgriddatastructureinaGISUsingthementalmodeldatastructureinFigure8tovisualizeagentpreconceptionsthebalanceofgreenversusredcolorpatternsreflectsthebalanceofinfluencebetweenaffectedpartypreferencestechnicalspecialistbeliefsandexecutiveassessmentsForexampleinFigure8thecolorpatternintheaffectedpartymentalmodelcarriedbyeachagentrangesfrommostpreferred(green)toleastpreferred(red)inagenerallyupperlefttolowerrightcolorgradientrepresentingdifferentpreferencesofmorecoastalversusmoreinteriororientatedagents(seealsoORIENTATIONinFigure7)Thecolorpatterninthetechnicalspecialistmentalmodelcarriedbyeachagentrangesfromleastbelievable(red)tomostbelievable(green)infourdistincthotspots(Figure8)Finallyinasomewhatmorecomplicatedschemethecolorpatternintheexecutivementalmodelcarriedbyeachagentrangesfromleastfeasible(red)tomostfeasible(green)bybalancingonthehandtheaffectedpartypreconceptionsofagentsfromtheexecutivespoliticaljurisdictionandontheotherhandthebasetechnicalspecialistpreconceptionsAsnotedtheexecutivementalmodelofwhatismostfeasibleisliterallyamathematicalcompromisebetweenwhatismorepreferredbytheaffectedpartieswithintheexecutivejurisdictionversuswhatismorebelievableaccordingtothetechnicalexperts(Figure8)Ideallyanyvisualanalystcanlookatacolorpatternandvisualdetectpossiblysupportedbysimplespatialstatisticsifanexperimentaloutcomewasinfluencedmorebyaffectedpartypreferencestechnicalspecialistbeliefsorabalancingofthetwobyexecutiveassessments

Figure8Social-actorsmentalmodelasvisualizedinaGISasarasterdatastructure

ChangesintheConceptualPropertiesofAgents

315 EachinstanceoftheagentobjectclasscarriesauniquecapacitytoupdateitspreconceptionsbylearningfromotheragentsandexperiencingconceptualchangeAccordingtoBayesiantheoriesoflearningthedegreetowhichapersonbelievesapropositionistruedependsonthepriorpreconceptionsthatapersonhasinthetruthofthepropositionandtheevidencecollectedtoinvestigatethatproposition(Dempster1968KingandGolledge1969GolledgeandStimson1997DaviesWithers2002CatenacciandGiupponi2010)TheBayesiantheoryoflearningcanbemathematicallydescribedasafunctionofexistingpreconceptions(Heckerman1996Robinson2003)theinherentcredibilityofaparticularelementofinformation(Flach1999)andtheavailabilityorexposuretoapieceofinformationbyeachparticipant(Acemogluetal2010)Weassumedthatthementalmodeltowhichtheagentwasmosthighly-orientedwouldbemoreresistanttoupdatingieamentalmodelbuiltupoverlongperiodsofexposuretocredibleinformationInotherwordsifanagentwaslikelytoreasonwithanaffectedpartymentalmodelthenthatagentobjectwouldcarryaproportionallylowprobabilitytoupdatetheiraffectedpartymentalmodelTocalculateconceptualchangeandlearningweusedtheLaplacian-correctedBayesianalgorithmbasedonitssuccessfulimplementationasaSPAMfilteringalgorithm(seeRobinson2003)ThealgorithmweusedcodedasanactionchartinAnyLogicupdatedanagentspreconceptionsinthesamemannerthatabasicSPAMfilterworksbasedonthecredibilityofthemessageandrepeatedexposuretocertainelementsofamessage(Robinson2003)AfterallofanagentspreconceptionsareupdatedtonewvaluesasspecifiedbyouralgorithmbysubtractingthedifferencesbetweentheimmediatelypriorandthenewlyupdatedvaluesofamentalmodelwewereabletocalculateanagentobjectsconceptualchangeWhenwesumallindividualagentconceptualchangesoverthecourseoftheentiredecisionsituationwecalledthatsumameasureofsociallearning

316 Whatdeterminesifahumansocialactorwillactuallylearntherebyupdatingtheirpreconceptionsandundergoingaconceptualchangeremainsamatteroftheoreticaldebatewithinthecognitivesciences(Chateretal2006a2006b2006c)andagent-basedsimulationsaswell(Lempert2002Ramanath2004Sun2006Kimetal2010BarreteauandLe2011Kim2011Squazzoni2012)ItisalreadyunderstoodthatBayesiantheoriesoflearningareverysensitivetothesimplifyingassumptionsresearchersmakeaboutpreconceptions(DaviesWithers2002)NotsatisfiedthatwecouldprovidetheanswertothesetheoreticalandmethodologicalquestionswedecidedthatwewouldconductaparametervariationexperimentthatvariedthelevelofchangeeachagentobjectinstancecouldundergoAglobalconceptualchangevalueof00meantthatallagentspossessedarigidmentalmodelthatneverchangedwhereasavalueof10meantthatanygivenagentwasallowedtoexperienceconceptualchangeaccordingtoauniqueagent-basedprobabilityforexperiencingconceptualchange(egAP_LEARNinFigure7)

SymbolicPropertiesofAgents

317 Sociallyintelligentagentscommunicatetheirpreconceptionstooneanotherusingasystemofsymbols(Conte2002)Anumberoftheoreticalandphilosophicalperspectivesabouthowactorsinteractandinfluenceoneanotherthroughcommunicationandlanguagelikesemioticssymbolicinteractionismorthephilosophyofmindpointtotheimportanceofreasoningaboutsymbolsthatstandforaconceptinonesmindasappliedtoasetofreferentsintheworld(PeirceNDSperber19851990Auspitz1994Hilpinen1995Sowa2000ManciniandShum2006Sowa2006Hilpinen2007)Interestinglyatleastoneassessmentsuggeststhatsimulationtoolsarelackingwhenitcomestoviewingorvisualizinginformationexchangesbetweenagentsinanagent-basedmodel(Ralambondrainyetal2007)

httpjassssocsurreyacuk1717html 6 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

Figure4AnexampleofanAnyLogicactionchartusedtoimplementagentsinteractionswithsymbols

25 AfterparsinganarrativestatementintomodelelementsweimplementedsocialactoragentsasanactiveobjectclassinAnyLogicWithinthatactiveobjectclasswedefinedagentpropertiesstatesandbehaviorsusingthesoftwarefeaturesofAnyLogicincludingparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsandpresentations(seeAnyLogic2013)Thereareanumberofstandardsfordocumentinganagent-basedmodeltoensureitsreproducibilitySuchstandardsincludeentity-relationshipdiagramsUnifiedModelingLanguage(UML)diagramsvariousotherobject-oriented(OO)diagrammingtechniquesandtheOverviewDesignconceptsandDetails(ODD)protocolforagent-basedmodels(Grimmetal2010Polhill2008)FormattersofeaseofproductionanddetailwedocumentedthephysicalimplementationofthemodelitselfwiththedocumentationtoolsavailableinAnyLogicTheAnyLogicdocumentationtoolslistthecompletedescriptionsofallmodelelementsegparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsgraphicsetcinPDFDOCXorHTMLformforeaseofdistribution

26 Figure1isaschematicrepresentationdescribinghowagentswereimplementedinAnyLogicasanactiveobjectclassStatechartsweremodeledusingcomputableUnifiedModelingLanguageforRealTime(UML-RT)diagramsFigure2isanillustrativeexampleoftheUML-RTstatechartusedtospecifyandimplementagentbehavioralstatesandrulesfortransitionsbetweenstatesduringthesimulationForinstanceinFigure2afteranagenttransitionsfromastateofbeingloggedintotheonlineplatform(stateA)tobeingactive(stateA1)tobeingreadytocreatedeliberativecontentintheformofavotepostorreply(stateA1basmarkedwithanasterisk)consequentlytheyenteranactionchartthatdetermineswhatkindofdeliberativebehaviortheywilllikelytakeActionchartsarestructuredprogrammingblocksthatimplementcodesnippetsusinggraphicalJavaoperatorsFigure3isanexampleofanactionchartimplementingvotingbehaviorforasocialactoragentoperatinginanexecutive(EX)mentalmodelwhichitselfwasimplementedasaJavacollectionIntheactionchartinFigure3thereisanequalchancetheagentwilleithervoteinfavorofsituationsthatbestmatchtheirpreconceptionsorvoteagainstthosethatleastmatchtheirpreconceptionsFurtherexamplesinthepaperprovideillustrativeexamplesofagentobjectvotingbehaviorwhereasfulldetailsaboutstatecharttransitionrulesandactionchartalgorithmsusedinthesimulationareavailableinourmodeldocumentation

ResearchDesignforaSimulatedOnlineFieldExperiment

31 AprimeconcerninexperimentalresearchislimitingthenumberofvariablesbeingconsideredallatonceForexampleinafactorialresearchdesignthenumberofdifferenttreatmentsrequiredequalsthecross-productofthenumberofinterdependentfactorsbeingconsideredBasedonthetheoryofself-organizingbehaviorinsustainabilitysciencewetookfoursubsystemvariablesofinterestincludingsizeoftheresourcesystemthenumberofuserstheamountofknowledgesharingamongdifferentresourceusersmentalmodelsandthelevelofimportanceoftheresourcetoeachuserandthendevelopedthreesimplesetsofagent-basedproperties

SocialampGeographicPropertiesAgentshaveacertainsocialandgeographicorientationtosituationsintheirenvironmentConceptualPropertiesAgentscarrypreconceptionsorganizedintomentalmodelswhichtheyusetoreasonaboutsituationsintheirenvironmentSymbolicPropertiesAgentsaresociallyintelligentandcancommunicatetheirpreconceptionstooneanotherusingasystemofsymbols

32 Eachsetofpropertieswerefurthercategorizedintothreelevelsandanumberofqualificationshadtobemadewhenitcametoimplementingthepropertiesofagentobjectsinarelationaldatabaseintegratedwiththeagent-basedmodel(Figure6)explainedinmoredetailbelowThususingafactorialresearchdesignaftercross-tabulatingthreeinterdependentfactorseachwiththreedifferentlevelstheresultwas27experimentaltreatmentsnotincludingparametervariationexperimentsandreplicationexperimentstoevaluaterandomeffects

SocialampGeographicPropertiesofAgents

33 Ourfirsttaskwastocreateapopulationofagentswithsocialandgeographicpropertiesandthensettargetvaluesforrecruitingacertainnumberoftheseagents(lowmediumandhigh)fromwithintheboundariesofregionalareasrepresentingaresourcesystem(localregionalandinternational)WeestablishedtheboundariesrepresentinglocalregionalandinternationalareasusingacombinationofpoliticaljurisdictionsanddrainageareasandthenusedArcGIStogenerateapopulationofpotentialagentsinWashingtonStateandBritishColombiaCanada(Figure4)ThelocalscaleforthesimulationwasanareaformedbytheninecountiesintersectingthewatershedsofthegreaterPugetSoundregionofWashingtonStateincludingtheCityofSeattleandKingCountyencompassing228strata(ZCTAs)withapopulationof37millionpeopleintheyear2000Theregionalscaleforthesimulationwasanareacreatedbythe85majorwatersheds(areasconformingtoan8-digitHUCorUSGShydrologicunitcodeandCanadianequivalents)contributingtothewaterbodydefinedastheSalishSeawhichencompassed804strata(ZCTAsandCSDs)withatotalpopulationof71millionFinallytheinternationalscalewasWashingtonStateandBritishColumbiaencompassing1423strata(ZCTAsandCSDs)withatotalpopulationof98millionTorepresentapopulationofagentsweusedcountsfromthemosteasilyavailableyeartheyear2000enumeratedinzipcodetabulationareas(ZCTAs)intheUnitedStatesandcensussubdivisions(CSDs)inCanadaWethenusedthecentroidsofeachZCTAandCSDasthecoordinatelocationforeachagentobjectinstanceinthesamewayweusedself-reportedzipcodeinformationtorepresentthelocationofhumansubjectsinpriorexperiments(NyergesandAguirre2011AguirreandNyerges2011)Lastlywesettargetvaluesforlowmediumandhighnumbersofagentsatapproximately25100and1000respectively

Figure5Mapsillustratingthethreedifferentscalesofagentdistribution(localregionalinternational)usedintheexperiment

httpjassssocsurreyacuk1717html 4 16102015

Figure6MapshowingadetailedviewoftheregionalscaleofthesimulationThegrayarearepresentscoastalandfluvialdrainagebasinsemptyingintotothewaterbodydefinedastheSalishSeaThetotalpopulationofeachgeographicstrata(ZCTAsandCSDs)availableforsamplingarerepresentedasproportionalsizesymbolsAffectedpartypreferencesofsocialactorsarerepresentedasa

colorrangefromblue(moreorientedtothecoast)tored(lessorientedtothecoast)

34 Gastiletal(2007)suggestusingaCitizenJuryrecruitmentstrategyusingsmallrandomly-selectedgroupsasrepresentativeoflargerpopulations(seealsoFerguson2007)TheJeffersonCenter(2009)similarlyusedrandomlysampledparticipantsasrepresentativegroupsonthebasisofdemographiccharacteristicsOtherauthorsadvocatenon-randomlysampledgroupsofparticipantspointingoutfromsomewhatanecdotalevidencethatparticipationworkedbestwhenparticipantswerenominatedbytheircommunitytorepresenttheirpreferencesorbeliefs(CarsonandMartin2002Rayner2003)Stillotherspointouttherealityofonlinesituationsintermsofbeingstuckwithnon-randomlyselectedparticipantsakasamplesofconveniencewhicharenotlikelytoberepresentativeofanyparticulargrouporgeographicarea(KonstanandChen2007)

35 Ourrecruitmentstrategywasbasicallytouseageographically-stratifiedsampleandcreatethreelevelsofagentabundance(highmediumlow)usingamodelstwoformsofpoliticalrepresentationintheUnitedStatesCongressTorecruitthelowlevelofapproximately25fromourpopulationweusedamodelsimilartopoliticalrepresentationtheUSSenatebyselectingoneagentfromeachmajorsubdivision(egcountyorwatershed)beginningwiththemostpopulatedZCTAorCSDTorecruitmediumandhighlevelsofapproximately100and1000agentsweusedadifferentmodelmorelikethecongressionaldistrictsintheUSHouseofRepresentativesselectingagentsproportionaltothepopulationofeachminorsubdivision(egzipcodetabulationareaorCanadiancensussubdivision)

36 AsnotedagentsusesymbolstocommunicatetheirmentalmodelsaboutsituationsintheirenvironmentForhumansocialactorentitiesasituationisanysetofsocial-ecologicalentitiesorrelationshipstowhichthesocialactorhasanindividualsocialandgeographicorientationAsocialactorsorientationwithrespecttothosereferentsmightbeperceivedintermsofadirectbenefitorresourceproducedbythatsituationoritmightbeperceivedasanindirectparallelorinducedbenefitorservicederivedfromasituationLikewiseasocialactorsorientationmaybebasedontheirperceptionofadirectorindirectbenefitfromasituationoralternativelyintermsofthatsocialactorsoccupationintermsofapublicagencysjurisdictionoverasituationMentalmodelshavebeenoflongstandinginterestinsustainabilityscience(egseeMathevetetal2011)Howeverlessinfluentialinsustainabilitysciencearegeohistoricalsocialscienceperspectivesthatdemonstratethecontemporarysocialandpoliticalmanifestationsstemmingfromthelong-terminfluenceofsocialandgeographiciemaritime-commercialversusterritorial-administrativeorientationtoeverydayflowsofgoodsandmaterialspeoplefinanceandinformation(Fox19711980Braudel1972)Discussionofthegeohistoricalsocialscienceliteratureisbeyondtheintentofthisarticlebutitbearsmentionintermsofcallsforreunifyingsocialandbehavioralsciencewithsocialtheoryincomputationalcognitivemodeling(Conte2002)NonethelesswithsuchgeneraltheoreticalinsightsinmindweusedGIStocalculatearudimentarysocial-geographicorientationorlevelofaffectednesswithrespecttothegreaterPugetSoundandSalishSearegionasaproductofdistancefromthecoastmultipliedbyelevationabovesealevel(seetheattributeORIENTATIONinFigure6)

Figure7SchematicrepresentationoftherelationaldatabaseusedinthesimulationrepresentingsomeofthekeytablesandattributesoftheagentobjectclassSeeFigure8foravisualizationofthementalmodeltables

ConceptualPropertiesofAgents

37 Agentsoperatedwithoneofthreemodeswithrespecttotheirpreconceptions(blankslateclonesocialactor)Atthefirstlevelagentsoperateinblankslatemode(Figure7)InblankslatemodeagentsbeginwithnopreconceptionsaboutanythingbeingneutralwithrespecttoeverysituationregardlessofthementalmodelAtthesecondlevelagentsoperateinclonemodeInclonemode

httpjassssocsurreyacuk1717html 5 16102015

agentsbegintheexperimentwithdifferentpreconceptionsdependingonthesituationbuttheyallhaveexactlythesamepreconceptionstothesamesituationsAtthethirdandmostdiverselevelofpreconceptionsagentsoperateinfullsocialactormodeInsocialactormodeeachagentobjectinstancecarriesadifferentsetofpreconceptionsforeachsituationAgentmentalmodelswereintegratedwiththeagent-basedmodelusingarelationaldatabase

38 InhisclassicstudyoforganizationaldecisionmakingThompson(1967)suggestedtherearetwokindsofuncertaintieswhenpeoplemakedecisionsaboutchanginganexistingsituationintoapreferredoneOnekindofuncertaintysurroundsbeliefsaboutthecauseandeffectrelationsthatproducethecurrentsituationormightproduceapreferredsituationinthefutureTheotherkindofuncertaintyisaboutpreferencesaboutwhichfutureoutcomesaremoredesirable(seealsoLaveandDowlatabadi1993)ElaboratingonThompsons(1967)twokindsofuncertaintywedevelopedthreedifferentkindsofsocialactorpreconceptionsinvolvingbeliefspreferencesorassessmentsWebasedourchoiceofthreekindsofpreconceptionsonbroadsummariesofthedecisionmakingliteraturethattypicallyidentifythreekindsofsocialactorswithslightlydifferentpreconceptions(egNRC19962005)inadditiontooccasionalcasestudiesaboutparticipatorydecisionmakingforsustainabilitymanagementthatconfirmthreesocialactormentalmodels(egDelgadoetal2009)

39 AgentscarrythreekindsofpreconceptionsThefirstkindofpreconceptionistheaffectedparty(AP)orstakeholderpublicmentalmodelthatlooksatasituationfromtheperspectiveofthedesirabilityofchangingsomeexistingsituationintoamorepreferredone(ieintolerableundesirableacceptabledesirableandindispensable)Anotherkindofpreconceptionisthetechnicalspecialist(TS)mentalmodelthatlooksatasituationintermsofbeliefsabouttheplausibilitythatsomesetofcauseandeffectrelationsproducedthecurrentlyexistingsituationorcouldproducesomefuturesituation(ieunimaginableimplausibleconceivableplausibleandcertain)Finallythethirdkindofpreconceptionwastheexecutive(EX)mentalmodelthatlooksatchangingtheexistingsituationtoafuturesituationfromtheperspectiveoffeasibilityassessment(ieunrealisticinfeasiblepossiblefeasibleandpractical)

310 Figure8illustrateshoweachsocialactormentalmodelwascontrolledusingadistinctcolorpatterninarasterdatastructureInthecaseoftheaffectedparty(AP)preconceptionsdifferedfromlowerrighttoupperleftinthiscasefromalowpreconceptioncoloredredrepresentinganintolerablesituationtothehighestpreconceptioncoloredgreenrepresentinganindispensablesituationWebuiltintoourassumptionsthatexecutiveswillgenerallyattempttobalanceaffectedpartyandtechnicalspecialistpreconceptionswhenassessingthefeasibilityofanyparticularprojectprogramorplanaimedatchanginganexistingsituationintoapreferredoneThustheexecutivementalmodelwascalculatedusingrastermathematicsinGISbasedonthetechnicalspecialistmentalmodelandtheaverageofallpreferencesoftheaffectedpartieswithinthejurisdictionalboundaryareatheexecutiveissupposedtorepresentegacountyAsnotedwealsocreatedfourdifferentlevelsofexpertiseforeachagentobjectoperatinginsocialactormodeinordertofurtherdifferentiatewithinaffectedparty(AP)technicalspecialist(TS)andexecutivedecisionmaker(EX)mentalmodelsonthebasisoftheirlevelofaffectednessexpertiseandauthorityresultinginatotalof12differentmentalmodels(seeFigure7)

311 ThethreesetsofsocialactorpreconceptionsdonotdefinethreedifferentagentsForexampleinrealitythesamehumansocialactormaytendtoreasonforthemostpartusingtheiraffectedpartypreferencesbutattimesmayswitchmentalmodelsandconsiderthesamesituationbasedontheirbeliefsorassessmentsTheinterestingcomplexitywhenitcomestotheinteractionsofthesementalmodelsiswhensituationsaredeemedindispensablebyaffectedpartiesbutonlyconceivablebytechnicalspecialistsandinfeasiblebyexecutivesInotherwordsthesamesocialactormaypreferacertainfuturesituationbutmayalsoattheverysametimeunderstandthattheirownpreferencesareunlikelygiventhetimeandresourcesneeded

312 ThuseveryinstanceoftheagentobjectclasscarriesallthreepreconceptionsHowevereachinstanceoftheagentobjectclassalsocarriesauniqueprobabilityortendencytofavoronesetofpreconceptionsoverothersatanygiventimesimilartoafuzzysetForexampleanagentmighthaveanaffectedpartyprobability(AP_PROBinFigure7)of075atechnicalspecialistprobabilityof02(TS_PROBinFigure7)andanexecutiveprobabilityof005(EX_PROBinFigure7)Thereforethisparticularagentwilltendtoreasonaboutasituationbasedontheiraffectedpartypreferencesonaveragethreeoutofeveryfourtimestheyencounterasymbolandrespondaccordinglywhenvotingpostingorreplyingintheonlineplatform

313 Inrealhumansubjectspreconceptionsareoftenmeasuredinordinallevelsofmeasurementfromaquestionnaireorsimilarself-reportmeasureaskingparticipantstoranktheiragreementordisagreementonaLikert-typeitemscaleOriginallyweassignedagentspriorpreconceptionsintherelationaldatabaseasintegerswithpermissiblevaluesrangingfrom1to5correspondingtofiveLikert-typecategoriesWethenconvertedthemtorealnumberseganormalizedrealnumberscalerangingfromhighlynegative(000)tohighlypositive(100)similartopersonalprobabilities(Kahnemanetal1982)inordertostorethemasJavacollectionsinAnyLogicalthoughitbecomesquestionablewhetherpreconceptionsshouldbestoredusingrealnumbervaluesmoreprecisethanthenearesttenthofadecimalpoint

314 Similartootherapproachesthathaveattemptedtoorganizethementalmodelsofsometimesverylargepopulationsofagents(VogtandDivina20052007ChaoqingandPeuquet2009Vogt2009)westructuredmentalmodelsasarasterorgriddatastructureinaGISUsingthementalmodeldatastructureinFigure8tovisualizeagentpreconceptionsthebalanceofgreenversusredcolorpatternsreflectsthebalanceofinfluencebetweenaffectedpartypreferencestechnicalspecialistbeliefsandexecutiveassessmentsForexampleinFigure8thecolorpatternintheaffectedpartymentalmodelcarriedbyeachagentrangesfrommostpreferred(green)toleastpreferred(red)inagenerallyupperlefttolowerrightcolorgradientrepresentingdifferentpreferencesofmorecoastalversusmoreinteriororientatedagents(seealsoORIENTATIONinFigure7)Thecolorpatterninthetechnicalspecialistmentalmodelcarriedbyeachagentrangesfromleastbelievable(red)tomostbelievable(green)infourdistincthotspots(Figure8)Finallyinasomewhatmorecomplicatedschemethecolorpatternintheexecutivementalmodelcarriedbyeachagentrangesfromleastfeasible(red)tomostfeasible(green)bybalancingonthehandtheaffectedpartypreconceptionsofagentsfromtheexecutivespoliticaljurisdictionandontheotherhandthebasetechnicalspecialistpreconceptionsAsnotedtheexecutivementalmodelofwhatismostfeasibleisliterallyamathematicalcompromisebetweenwhatismorepreferredbytheaffectedpartieswithintheexecutivejurisdictionversuswhatismorebelievableaccordingtothetechnicalexperts(Figure8)Ideallyanyvisualanalystcanlookatacolorpatternandvisualdetectpossiblysupportedbysimplespatialstatisticsifanexperimentaloutcomewasinfluencedmorebyaffectedpartypreferencestechnicalspecialistbeliefsorabalancingofthetwobyexecutiveassessments

Figure8Social-actorsmentalmodelasvisualizedinaGISasarasterdatastructure

ChangesintheConceptualPropertiesofAgents

315 EachinstanceoftheagentobjectclasscarriesauniquecapacitytoupdateitspreconceptionsbylearningfromotheragentsandexperiencingconceptualchangeAccordingtoBayesiantheoriesoflearningthedegreetowhichapersonbelievesapropositionistruedependsonthepriorpreconceptionsthatapersonhasinthetruthofthepropositionandtheevidencecollectedtoinvestigatethatproposition(Dempster1968KingandGolledge1969GolledgeandStimson1997DaviesWithers2002CatenacciandGiupponi2010)TheBayesiantheoryoflearningcanbemathematicallydescribedasafunctionofexistingpreconceptions(Heckerman1996Robinson2003)theinherentcredibilityofaparticularelementofinformation(Flach1999)andtheavailabilityorexposuretoapieceofinformationbyeachparticipant(Acemogluetal2010)Weassumedthatthementalmodeltowhichtheagentwasmosthighly-orientedwouldbemoreresistanttoupdatingieamentalmodelbuiltupoverlongperiodsofexposuretocredibleinformationInotherwordsifanagentwaslikelytoreasonwithanaffectedpartymentalmodelthenthatagentobjectwouldcarryaproportionallylowprobabilitytoupdatetheiraffectedpartymentalmodelTocalculateconceptualchangeandlearningweusedtheLaplacian-correctedBayesianalgorithmbasedonitssuccessfulimplementationasaSPAMfilteringalgorithm(seeRobinson2003)ThealgorithmweusedcodedasanactionchartinAnyLogicupdatedanagentspreconceptionsinthesamemannerthatabasicSPAMfilterworksbasedonthecredibilityofthemessageandrepeatedexposuretocertainelementsofamessage(Robinson2003)AfterallofanagentspreconceptionsareupdatedtonewvaluesasspecifiedbyouralgorithmbysubtractingthedifferencesbetweentheimmediatelypriorandthenewlyupdatedvaluesofamentalmodelwewereabletocalculateanagentobjectsconceptualchangeWhenwesumallindividualagentconceptualchangesoverthecourseoftheentiredecisionsituationwecalledthatsumameasureofsociallearning

316 Whatdeterminesifahumansocialactorwillactuallylearntherebyupdatingtheirpreconceptionsandundergoingaconceptualchangeremainsamatteroftheoreticaldebatewithinthecognitivesciences(Chateretal2006a2006b2006c)andagent-basedsimulationsaswell(Lempert2002Ramanath2004Sun2006Kimetal2010BarreteauandLe2011Kim2011Squazzoni2012)ItisalreadyunderstoodthatBayesiantheoriesoflearningareverysensitivetothesimplifyingassumptionsresearchersmakeaboutpreconceptions(DaviesWithers2002)NotsatisfiedthatwecouldprovidetheanswertothesetheoreticalandmethodologicalquestionswedecidedthatwewouldconductaparametervariationexperimentthatvariedthelevelofchangeeachagentobjectinstancecouldundergoAglobalconceptualchangevalueof00meantthatallagentspossessedarigidmentalmodelthatneverchangedwhereasavalueof10meantthatanygivenagentwasallowedtoexperienceconceptualchangeaccordingtoauniqueagent-basedprobabilityforexperiencingconceptualchange(egAP_LEARNinFigure7)

SymbolicPropertiesofAgents

317 Sociallyintelligentagentscommunicatetheirpreconceptionstooneanotherusingasystemofsymbols(Conte2002)Anumberoftheoreticalandphilosophicalperspectivesabouthowactorsinteractandinfluenceoneanotherthroughcommunicationandlanguagelikesemioticssymbolicinteractionismorthephilosophyofmindpointtotheimportanceofreasoningaboutsymbolsthatstandforaconceptinonesmindasappliedtoasetofreferentsintheworld(PeirceNDSperber19851990Auspitz1994Hilpinen1995Sowa2000ManciniandShum2006Sowa2006Hilpinen2007)Interestinglyatleastoneassessmentsuggeststhatsimulationtoolsarelackingwhenitcomestoviewingorvisualizinginformationexchangesbetweenagentsinanagent-basedmodel(Ralambondrainyetal2007)

httpjassssocsurreyacuk1717html 6 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

Figure6MapshowingadetailedviewoftheregionalscaleofthesimulationThegrayarearepresentscoastalandfluvialdrainagebasinsemptyingintotothewaterbodydefinedastheSalishSeaThetotalpopulationofeachgeographicstrata(ZCTAsandCSDs)availableforsamplingarerepresentedasproportionalsizesymbolsAffectedpartypreferencesofsocialactorsarerepresentedasa

colorrangefromblue(moreorientedtothecoast)tored(lessorientedtothecoast)

34 Gastiletal(2007)suggestusingaCitizenJuryrecruitmentstrategyusingsmallrandomly-selectedgroupsasrepresentativeoflargerpopulations(seealsoFerguson2007)TheJeffersonCenter(2009)similarlyusedrandomlysampledparticipantsasrepresentativegroupsonthebasisofdemographiccharacteristicsOtherauthorsadvocatenon-randomlysampledgroupsofparticipantspointingoutfromsomewhatanecdotalevidencethatparticipationworkedbestwhenparticipantswerenominatedbytheircommunitytorepresenttheirpreferencesorbeliefs(CarsonandMartin2002Rayner2003)Stillotherspointouttherealityofonlinesituationsintermsofbeingstuckwithnon-randomlyselectedparticipantsakasamplesofconveniencewhicharenotlikelytoberepresentativeofanyparticulargrouporgeographicarea(KonstanandChen2007)

35 Ourrecruitmentstrategywasbasicallytouseageographically-stratifiedsampleandcreatethreelevelsofagentabundance(highmediumlow)usingamodelstwoformsofpoliticalrepresentationintheUnitedStatesCongressTorecruitthelowlevelofapproximately25fromourpopulationweusedamodelsimilartopoliticalrepresentationtheUSSenatebyselectingoneagentfromeachmajorsubdivision(egcountyorwatershed)beginningwiththemostpopulatedZCTAorCSDTorecruitmediumandhighlevelsofapproximately100and1000agentsweusedadifferentmodelmorelikethecongressionaldistrictsintheUSHouseofRepresentativesselectingagentsproportionaltothepopulationofeachminorsubdivision(egzipcodetabulationareaorCanadiancensussubdivision)

36 AsnotedagentsusesymbolstocommunicatetheirmentalmodelsaboutsituationsintheirenvironmentForhumansocialactorentitiesasituationisanysetofsocial-ecologicalentitiesorrelationshipstowhichthesocialactorhasanindividualsocialandgeographicorientationAsocialactorsorientationwithrespecttothosereferentsmightbeperceivedintermsofadirectbenefitorresourceproducedbythatsituationoritmightbeperceivedasanindirectparallelorinducedbenefitorservicederivedfromasituationLikewiseasocialactorsorientationmaybebasedontheirperceptionofadirectorindirectbenefitfromasituationoralternativelyintermsofthatsocialactorsoccupationintermsofapublicagencysjurisdictionoverasituationMentalmodelshavebeenoflongstandinginterestinsustainabilityscience(egseeMathevetetal2011)Howeverlessinfluentialinsustainabilitysciencearegeohistoricalsocialscienceperspectivesthatdemonstratethecontemporarysocialandpoliticalmanifestationsstemmingfromthelong-terminfluenceofsocialandgeographiciemaritime-commercialversusterritorial-administrativeorientationtoeverydayflowsofgoodsandmaterialspeoplefinanceandinformation(Fox19711980Braudel1972)Discussionofthegeohistoricalsocialscienceliteratureisbeyondtheintentofthisarticlebutitbearsmentionintermsofcallsforreunifyingsocialandbehavioralsciencewithsocialtheoryincomputationalcognitivemodeling(Conte2002)NonethelesswithsuchgeneraltheoreticalinsightsinmindweusedGIStocalculatearudimentarysocial-geographicorientationorlevelofaffectednesswithrespecttothegreaterPugetSoundandSalishSearegionasaproductofdistancefromthecoastmultipliedbyelevationabovesealevel(seetheattributeORIENTATIONinFigure6)

Figure7SchematicrepresentationoftherelationaldatabaseusedinthesimulationrepresentingsomeofthekeytablesandattributesoftheagentobjectclassSeeFigure8foravisualizationofthementalmodeltables

ConceptualPropertiesofAgents

37 Agentsoperatedwithoneofthreemodeswithrespecttotheirpreconceptions(blankslateclonesocialactor)Atthefirstlevelagentsoperateinblankslatemode(Figure7)InblankslatemodeagentsbeginwithnopreconceptionsaboutanythingbeingneutralwithrespecttoeverysituationregardlessofthementalmodelAtthesecondlevelagentsoperateinclonemodeInclonemode

httpjassssocsurreyacuk1717html 5 16102015

agentsbegintheexperimentwithdifferentpreconceptionsdependingonthesituationbuttheyallhaveexactlythesamepreconceptionstothesamesituationsAtthethirdandmostdiverselevelofpreconceptionsagentsoperateinfullsocialactormodeInsocialactormodeeachagentobjectinstancecarriesadifferentsetofpreconceptionsforeachsituationAgentmentalmodelswereintegratedwiththeagent-basedmodelusingarelationaldatabase

38 InhisclassicstudyoforganizationaldecisionmakingThompson(1967)suggestedtherearetwokindsofuncertaintieswhenpeoplemakedecisionsaboutchanginganexistingsituationintoapreferredoneOnekindofuncertaintysurroundsbeliefsaboutthecauseandeffectrelationsthatproducethecurrentsituationormightproduceapreferredsituationinthefutureTheotherkindofuncertaintyisaboutpreferencesaboutwhichfutureoutcomesaremoredesirable(seealsoLaveandDowlatabadi1993)ElaboratingonThompsons(1967)twokindsofuncertaintywedevelopedthreedifferentkindsofsocialactorpreconceptionsinvolvingbeliefspreferencesorassessmentsWebasedourchoiceofthreekindsofpreconceptionsonbroadsummariesofthedecisionmakingliteraturethattypicallyidentifythreekindsofsocialactorswithslightlydifferentpreconceptions(egNRC19962005)inadditiontooccasionalcasestudiesaboutparticipatorydecisionmakingforsustainabilitymanagementthatconfirmthreesocialactormentalmodels(egDelgadoetal2009)

39 AgentscarrythreekindsofpreconceptionsThefirstkindofpreconceptionistheaffectedparty(AP)orstakeholderpublicmentalmodelthatlooksatasituationfromtheperspectiveofthedesirabilityofchangingsomeexistingsituationintoamorepreferredone(ieintolerableundesirableacceptabledesirableandindispensable)Anotherkindofpreconceptionisthetechnicalspecialist(TS)mentalmodelthatlooksatasituationintermsofbeliefsabouttheplausibilitythatsomesetofcauseandeffectrelationsproducedthecurrentlyexistingsituationorcouldproducesomefuturesituation(ieunimaginableimplausibleconceivableplausibleandcertain)Finallythethirdkindofpreconceptionwastheexecutive(EX)mentalmodelthatlooksatchangingtheexistingsituationtoafuturesituationfromtheperspectiveoffeasibilityassessment(ieunrealisticinfeasiblepossiblefeasibleandpractical)

310 Figure8illustrateshoweachsocialactormentalmodelwascontrolledusingadistinctcolorpatterninarasterdatastructureInthecaseoftheaffectedparty(AP)preconceptionsdifferedfromlowerrighttoupperleftinthiscasefromalowpreconceptioncoloredredrepresentinganintolerablesituationtothehighestpreconceptioncoloredgreenrepresentinganindispensablesituationWebuiltintoourassumptionsthatexecutiveswillgenerallyattempttobalanceaffectedpartyandtechnicalspecialistpreconceptionswhenassessingthefeasibilityofanyparticularprojectprogramorplanaimedatchanginganexistingsituationintoapreferredoneThustheexecutivementalmodelwascalculatedusingrastermathematicsinGISbasedonthetechnicalspecialistmentalmodelandtheaverageofallpreferencesoftheaffectedpartieswithinthejurisdictionalboundaryareatheexecutiveissupposedtorepresentegacountyAsnotedwealsocreatedfourdifferentlevelsofexpertiseforeachagentobjectoperatinginsocialactormodeinordertofurtherdifferentiatewithinaffectedparty(AP)technicalspecialist(TS)andexecutivedecisionmaker(EX)mentalmodelsonthebasisoftheirlevelofaffectednessexpertiseandauthorityresultinginatotalof12differentmentalmodels(seeFigure7)

311 ThethreesetsofsocialactorpreconceptionsdonotdefinethreedifferentagentsForexampleinrealitythesamehumansocialactormaytendtoreasonforthemostpartusingtheiraffectedpartypreferencesbutattimesmayswitchmentalmodelsandconsiderthesamesituationbasedontheirbeliefsorassessmentsTheinterestingcomplexitywhenitcomestotheinteractionsofthesementalmodelsiswhensituationsaredeemedindispensablebyaffectedpartiesbutonlyconceivablebytechnicalspecialistsandinfeasiblebyexecutivesInotherwordsthesamesocialactormaypreferacertainfuturesituationbutmayalsoattheverysametimeunderstandthattheirownpreferencesareunlikelygiventhetimeandresourcesneeded

312 ThuseveryinstanceoftheagentobjectclasscarriesallthreepreconceptionsHowevereachinstanceoftheagentobjectclassalsocarriesauniqueprobabilityortendencytofavoronesetofpreconceptionsoverothersatanygiventimesimilartoafuzzysetForexampleanagentmighthaveanaffectedpartyprobability(AP_PROBinFigure7)of075atechnicalspecialistprobabilityof02(TS_PROBinFigure7)andanexecutiveprobabilityof005(EX_PROBinFigure7)Thereforethisparticularagentwilltendtoreasonaboutasituationbasedontheiraffectedpartypreferencesonaveragethreeoutofeveryfourtimestheyencounterasymbolandrespondaccordinglywhenvotingpostingorreplyingintheonlineplatform

313 Inrealhumansubjectspreconceptionsareoftenmeasuredinordinallevelsofmeasurementfromaquestionnaireorsimilarself-reportmeasureaskingparticipantstoranktheiragreementordisagreementonaLikert-typeitemscaleOriginallyweassignedagentspriorpreconceptionsintherelationaldatabaseasintegerswithpermissiblevaluesrangingfrom1to5correspondingtofiveLikert-typecategoriesWethenconvertedthemtorealnumberseganormalizedrealnumberscalerangingfromhighlynegative(000)tohighlypositive(100)similartopersonalprobabilities(Kahnemanetal1982)inordertostorethemasJavacollectionsinAnyLogicalthoughitbecomesquestionablewhetherpreconceptionsshouldbestoredusingrealnumbervaluesmoreprecisethanthenearesttenthofadecimalpoint

314 Similartootherapproachesthathaveattemptedtoorganizethementalmodelsofsometimesverylargepopulationsofagents(VogtandDivina20052007ChaoqingandPeuquet2009Vogt2009)westructuredmentalmodelsasarasterorgriddatastructureinaGISUsingthementalmodeldatastructureinFigure8tovisualizeagentpreconceptionsthebalanceofgreenversusredcolorpatternsreflectsthebalanceofinfluencebetweenaffectedpartypreferencestechnicalspecialistbeliefsandexecutiveassessmentsForexampleinFigure8thecolorpatternintheaffectedpartymentalmodelcarriedbyeachagentrangesfrommostpreferred(green)toleastpreferred(red)inagenerallyupperlefttolowerrightcolorgradientrepresentingdifferentpreferencesofmorecoastalversusmoreinteriororientatedagents(seealsoORIENTATIONinFigure7)Thecolorpatterninthetechnicalspecialistmentalmodelcarriedbyeachagentrangesfromleastbelievable(red)tomostbelievable(green)infourdistincthotspots(Figure8)Finallyinasomewhatmorecomplicatedschemethecolorpatternintheexecutivementalmodelcarriedbyeachagentrangesfromleastfeasible(red)tomostfeasible(green)bybalancingonthehandtheaffectedpartypreconceptionsofagentsfromtheexecutivespoliticaljurisdictionandontheotherhandthebasetechnicalspecialistpreconceptionsAsnotedtheexecutivementalmodelofwhatismostfeasibleisliterallyamathematicalcompromisebetweenwhatismorepreferredbytheaffectedpartieswithintheexecutivejurisdictionversuswhatismorebelievableaccordingtothetechnicalexperts(Figure8)Ideallyanyvisualanalystcanlookatacolorpatternandvisualdetectpossiblysupportedbysimplespatialstatisticsifanexperimentaloutcomewasinfluencedmorebyaffectedpartypreferencestechnicalspecialistbeliefsorabalancingofthetwobyexecutiveassessments

Figure8Social-actorsmentalmodelasvisualizedinaGISasarasterdatastructure

ChangesintheConceptualPropertiesofAgents

315 EachinstanceoftheagentobjectclasscarriesauniquecapacitytoupdateitspreconceptionsbylearningfromotheragentsandexperiencingconceptualchangeAccordingtoBayesiantheoriesoflearningthedegreetowhichapersonbelievesapropositionistruedependsonthepriorpreconceptionsthatapersonhasinthetruthofthepropositionandtheevidencecollectedtoinvestigatethatproposition(Dempster1968KingandGolledge1969GolledgeandStimson1997DaviesWithers2002CatenacciandGiupponi2010)TheBayesiantheoryoflearningcanbemathematicallydescribedasafunctionofexistingpreconceptions(Heckerman1996Robinson2003)theinherentcredibilityofaparticularelementofinformation(Flach1999)andtheavailabilityorexposuretoapieceofinformationbyeachparticipant(Acemogluetal2010)Weassumedthatthementalmodeltowhichtheagentwasmosthighly-orientedwouldbemoreresistanttoupdatingieamentalmodelbuiltupoverlongperiodsofexposuretocredibleinformationInotherwordsifanagentwaslikelytoreasonwithanaffectedpartymentalmodelthenthatagentobjectwouldcarryaproportionallylowprobabilitytoupdatetheiraffectedpartymentalmodelTocalculateconceptualchangeandlearningweusedtheLaplacian-correctedBayesianalgorithmbasedonitssuccessfulimplementationasaSPAMfilteringalgorithm(seeRobinson2003)ThealgorithmweusedcodedasanactionchartinAnyLogicupdatedanagentspreconceptionsinthesamemannerthatabasicSPAMfilterworksbasedonthecredibilityofthemessageandrepeatedexposuretocertainelementsofamessage(Robinson2003)AfterallofanagentspreconceptionsareupdatedtonewvaluesasspecifiedbyouralgorithmbysubtractingthedifferencesbetweentheimmediatelypriorandthenewlyupdatedvaluesofamentalmodelwewereabletocalculateanagentobjectsconceptualchangeWhenwesumallindividualagentconceptualchangesoverthecourseoftheentiredecisionsituationwecalledthatsumameasureofsociallearning

316 Whatdeterminesifahumansocialactorwillactuallylearntherebyupdatingtheirpreconceptionsandundergoingaconceptualchangeremainsamatteroftheoreticaldebatewithinthecognitivesciences(Chateretal2006a2006b2006c)andagent-basedsimulationsaswell(Lempert2002Ramanath2004Sun2006Kimetal2010BarreteauandLe2011Kim2011Squazzoni2012)ItisalreadyunderstoodthatBayesiantheoriesoflearningareverysensitivetothesimplifyingassumptionsresearchersmakeaboutpreconceptions(DaviesWithers2002)NotsatisfiedthatwecouldprovidetheanswertothesetheoreticalandmethodologicalquestionswedecidedthatwewouldconductaparametervariationexperimentthatvariedthelevelofchangeeachagentobjectinstancecouldundergoAglobalconceptualchangevalueof00meantthatallagentspossessedarigidmentalmodelthatneverchangedwhereasavalueof10meantthatanygivenagentwasallowedtoexperienceconceptualchangeaccordingtoauniqueagent-basedprobabilityforexperiencingconceptualchange(egAP_LEARNinFigure7)

SymbolicPropertiesofAgents

317 Sociallyintelligentagentscommunicatetheirpreconceptionstooneanotherusingasystemofsymbols(Conte2002)Anumberoftheoreticalandphilosophicalperspectivesabouthowactorsinteractandinfluenceoneanotherthroughcommunicationandlanguagelikesemioticssymbolicinteractionismorthephilosophyofmindpointtotheimportanceofreasoningaboutsymbolsthatstandforaconceptinonesmindasappliedtoasetofreferentsintheworld(PeirceNDSperber19851990Auspitz1994Hilpinen1995Sowa2000ManciniandShum2006Sowa2006Hilpinen2007)Interestinglyatleastoneassessmentsuggeststhatsimulationtoolsarelackingwhenitcomestoviewingorvisualizinginformationexchangesbetweenagentsinanagent-basedmodel(Ralambondrainyetal2007)

httpjassssocsurreyacuk1717html 6 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

agentsbegintheexperimentwithdifferentpreconceptionsdependingonthesituationbuttheyallhaveexactlythesamepreconceptionstothesamesituationsAtthethirdandmostdiverselevelofpreconceptionsagentsoperateinfullsocialactormodeInsocialactormodeeachagentobjectinstancecarriesadifferentsetofpreconceptionsforeachsituationAgentmentalmodelswereintegratedwiththeagent-basedmodelusingarelationaldatabase

38 InhisclassicstudyoforganizationaldecisionmakingThompson(1967)suggestedtherearetwokindsofuncertaintieswhenpeoplemakedecisionsaboutchanginganexistingsituationintoapreferredoneOnekindofuncertaintysurroundsbeliefsaboutthecauseandeffectrelationsthatproducethecurrentsituationormightproduceapreferredsituationinthefutureTheotherkindofuncertaintyisaboutpreferencesaboutwhichfutureoutcomesaremoredesirable(seealsoLaveandDowlatabadi1993)ElaboratingonThompsons(1967)twokindsofuncertaintywedevelopedthreedifferentkindsofsocialactorpreconceptionsinvolvingbeliefspreferencesorassessmentsWebasedourchoiceofthreekindsofpreconceptionsonbroadsummariesofthedecisionmakingliteraturethattypicallyidentifythreekindsofsocialactorswithslightlydifferentpreconceptions(egNRC19962005)inadditiontooccasionalcasestudiesaboutparticipatorydecisionmakingforsustainabilitymanagementthatconfirmthreesocialactormentalmodels(egDelgadoetal2009)

39 AgentscarrythreekindsofpreconceptionsThefirstkindofpreconceptionistheaffectedparty(AP)orstakeholderpublicmentalmodelthatlooksatasituationfromtheperspectiveofthedesirabilityofchangingsomeexistingsituationintoamorepreferredone(ieintolerableundesirableacceptabledesirableandindispensable)Anotherkindofpreconceptionisthetechnicalspecialist(TS)mentalmodelthatlooksatasituationintermsofbeliefsabouttheplausibilitythatsomesetofcauseandeffectrelationsproducedthecurrentlyexistingsituationorcouldproducesomefuturesituation(ieunimaginableimplausibleconceivableplausibleandcertain)Finallythethirdkindofpreconceptionwastheexecutive(EX)mentalmodelthatlooksatchangingtheexistingsituationtoafuturesituationfromtheperspectiveoffeasibilityassessment(ieunrealisticinfeasiblepossiblefeasibleandpractical)

310 Figure8illustrateshoweachsocialactormentalmodelwascontrolledusingadistinctcolorpatterninarasterdatastructureInthecaseoftheaffectedparty(AP)preconceptionsdifferedfromlowerrighttoupperleftinthiscasefromalowpreconceptioncoloredredrepresentinganintolerablesituationtothehighestpreconceptioncoloredgreenrepresentinganindispensablesituationWebuiltintoourassumptionsthatexecutiveswillgenerallyattempttobalanceaffectedpartyandtechnicalspecialistpreconceptionswhenassessingthefeasibilityofanyparticularprojectprogramorplanaimedatchanginganexistingsituationintoapreferredoneThustheexecutivementalmodelwascalculatedusingrastermathematicsinGISbasedonthetechnicalspecialistmentalmodelandtheaverageofallpreferencesoftheaffectedpartieswithinthejurisdictionalboundaryareatheexecutiveissupposedtorepresentegacountyAsnotedwealsocreatedfourdifferentlevelsofexpertiseforeachagentobjectoperatinginsocialactormodeinordertofurtherdifferentiatewithinaffectedparty(AP)technicalspecialist(TS)andexecutivedecisionmaker(EX)mentalmodelsonthebasisoftheirlevelofaffectednessexpertiseandauthorityresultinginatotalof12differentmentalmodels(seeFigure7)

311 ThethreesetsofsocialactorpreconceptionsdonotdefinethreedifferentagentsForexampleinrealitythesamehumansocialactormaytendtoreasonforthemostpartusingtheiraffectedpartypreferencesbutattimesmayswitchmentalmodelsandconsiderthesamesituationbasedontheirbeliefsorassessmentsTheinterestingcomplexitywhenitcomestotheinteractionsofthesementalmodelsiswhensituationsaredeemedindispensablebyaffectedpartiesbutonlyconceivablebytechnicalspecialistsandinfeasiblebyexecutivesInotherwordsthesamesocialactormaypreferacertainfuturesituationbutmayalsoattheverysametimeunderstandthattheirownpreferencesareunlikelygiventhetimeandresourcesneeded

312 ThuseveryinstanceoftheagentobjectclasscarriesallthreepreconceptionsHowevereachinstanceoftheagentobjectclassalsocarriesauniqueprobabilityortendencytofavoronesetofpreconceptionsoverothersatanygiventimesimilartoafuzzysetForexampleanagentmighthaveanaffectedpartyprobability(AP_PROBinFigure7)of075atechnicalspecialistprobabilityof02(TS_PROBinFigure7)andanexecutiveprobabilityof005(EX_PROBinFigure7)Thereforethisparticularagentwilltendtoreasonaboutasituationbasedontheiraffectedpartypreferencesonaveragethreeoutofeveryfourtimestheyencounterasymbolandrespondaccordinglywhenvotingpostingorreplyingintheonlineplatform

313 Inrealhumansubjectspreconceptionsareoftenmeasuredinordinallevelsofmeasurementfromaquestionnaireorsimilarself-reportmeasureaskingparticipantstoranktheiragreementordisagreementonaLikert-typeitemscaleOriginallyweassignedagentspriorpreconceptionsintherelationaldatabaseasintegerswithpermissiblevaluesrangingfrom1to5correspondingtofiveLikert-typecategoriesWethenconvertedthemtorealnumberseganormalizedrealnumberscalerangingfromhighlynegative(000)tohighlypositive(100)similartopersonalprobabilities(Kahnemanetal1982)inordertostorethemasJavacollectionsinAnyLogicalthoughitbecomesquestionablewhetherpreconceptionsshouldbestoredusingrealnumbervaluesmoreprecisethanthenearesttenthofadecimalpoint

314 Similartootherapproachesthathaveattemptedtoorganizethementalmodelsofsometimesverylargepopulationsofagents(VogtandDivina20052007ChaoqingandPeuquet2009Vogt2009)westructuredmentalmodelsasarasterorgriddatastructureinaGISUsingthementalmodeldatastructureinFigure8tovisualizeagentpreconceptionsthebalanceofgreenversusredcolorpatternsreflectsthebalanceofinfluencebetweenaffectedpartypreferencestechnicalspecialistbeliefsandexecutiveassessmentsForexampleinFigure8thecolorpatternintheaffectedpartymentalmodelcarriedbyeachagentrangesfrommostpreferred(green)toleastpreferred(red)inagenerallyupperlefttolowerrightcolorgradientrepresentingdifferentpreferencesofmorecoastalversusmoreinteriororientatedagents(seealsoORIENTATIONinFigure7)Thecolorpatterninthetechnicalspecialistmentalmodelcarriedbyeachagentrangesfromleastbelievable(red)tomostbelievable(green)infourdistincthotspots(Figure8)Finallyinasomewhatmorecomplicatedschemethecolorpatternintheexecutivementalmodelcarriedbyeachagentrangesfromleastfeasible(red)tomostfeasible(green)bybalancingonthehandtheaffectedpartypreconceptionsofagentsfromtheexecutivespoliticaljurisdictionandontheotherhandthebasetechnicalspecialistpreconceptionsAsnotedtheexecutivementalmodelofwhatismostfeasibleisliterallyamathematicalcompromisebetweenwhatismorepreferredbytheaffectedpartieswithintheexecutivejurisdictionversuswhatismorebelievableaccordingtothetechnicalexperts(Figure8)Ideallyanyvisualanalystcanlookatacolorpatternandvisualdetectpossiblysupportedbysimplespatialstatisticsifanexperimentaloutcomewasinfluencedmorebyaffectedpartypreferencestechnicalspecialistbeliefsorabalancingofthetwobyexecutiveassessments

Figure8Social-actorsmentalmodelasvisualizedinaGISasarasterdatastructure

ChangesintheConceptualPropertiesofAgents

315 EachinstanceoftheagentobjectclasscarriesauniquecapacitytoupdateitspreconceptionsbylearningfromotheragentsandexperiencingconceptualchangeAccordingtoBayesiantheoriesoflearningthedegreetowhichapersonbelievesapropositionistruedependsonthepriorpreconceptionsthatapersonhasinthetruthofthepropositionandtheevidencecollectedtoinvestigatethatproposition(Dempster1968KingandGolledge1969GolledgeandStimson1997DaviesWithers2002CatenacciandGiupponi2010)TheBayesiantheoryoflearningcanbemathematicallydescribedasafunctionofexistingpreconceptions(Heckerman1996Robinson2003)theinherentcredibilityofaparticularelementofinformation(Flach1999)andtheavailabilityorexposuretoapieceofinformationbyeachparticipant(Acemogluetal2010)Weassumedthatthementalmodeltowhichtheagentwasmosthighly-orientedwouldbemoreresistanttoupdatingieamentalmodelbuiltupoverlongperiodsofexposuretocredibleinformationInotherwordsifanagentwaslikelytoreasonwithanaffectedpartymentalmodelthenthatagentobjectwouldcarryaproportionallylowprobabilitytoupdatetheiraffectedpartymentalmodelTocalculateconceptualchangeandlearningweusedtheLaplacian-correctedBayesianalgorithmbasedonitssuccessfulimplementationasaSPAMfilteringalgorithm(seeRobinson2003)ThealgorithmweusedcodedasanactionchartinAnyLogicupdatedanagentspreconceptionsinthesamemannerthatabasicSPAMfilterworksbasedonthecredibilityofthemessageandrepeatedexposuretocertainelementsofamessage(Robinson2003)AfterallofanagentspreconceptionsareupdatedtonewvaluesasspecifiedbyouralgorithmbysubtractingthedifferencesbetweentheimmediatelypriorandthenewlyupdatedvaluesofamentalmodelwewereabletocalculateanagentobjectsconceptualchangeWhenwesumallindividualagentconceptualchangesoverthecourseoftheentiredecisionsituationwecalledthatsumameasureofsociallearning

316 Whatdeterminesifahumansocialactorwillactuallylearntherebyupdatingtheirpreconceptionsandundergoingaconceptualchangeremainsamatteroftheoreticaldebatewithinthecognitivesciences(Chateretal2006a2006b2006c)andagent-basedsimulationsaswell(Lempert2002Ramanath2004Sun2006Kimetal2010BarreteauandLe2011Kim2011Squazzoni2012)ItisalreadyunderstoodthatBayesiantheoriesoflearningareverysensitivetothesimplifyingassumptionsresearchersmakeaboutpreconceptions(DaviesWithers2002)NotsatisfiedthatwecouldprovidetheanswertothesetheoreticalandmethodologicalquestionswedecidedthatwewouldconductaparametervariationexperimentthatvariedthelevelofchangeeachagentobjectinstancecouldundergoAglobalconceptualchangevalueof00meantthatallagentspossessedarigidmentalmodelthatneverchangedwhereasavalueof10meantthatanygivenagentwasallowedtoexperienceconceptualchangeaccordingtoauniqueagent-basedprobabilityforexperiencingconceptualchange(egAP_LEARNinFigure7)

SymbolicPropertiesofAgents

317 Sociallyintelligentagentscommunicatetheirpreconceptionstooneanotherusingasystemofsymbols(Conte2002)Anumberoftheoreticalandphilosophicalperspectivesabouthowactorsinteractandinfluenceoneanotherthroughcommunicationandlanguagelikesemioticssymbolicinteractionismorthephilosophyofmindpointtotheimportanceofreasoningaboutsymbolsthatstandforaconceptinonesmindasappliedtoasetofreferentsintheworld(PeirceNDSperber19851990Auspitz1994Hilpinen1995Sowa2000ManciniandShum2006Sowa2006Hilpinen2007)Interestinglyatleastoneassessmentsuggeststhatsimulationtoolsarelackingwhenitcomestoviewingorvisualizinginformationexchangesbetweenagentsinanagent-basedmodel(Ralambondrainyetal2007)

httpjassssocsurreyacuk1717html 6 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

318 InoursimulationeachagentusedtheonlineplatformtobrowseandfiltersymbolsandthenreasonaboutthesituationbymatchingittotheirpreconceptionsAlphabeticaltokenslikeAandBstandforconceptsNumerictokenslike1and2standforentitiesandrelationshipsofasocial-ecologicalsystem(iethereferents)WeconsidertokensAorBcombinedwith1or2asthebasicbundleofcategoriesthatagentsuselikeinalanguagegame(ShohamandBrown2009Gilbert2008)Addinginsightsfromgeodesignsustainabilityscienceandresiliencethinking(Gallopiacuten2006Moser2008Gunderson2009Cumming2011)theconceptAcouldbeanassessmentofthestateoridentityofasocial-ecologicalsystem(egtheconceptofmoderately-susceptibletoorganicwastecontaminationduringpeakepisodesofstormrunoff)ThisconceptAcouldbeappliedtoanyparticularsetofspatialelementsorrelationshipsofinterest1(egrelationshipsbetweenorganicwastefromsmalldairyfarmsandaquaticinvertebratesintheupperreachesoftheDuwamishRiverwatershedinKingCountyWashington)Athirdtokenwasaddedasacueaboutwhethertheagentswereexpressingtheirbelief(b)preference(p)orassessment(a)ofaconcept-referentbundleormessageegb|A|1orp|A|1WeconsideredbutdidnotimplementafourthsetoftokenstoindicatetheirordinalrankstrengthofbeliefpreferenceorassessmentInsumwiththreebasicframesofmind(aborp)x26concepts(AtoZ)times26referents(1to26)agentshadthecapabilitytoreasonabout676differentsituationsusing8112symbols

319 ThesimulationwassettounfoldinrealPacificStandardTimeoverexactlythesameperiodasoneofouronlinefieldexperimentsin2007(AguirreandNyerges2011)Figure10isanillustrativeexampleofhowanagentwhenroutedthroughadeliberativeactionchartaftertransitioningtothestateofbeingactiveintheonlineplatformusedthesimulatedbrowsingandfilteringtoolsintheplatformtosortsymbolsasmessagesaboutsituationsbymostvotedandthenreasonabouttheresultinglistandvotetoagreewithoneofthesituationsbeingposedEachagentwasrandomlyassignedacertainnumberoftimesperdaytheywouldbeexpectedtoperformadeliberativeactionAgentswereexpectedtobeactiveintheonlineplatformforonlyacertaintimeduringthedayandweekbasedonthefrequencyofactivityobservedinhumansubjectsfrompreviousonlinefieldexperiments

320 Agentshadavailabletothemthreedifferentmethodsofbrowsingandfilteringmessagesincludingfilteringbythetop10mostrecentlypostedbythetop10mostvotedintermsofnumberofnegativeorpositivevotes(seeFigure10)andfinallybythetop10mostrepliedRulesforhowagentsbrowseandfiltermessagesareaparticularlyinterestingsetofcontrolstoconsidersinceactualhumanparticipantsinonlinepublicparticipationdecisionmakingmaygenerallyprefercertainmethodsoverotherswhichmaybiascertainkindsofmessagesNonethelessafterfilteringasampleof10messagesusingoneofthreemethodsfollowingthesamepreferencesobservedinhumansubjectsagentsreasonedabouttheirsubsetofmessagesintermsofhowtheymatchedtheirpreconceptionsAgentsre-sortedtheirsampleof10messagesfromhighesttolowestmatchwiththeirexistingpreconceptionsandthenselectedthetopresultofthisre-sortedlisttovoteonorreplyto(Figure10)IftheyintendedtofindthesituationthatmostmatchedtheirpreconceptionsthentheyvotedtoagreewiththetopresultIftheagentswerereplyingtoamessageratherthansimplyvotingonittheycouldengageinasomewhatmorecomplexsituationwheretheywouldbeabletochangeonetokeninthemessageeithertheconceptorthereferenttokensothattheresultingbundleoftokensinthesymbolrankedhigheraccordingtotheirmentalmodelatthetime

Results

41 Thethreefactorsandthreelevelsincluded1)thesocialandgeographicdistributionofagents(localregionalinternational)2)theabundanceofagents(lowmediumhigh)and3)thediversityofpreconceptions(blankslateclonesocialactor)Cross-tabulatingallthreefactorsandlevelsmeantrunning27simulatedfieldexperimentsnotincludingsensitivityanalysesorreplicationexperimentstoevaluaterandomeffectsHoweverwewereunabletorunanytreatmentsatthehighlevelofabundanceofagentsinvolvingroughly1000agentsbecausethecomplexityofthesimulationoutstrippedthepowerofourdesktopcomputingcapabilitiesThuswewereonlyabletoexaminethefirsttwolevelsofabundanceofagents(lowandmedium)resultinginatotalof18treatmentsinsteadoftheoriginallyplanned27treatmentsInfutureresearcheitherasimplermodeldesignorhigherperformingcomputingsystemswouldbeneeded

Figure9Eventlogtablefromsimulatedonlinefieldexperiment

httpjassssocsurreyacuk1717html 7 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

Figure10

42 Forthe18simulatedfieldexperimentswewereabletosuccessfullyrunwegeneratedasetofobservationsresemblingaclient-servereventlog(Figure9)ThesimulatedeventloginFigure9wasdesignedtobeverysimilartowhatwascollectedfromtheonlineplatformusedinactualfieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)SeveralthousandeventswereloggedforeachtreatmentafterwhichtheywereexportedtoarelationaldatabaseforanalysisParsingoutsomeoftheattributeinformationinasamplerowfromtheeventlogtableinFigure9onecanseeanexampleofdeliberativeactivitybyanagentobjectinstancewithID78operatinginsocialactormode(Preconceptions0000)duringthelowabundanceinternationalscaletreatment(1423LOW101)referringtothe101participantsrecruitedfrom1423sub-divisionsthroughoutWashingtonStateandBritishColumbiaThetermsUpdating000002indicatesthatthelevelofconceptualchangeintheparametervariationsensitivityanalysiswasatstep02onapossiblerangeof01to10Thesimulatedeventlogrecordedaparticularinteractioneventbyagentobjectinstance78anagentthattendstooperatewithanexecutivesocialactormentalmodel(061)duringStep6ofthesimulatedexperimentattimeFridayNovember92007at080304AMPSTAtthattimeagent78repliedtoasituationrepresentedbysymbola|T|7withamodifiedmessagea|H|7whichaccordingtotheirexecutivementalmodelrepresentedaslightlymorefeasible(069versus066)stateforthesocial-ecologicalsystemreferredtoin7

Scalingdidnotaffectconceptualchangeonaperagentbasis

43 AsexpectedasanagentsabilitytoexperienceconceptualchangeincreasedtheoverallsociallearningsteeplyincreasedInadditionthegreaterthediversityofpreconceptionsthegreatertheaveragelevelofconceptualchangeonaperagentbasisForexampletheresultsofaveragelevelofconceptualchangeforamediumabundanceexperiment(c100participants)acrossdifferentlocalregionalandinternationalscalesindicatemuchmoreconceptualchangeoccurswhenagentsareactinginsocialactormodeasopposedtoblankslateorclonemodeHowevernotasexpectedchangingthesocialandgeographicdistributionandabundanceofagentsdidnotseemtohaveanysignificantimpactonsociallearningoutcomesmeasuredonanaverageagentbasisInfactwefoundnearlythesamelevelsofconceptualchangeonaperagentbasisforthelowabundanceexperiment(between12and37participants)acrossallthreelocalregionalandinternationalsocialandgeographicdistributionsThisfindingmightsuggestthatwhileadiversityofpreconceptionsincreasessociallearningvaryingsocialandgeographicdistributionaswellasabundancearenotimportantinfluencesWhyisitthat100agentsfromalocalgeographicareawouldexperiencethesamelevelofconceptualchangeonaverageas100agentsfromaregionalorinternationalgeographicareaifpreconceptionsaresupposedtovarygeographicallyWefeltthatthisresultwasaproductofourownsimplifyingassumptionsinthemodelitselfbutnotareasonableoneFurthermodeldesignshouldfocusonthesensitivityofthemodeltochangesinthesocialandgeographicdistributionofagents(localregionalinternational)andtheabundanceofagents(lowmediumhigh)

Scalingmayaffectthechoicesagentsmake

44 Itwasexpectedthatchangingthegeographicdistributionandabundanceofagentswouldhaveanimpactonthemostpopularsituationsinparticularshowingtheinfluenceofaffectedpartypreferencesvisuallyintermsofacolorpatternshiftedfromupperlefttolowerrightafterscalingoutfromalocal(centralPugetSoundregionorA)toregional(SalishSeadrainagebasinsorB)toaninternational(WashingtonandBritishColumbiaorC)regionToadequatelytestthishypothesisideallywewouldhavepreferredtosimplyiterateeachexperimenthundredsorthousandsoftimespossiblyusingspatialstatisticstodeterminehoweachrasterdatastructurewasdifferentTheAnyLogicsimulationplatformprovideduswithawayofmanagingreplicationexperimentsusingitsOptQuestalgorithm

45 Asexpectedthemostimportantresultofthesimulationisthefindingthatwhenthesocialandgeographicdistributionandabundanceofagentschangethemostpopularandleastpopularchoicesoutofthe676situationsalsochange(Figure11)WemeasuredthemostandleastpopularchoicesbycalculatingapopularityratiobasedonsubtractingagreevotesfromdisagreevotesandthendividingbytotalnumberofvotescastThehighestpopularityratiopossibleis10whereasthelowestpopularityratiopossibleisndash10Figure11illustratesanexampleofthemostpopularchoicesselectedbyallagentsatthelocalregionalandinternationalscalewithinthemediumabundanceexperimentofabout100agentsvisualizedinArcGISusingarasterdatastructureWediscussthetheoreticalimplicationsofthesefindingsinmoredetailbelow

httpjassssocsurreyacuk1717html 8 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

Figure11Themostpopularandleastpopularsituationsasvotedonbyagentsinthemediumabundanceexperiment(c100participants)acrosslocal(A)regional(B)andinternational(C)scales

Conclusion

51 Thegoalofthesimulationwastomodeltheimpactofscalingonhowsocialactorsmightself-organizethroughonlinecommunicationandconsensusOurfactorialresearchdesigninvolvedsociallyintelligentagentsinteractingunderdifferentconditionsbasedonthreesetsoffactorsinvolving27differenttreatmentsTheninefactorsincludedthesocialandgeographicdistributionofagents(localregionalinternational)abundanceofagents(lowmediumhigh)anddiversityofpreconceptions(blankslateclonesocialactor)Duetocomputationallimitationswewerenotabletorunthe9treatmentsinvolvingahighabundanceofagents

52 WeexpectedthatsocialandgeographicdistributionofagentsaswellasdiversityofagentpreconceptionswouldstronglyimpactconsensusaboutwhichsituationstochangeandwhichonesnottochangeHoweverourexpectationswerenotmetbyourfindingsFirstlyweexaminedhowchangesinsocialandgeographicdistributionandabundanceofagentsaswellasmentalmodeldiversityaffectedconceptualchangeandsociallearningonaperagentbasisAsexpectedincreasinganagentsabilitytoexperienceconceptualchangeandincreasingthediversityofpreconceptionsincreasedtheaveragelevelofconceptualchangeonaperagentbasisSomewhatunexpectedlygeographicdistributionandabundancehadlittleimpactonconceptualchangeSecondlyweexaminedwhetherchangesinsocialandgeographicdistributionandabundanceofagentsmightaffectthechoicesagentsmakeAsexpectedwhenwechangedthesocialandgeographicdistributionandabundanceofsocialactoragentsthemostpopularchoiceofsituationsalsochangedasmeasuredusingapopularityratiofrom10and-10

53 InfuturesimulationswemightmorecarefullystructureaffectedpartytechnicalspecialistandexecutivesocialactormentalmodelsinvisualpatternstogeneratepredictabletensionsbetweenwhatismostpreferredmostplausibleandmostfeasiblesuchthatwecouldcomputeanoptimumsetofchoicesandthencompareactualsimulationresultsofthemostpopularchoicesForexamplewemightseethemostpopularsituationsintheonlineplatformchangeasafunctionoftheactivityofcertainkindsofsocialactoragentsAsanotherexamplebyincreasingtheabundanceortherelativeimportanceofcertainsocialactorrolessimulatingtheinfluenceofcompulsionandpowerwecouldcalculatespatialstatisticsbasedonvisualrepresentationslikeFigure11toseehowthemostpopularchoicesaremadetoconformtoacertainmentalmodelAnotherstepwouldbetocontrolthenumberandcomplexityofrepresentationalsignsofmeaningfromacognitivelyfundamentalhandful(5times5or25situations)toadozen(12times12or144situations)andthenfinallythealphabetsoupsetofconditions(26times26or676situations)weusedinourcurrentresearchdesignIntermsofafutureresearchdesignitwouldbeusefultoestablishcontrolsovercertainagentobjectparametersorvariablesnowthatwehavemoreinsightaboutwhattocontrolegthebalanceofsocialactorrolesthevarietyofsituationsbeingconsideredoreventheonlineplatformtoolsavailableforbrowsingandfilteringInfuturesimulationswemightalsoconsiderentirelynewmentalmodelrepresentationslikeconceptmapsratherthanthe26times26rastercellmatricesimplementedassortableJavacollectionsLastlyunexpectedcomputingissuespreventedourbeingabletorunacompletesetof27controlledconditionsObviouslyausefulnextstepistomakeuseofamorepowerfulcomputationalplatform

54 Wehaveyettotakethelessonslearnedfromsimulationandturnbacktoexperimentswithhumansubjectparticipantsasinearlierresearchonface-to-facehumancomputerinteraction(JankowskiandNyerges2001)andonlinefieldexperiments(NyergesandAguirre2011AguirreandNyerges2011)BrinbergandMcGrath(1985)whowedrawuponforourownresearchinthisarticleofferedwarningsabouttheimpactofmethodologicaltheoreticalorsubstantivepreferencesinthesocialsciencesReflectingontheimpactofmethodologicaldisputesaboutthemeritsofexperimentationversusfieldobservationinthehistoryofbiologicalthoughtErnstMayr(1982)believedthatanynarrativestatementaboutarelationshipbetweenelementscouldlegitimatelybetestedbyexperimentationHoweverifthenarrativestatementinquestiondescribedanactualsequenceofoccurrencesthenitcouldonlybereconstructedthroughsubstantiveobservationsofthepastinwhichcaseharboringapreferencefortheoreticalexperimentationattheexpenseoffieldobservationswasmisplacedMayrfeltthatabiologicalresearchersownprematureinsistenceoneitherexperimentationorfieldobservationwaswhathadcausedbiologicalresearchitselftomoveintounsuitabledirectionsasifstuckbetweentwofalsealternativessomethinghefeltwasthecauseofnearlyeverycontroversyinthehistoryofevolutionarybiology(Mayr1982)

55 Researchonparticipatorydecisionmakingissusceptibletocontroversiesatanevenmoreimpulsivelevelsinceresearchersconfidenceinfalsealternativesislikelybaseduponsimplifyingstatementsthathaveneverbeenfullyexploredeitherthroughlaboratoryexperimentationorevaluationinthefield(LaurianandShaw2009)InvestigatingasingleelementofsuccessorfailurewhenitcomestoparticipatorydecisionmakingforsustainabilitymanagementmightnaturallyleadaresearchertomakeprematureconclusionsaboutthebestwaytomanageanynumberofimportantelementsincludingthebestwayofrecruitingparticipantsmakingfactualinformationavailablescaffoldingreasoningandlearningorcreatingaforumfordeliberationThewaysinwhichalltheseelementsarerelatedandthesometimesunintendedunanticipatedorunknownspatialandtemporalrelationshipsthatemergebetweenthemhaveyettobeunderstood

56 Thoughasimulation-basedresearchdesignisnotasubstituteforresearchwithhumansubjectsitiswellsuitedtotriangulatingfindingsdrawnfromfieldexperimentsandcasestudiesHoweverourresultssuggestedtousmoreaboutthetheoreticalconceptsweusedtoinformouragent-basedmodeldesignthanoursubstantiveareaofinterestthegreaterPugetSoundregionThetheoryofself-organizingcontrolsystemsinsustainabilityscienceassumesthatthemoreresourceusersareabletocommunicatetheirmentalmodelsofthesystemofwhichtheyareadependentpartcombinedwiththeimportanceofthatresourcetotheusersthemselvesthemorelikelytheywillinvestthenecessarytimeandenergytomanagethesystemtomaintainitsidentityanditsresiliencetodisturbanceoroveruseSustainabilityscienceprovidesaconceptualframeworkofvariablespredictingself-organizingbehaviorbutthisframeworkwascreatedforthemostpartthroughcasestudiesnotexperimentationwithhumansubjectsoragent-basedmodelsAsaresultwhenoneasksfundamentalquestionsofthetheoryofself-organizingbehaviorforthepurposeofanagent-basedmodeltheanswersarenotclear

57 WefeelthatourconceptualmodelingeffortswerechallengedbythecurrentstateofsustainabilitysciencetheoryIntermsofgeographicspacehowisthestrengthofasocialactorspreconceptionsaboutaspecificsituationintheirenvironmentegthedirectharvestingoftimberresourcesinterdependentwiththeirsocialandgeographicorientationtoanyofthemyriadflowsofgoodspeoplefinanceandinformationassociatedwiththosetimberresourcesIntermsofhistoricaltimecanself-organizingbehavioramongresourceusersbesparkedbynomorethanamonth-longdecisionmakingsituationhostedinanonlineplatformHowcanself-organizingbehaviorbesustainedgivenshort-termpoliticalordisturbanceeventsmedium-termeconomiccyclesorlong-termculturalandenvironmentalchangeAsourfindingsclearlysuggestexperimentationorsimulationareespeciallyusefulinatleastoneparticularregardieitforcesonetospecifythesocialgeographicandhistoricalfactorspredictingwhenagroupofsocialactorsinacertaincontextwillself-organizetoavoiddeterioratingtheirownenvironmentandwhentheconditionstendtomakegovernmentcompulsionandauthoritynecessary

Acknowledgements

AportionofthismaterialisbaseduponworksupportedbytheNationalScienceFoundationunderGrantNumberOCI-1047916BCS-0921688andEIA0325916andNationalOceanicandAtmosphericAdministrationSectoralApplicationsResearchProgramGrantNA07OAR4310410Anyopinionsfindingsandconclusionsorrecommendationsexpressedinthismaterialarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsoftheNationalScienceFoundationSupportfromtheNationalScienceFoundationandNationalOceanicandAtmosphericAdministrationisgratefullyacknowledgedWewouldalsoliketoacknowledgetheDepartmentofGeographytheProfessionalMastersPrograminGeographicInformationSystemsforSustainabilityManagementandtheParticipatoryGeographicInformationSystemsTechnologiesGroupattheUniversityofWashingtonTheauthorsaresolelyresponsibleforthecontentForfulldocumentationofthemodelincludingcompletedescriptionsofallmodelelementsinPDFDOCXorHTMLformatorfortheworkingversionoftheAnyLogicmodelandaccompanyingrelationaldatabasepleasefeelfreetocontacttheauthors

Notes

httpjassssocsurreyacuk1717html 9 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

ACEMOGLUDDahlehMALobelIandOzdaglarA(2010)BayesianLearninginSocialNetworksMITLIDSWorkingPaper2780httppagessternnyuedu~ilobelsocialnetworks_revisedpdf

AGRAWALA(2001)CommonPropertyInstitutionsandSustainableGovernanceofResourcesWorldDevelopment29(10)1649-1672[doi101016S0305-750X(01)00063-8]

AGUIRRERampNyergesT(2011)GeovisualevaluationofpublicparticipationindecisionmakingThegrapevineSpecialIssueonChallengingProblemsinGeovisualAnalyticsJournalofVisualLanguagesandComputing22(4)305ndash321[doi101016jjvlc201012004]

ANYLOGIC(2013)httpwwwanylogiccom

AUSPITZJL(1994)ThewaspleavesthebottleTheAmericanScholar63602ndash6

BARRETEAUOampLePC(2011)UsingsocialsimulationtoexplorethedynamicsatstakeinparticipatoryresearchJournalofArtificialSocietiesandSocialSimulation144

BEISNERBEHaydonDTandCuddingtonK(2003)AlternativeStableStatesinEcologyFrontiersinEcologyandtheEnvironment17[doi1018901540-9295(2003)001[0376ASSIE]20CO2]

BENNETTRJampChorleyRJ(1978)EnvironmentalsystemsPhilosophyanalysisandcontrolLondonMetheunampCoLtd

BRAUDELF(1972)TheMediterraneanandtheMediterraneanworldintheageofPhilipIINewYorkHarperampRow

BRINBERGDampMcGrathJE(1985)ValidityandtheresearchprocessBeverlyHillsSagePublications

BROWNDGRioloRRobinsonDTNorthMandRandW(2005)SpatialprocessanddatamodelsTowardintegrationofagent-basedmodelsandGISJournalofGeographicalSystems725ndash47[doi101007s10109-005-0148-5]

CAMPAGNAM(2006)GISforsustainabledevelopmentBocaRatonCRCPress

CARSONLandBMartinB(2002)RandomSelectionofCitizensforTechnologicalDecisionMakingScienceandPublicPolicy29(2)105-113[doi103152147154302781781047]

CATENACCIMampGiupponiC(2010)PotentialsandLimitsofBayesianNetworkstoDealwithUncertaintyintheAssessmentofClimateChangeAdaptationPoliciesMilanoFondazioneEniEnricoMattei

CHAOQINGYampPeuquetDJ(2009)AGeoAgent-basedframeworkforknowledge-orientedrepresentationEmbracingsocialrulesinGISInternationalJournalofGeographicalInformationScience23(7)923ndash960[doi10108013658810701602104]

CHATERNampManningCD(2006a)ProbabilisticmodelsoflanguageprocessingandacquisitionTrendsinCognitiveSciences10(7)335ndash344[doi101016jtics200605006]

CHATERNTenenbaumJBampYuilleA(2006b)ProbabilisticmodelsofcognitionConceptualfoundationsTrendsinCognitiveSciences10(7)287ndash291[doi101016jtics200605007]

CHATERNTenenbaumJBampYuilleA(2006c)ProbabilisticmodelsofcognitionwherenextTrendsinCognitiveSciences10(7)292ndash293[doi101016jtics200605008]

CLARKWC(2007)SustainabilityscienceAroomofitsownProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica104(6)1737ndash8[doi101073pnas0611291104]

CONTER(2002)Agent-basedmodelingforunderstandingsocialintelligenceProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica997189-90[doi101073pnas072078999]

CUMMINGGS(2011)SpatialResilienceinSocial-EcologicalSystemsDordechtSpringer[doi101007978-94-007-0307-0]

DAVIESWITHERSS(2002)QuantitativemethodsBayesianinferenceBayesianthinkingProgressinHumanGeography26(4)553ndash566[doi1011910309132502ph386pr]

DELGADOLEMariacutenVHBachmannPLandTorres-GomezM(2009)ConceptualmodelsforecosystemmanagementthroughtheparticipationoflocalsocialactorstheRiacuteoCruceswetlandconflictEcologyandSociety14(1)50httpwwwecologyandsocietyorgvol14iss1art50

DEMPSTERAP(1968)AGeneralizationofBayesianInferenceJournaloftheRoyalStatisticalSocietySeriesB(Methodological)30(2)205ndash247

EARLEC(1988)TheMythoftheSouthernSoilMinerMacrohistoryAgriculturalInnovationandEnvironmentalChangeInWorsterDTheEndsoftheearthPerspectivesonmodernenvironmentalhistoryCambridgeEnglandCambridgeUniversityPress

FERGUSONML(2007)InitiativesReferendaandtheProblemofDemocraticInclusionAReplytoJohnGastilandKevinOLearyUniversityofColoradoLawReview78(4)1537ndash49

FLACHPandLachicheN(1999)1BCAFirst-OrderBayesianClassifierLectureNotesinComputerScience163492[doi1010073-540-48751-4_10]

FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

GALLOPIacuteNGC(2006)ResilienceVulnerabilityandAdaptationACross-CuttingThemeoftheInternationalHumanDimensionsProgrammeonGlobalEnvironmentalChangeGlobalEnvironmentalChange16(3)293ndash303[doi101016jgloenvcha200602004]

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

GILBERTGN(2008)Agent-basedmodelsLosAngelesSagePublications

GOLLEDGERGampStimsonRJ(1997)SpatialbehaviorageographicperspectiveNewYorkGuilfordPress

GRIMMVBergerUDeAngelisDLPolhillGGiskeJRailsbackSF(2010)TheODDprotocolareviewandfirstupdateEcologicalModelling2212760ndash2768[doi101016jecolmodel201008019]

GUNDERSONL(2009)ComparingecologicalandhumancommunityresilienceCARRIResearchReport5OakRidgeTNCommunityandRegionalResilienceInitiative

HECKERMAND(1996)AtutorialonlearningwithBayesiannetworksTechnicalReportMSR-TR-95-06RedmondMicrosoftResearchAdvancedTechnologyDivisionMicrosoftCorporation

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

HILTYLMSeifertEKampTreibertR(2005)InformationsystemsforsustainabledevelopmentHersheyPAIdeaGroupPub[doi104018978-1-59140-342-5]

JANKOWSKIPampNyergesTL(2001)GeographicinformationsystemsforgroupdecisionmakingTowardsaparticipatorygeographicinformationscienceLondonTaylorampFrancis

JEFFERSONCENTER2009CitizensJuryHandbookhttpwwwjefferson-centerorg(lastaccessed15July2009)

JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

KAHNEMANDSlovicPandTverskyA(Eds)(1982)JudgmentunderUncertaintyHeuristicsandBiasesCambridgeUniversityPress[doi101017cbo9780511809477]

KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

KIMS-YTaberCSandLodgeM(2010)AcomputationalmodelofthecitizenasmotivatedreasonerModelingthedynamicsofthe2000presidentialelectionPoliticalBehavior32(1)1ndash28

httpjassssocsurreyacuk1717html 10 16102015

[doi101007s11109-009-9099-8]

KIMS(2011)AmodelofpoliticaljudgmentAnagent-basedsimulationofcandidateevaluationJournalofArtificialSocietiesandSocialSimulation14(2)

KINGLJandGolledgeRG(1969)BayesiananalysisandmodelsingeographicresearchInMcCartyHHGeographicalessayscommemoratingtheretirementofProfessorHaroldHMcCartyIowaCityDeptofGeographyUniversityofIowa

KLINSKYSSieberRandMeredithT(2010)ConnectingLocaltoGlobalGeographicInformationSystemsandEcologicalFootprintsasToolsforSustainabilityTheProfessionalGeographer62(1)84ndash102[doi10108000330120903404892]

KONSTANJAandChenY(2007)OnlineFieldExperimentsLessonsfromCommunityLabProceedingsoftheThirdAnnualConferenceone-SocialScienceConferenceAnnArborMI

LAURIANLampShawM(2009)EvaluationofPublicParticipationJournalofPlanningEducationandResearch28(3)293ndash309[doi1011770739456X08326532]

LAVELBampDowlatabadiH(1993)ClimatechangetheeffectsofpersonalbeliefsandscientificuncertaintyEnvironmentalScienceandTechnology27(10)1962ndash72[doi101021es00047a001]

LEMPERTR(2002)Agent-basedmodelingasorganizationalandpublicpolicysimulatorsProceedingsoftheNationalAcademyofSciencesoftheUnitedStatesofAmerica99(10)7195ndash6[doi101073pnas072079399]

LIUJDietzTCarpenterSRAlbertiMFolkeCMoranEPellANTaylorWW(2007)ComplexityofcoupledhumanandnaturalsystemsScience317(5844)1513ndash6[doi101126science1144004]

MANCINICampShumSJB(2006)ModellingdiscourseincontesteddomainsAsemioticandcognitiveframeworkInternationalJournalofHuman-ComputerStudies64(11)1154ndash1171[doi101016jijhcs200607002]

MATHEVETRaphaelEtienneMLynamTandCalvetC(2011)WaterManagementintheCamargueBiosphereReserveInsightsfromComparativeMentalModelsAnalysisEcologyampSociety161

MAYRE(1982)ThegrowthofbiologicalthoughtDiversityevolutionandinheritanceCambridgeMassBelknapPress

MOSERS(2008)ResilienceinthefaceofglobalenvironmentalchangeCARRIResearchReport2OakRidgeTennCommunityandRegionalResilienceInitiative

NATIONALRESEARCHCOUNCIL(1996)UnderstandingRiskInformingDecisionsinaDemocraticSocietyNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2005)DecisionMakingfortheEnvironmentSocialandBehavioralScienceResearchPrioritiesNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2012)ComputingandsustainabilityNationalAcademyPressWashingtonDC

NYERGESTampAguirreR(2011)PublicParticipationinAnalytic-DeliberativeDecisionMakingEvaluatingaLarge-GroupOnlineFieldExperimentAnnalsoftheAssociationofAmericanGeographers101(3)561ndash586[doi101080000456082011563669]

NYERGESTLampJankowskiP(2010)RegionalandurbanGISAdecisionsupportapproachNewYorkGuilfordPress

OSTROME(2007)AdiagnosticapproachforgoingbeyondpanaceasProceedingsoftheNationalAcademyofSciences104(39)15181ndash15187[doi101073pnas0702288104]

OSTROME(2009)AGeneralFrameworkforAnalyzingSustainabilityofSocial-EcologicalSystemsScience3255939419ndash422[doi101126science1172133]

PEIRCECS(NODATE)WhatisaSignMS404httpwwwiupuiedu~peirceepep2ep2bookch02ep2ch2htm

POLHILLJGParkerDBrownDandGrimmV(2008)UsingtheODDProtocolforDescribingThreeAgent-BasedSocialSimulationModelsofLand-UseChangeJournalofArtificialSocietiesandSocialSimulation112

RALAMBONDRAINYTMeacutedocJ-MCourdierRampGuerrinF(2007)ToolstoVisualizetheStructureofMulti-agentConversationsatVariousLevelsofAnalysisInOxleyLandKulasiriD(Eds)MODSIM2007httpwwwmssanzorgauMODSIM07papers56_s43ToolsToVisualizes43_Ralambondrainy_pdf

RAMANATHANandGilbertN(2004)TheDesignofParticipatoryAgent-BasedSocialSimulationsJournalofArtificialSocietiesandSocialSimulation7(4)

RAYNERS(2003)DemocracyintheAgeofAssessmentReflectionsontheRolesofExpertiseandDemocracyinPublic-SectorDecisionMakingScienceandPublicPolicy30(3)163-170[doi103152147154303781780533]

ROBINSONG2003ASTATISTICALAPPROACHTOTHESPAMPROBLEM-CanmathematicstellspamapartfromlegitimatemailFindoutwhichapproachesworkbestinreal-worldtestsLinuxJournal(107)58

SIMONHA(1976)AdministrativebehaviorAstudyofdecision-makingprocessesinadministrativeorganizationNewYorkFreePress

SIMONHA(1981)ThesciencesoftheartificialCambridgeMassMITPress

SHOHAMYandLeyton-BrownK(2009)Multiagentsystemsalgorithmicgame-theoreticandlogicalfoundationsCambridgeCambridgeUniversityPress

SOWAJF(2000)OntologyMetadataandSemioticsLectureNotesinComputerScience186755ndash81[doi10100710722280_5]

SOWAJ(2006)WorldsModelsandDescriptionsStudiaLogica84(2)323ndash360[doi101007s11225-006-9012-y]

SPERBERD(1985)AnthropologyandPsychologyTowardsanEpidemiologyofRepresentationsMan20(1)73ndash89[doi1023072802222]

SPERBERD(1990)TheepidemiologyofbeliefsInFraserCampGaskellGThesocialpsychologicalstudyofwidespreadbeliefsOxfordClarendonPress

SQUAZZONIF(2012)Agent-basedcomputationalsociologyHobokenNJWileyampSons[doi1010029781119954200]

STEINITZC(2011)OnScaleandComplexityandtheNeedforSpatialAnalysisPositionpaperdeliveredtotheSpecialistMeetingonSpatialConceptsinGISandDesignSantaBarbaraCADecember15ndash162008httpncgiaucsbeduprojectsscdgdocspositionSteinitz-position-paperpdf

STEINITZC(2012)AframeworkforgeodesignRedlandsEsriPress

SUNR(2006)Cognitionandmulti-agentinteractionFromcognitivemodelingtosocialsimulationCambridgeCambridgeUniversityPress

THOMPSONJD(1967)OrganizationsinactionsocialsciencebasesofadministrativetheoryNewYorkMcGraw-Hill

VOGTP(2009)ModelingInteractionsBetweenLanguageEvolutionandDemographyHumanBiology81(23)237ndash58[doi1033780270810307]

VOGTPampDivinaF(2005)Languageevolutioninlargepopulationsofautonomousagentsissuesinscalinghttparnouvtnloffcampuslibwashingtonedushowcgifid=52775

VOGTPampDivinaF(2007)SocialsymbolgroundingandlanguageevolutionInteractionStudiesSocialBehaviourandCommunicationinBiologicalandArtificialSystems8(1)31ndash52[doi101075is8104vog]

WECD-WorldCommissiononEnvironmentandDevelopment(1987)OurCommonFuturehttpwwwun-documentsnetwced-ocfhtm

httpjassssocsurreyacuk1717html 11 16102015