An Agent-Based Model of Public Participation in Sustainability...

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©Copyright JASSS Robert Aguirre and Timothy Nyerges (2014) An Agent-Based Model of Public Participation in Sustainability Management Journal of Artificial Societies and Social Simulation 17 (1) 7 <http://jasss.soc.surrey.ac.uk/17/1/7.html> Received: 04-Sep-2012 Accepted: 02-Jun-2013 Published: 31-Jan-2014 Abstract This article reports on an agent-based simulation of public participation in decision making about sustainability management. Agents were modeled as socially intelligent actors who communicate using a system of symbols. The goal of the simulation was for agents to reach consensus about which situations in their regional environment to change and which ones not to change as part of a geodesign process for improving water quality in the greater Puget Sound region. As opposed to studying self-organizing behavior at the scale of a local 'commons', our interest was in how online technology supports the self-organizing behavior of agents distributed over a wide regional area, like a watershed or river basin. Geographically-distributed agents interacted through an online platform similar to that used in online field experiments with actual human subjects. We used a factorial research design to vary three interdependent factors each with three different levels. The three factors included 1) the social and geographic distribution of agents (local, regional, international levels), 2) abundance of agents (low, medium, high levels), and 3) diversity of preconceptions (blank slate, clone, social actor levels). We expected that increasing the social and geographic distribution of agents and the diversity of their preconceptions would have a significant impact on agent consensus about which situations to change and which ones not to change. However, our expectations were not met by our findings, which we trace all the way back to our conceptual model and a theoretical gap in sustainability science. The theory of self-organizing resource users does not specify how a group of social actors' preconceptions about a situation is interdependent with their social and geographic orientation to that situation. We discuss the results of the experiment and conclude with prospects for research on the social and geographic dimensions of self-organizing behavior in social-ecological systems spanning wide regional areas. Keywords: Social Actors, Public Participation, Decision Making, Sustainability Management, Geodesign, Geographic Information Systems (GIS) The Three Domains of Sustainability: Sustainability Science, Sustainability Information Science, and Sustainability Management 1.1 A widely accepted theory is that when people are left to their own devices they will simply consume the resources at their disposal and deteriorate their environment unless governments impose a control system to prevent an unavoidable tragedy of the commons. One of the interesting developments in sustainability science is the emergence of an alternate theory. Based on extensive case studies, sustainability science finds that human resource users may sometimes self-organize as a control system to make sure that the social-ecological system of which they are a dependent part remains resilient in a way they prefer (Ostrom 2009; Agrawal 2001). Systems theorists have long held that complex systems exhibit the capacity to evolve internal control systems and essentially self-regulate (Bennet and Chorley 1978). In fact, sustainability science and environmental history have both shown through historical case studies that government intervention into the self-organizing capabilities of an otherwise resilient social-ecological system sometimes accelerates its deterioration (Earle 1988). 1.2 Ostrom (2009) highlighted ten subsystem variables explaining self-organizing behavior leading to a sustainable social-ecological system. Of particular interest to us are four subsystem variables that scale the social and geographic dimensions of a decision making situation in different ways. The four variables of interest include the size of the resource system, the number of users, the amount of knowledge sharing among different resource user's mental models, and finally, the level of importance of the resource to each user. The probability that a group of resource users will self-organize as a control system is higher when these subsystem variables fall within a certain range. For example, it is unlikely that resource users will self-organize in systems spanning very large areas because of the burdens of managing extensive flows of resources. On the other hand, it is also unlikely that resource users will self-organize over very small areas that typically do not generate flows of substantial value. In sum, sustainability science holds that given the size of the area and the number of resource users, the more that resource users are able to share their mental models about the preferred attributes of the system of which they are a dependent part, and the more important the resource or ecosystem service is to the users themselves, the more likely a set of resource users will invest time and energy in managing the attributes of their system to maintain a preferred state or identity. Figure 1. Three knowledge domains of sustainability 1.3 When it comes to the actual design and testing of information technology platforms to support self-organizing behavior among geographically-distributed resource users, one can turn to sustainability information science. Figure 1 illustrates the three knowledge domains of sustainability including science, technology, and management (Kates et al. 2001; Clark 2007). While not meant to be mutually exclusive, these domains describe three kinds of expertise at work in assessing what is known about a social-ecological system, and deciding whether to intervene. Assessment and intervention is an organizational process with multiple roles and feedback loops. However, at minimum the process involves at least three activities including measuring the properties of real-life elements of a social- http://jasss.soc.surrey.ac.uk/17/1/7.html 1 16/10/2015

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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-

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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)

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Figure2AnagentactiveobjectclasswhosepropertiesstatesandbehaviorsareimplementedinAnyLogicasparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsorpresentations

Figure3AnexampleofastatechartinUMLforRealTime(UML-RT)usedtoimplementagentstatesandtransitions

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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

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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

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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)

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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

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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

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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

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1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

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

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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]

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FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

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

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

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

HILPINENR(1995)PeirceonlanguageandreferenceInKetnerKLPeirceandcontemporarythoughtphilosophicalinquiriesNewYorkFordhamUniversityPress

HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

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

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JOHNSTONKevinM2013AgentAnalystAgent-BasedModelinginArcGISRedlandsEsriPress

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KATESRKClarkWCCorellRHallJMJaegerCCLoweIMcCarthyJJSchellnhuberHJBolinBDicksonNMFaucheuxSGallopinGCGrublerAHuntleyBJagerJJodhaNSKaspersonREMabogunjeAMatsonPMooneyHMooreIIIBORiordanTSvedinU(2001)SustainabilityScienceScience292641ndash642[doi101126science1059386]

KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

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

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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)

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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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 2: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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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

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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)

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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

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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

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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

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1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

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KONSTANJAandChenY(2007)OnlineFieldExperimentsLessonsfromCommunityLabProceedingsoftheThirdAnnualConferenceone-SocialScienceConferenceAnnArborMI

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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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 3: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

Figure2AnagentactiveobjectclasswhosepropertiesstatesandbehaviorsareimplementedinAnyLogicasparametersplainvariablesJavacollectionsstatechartsactionchartsfunctionsorpresentations

Figure3AnexampleofastatechartinUMLforRealTime(UML-RT)usedtoimplementagentstatesandtransitions

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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

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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

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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)

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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

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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

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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

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1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

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KERSTENGEYehAGOMikolajukZampInternationalDevelopmentResearchCentre(Canada)(2000)DecisionsupportforsustainabledevelopmentAresourcebookofmethodsandapplicationsBostonKluwer

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KONSTANJAandChenY(2007)OnlineFieldExperimentsLessonsfromCommunityLabProceedingsoftheThirdAnnualConferenceone-SocialScienceConferenceAnnArborMI

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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]

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NATIONALRESEARCHCOUNCIL(1996)UnderstandingRiskInformingDecisionsinaDemocraticSocietyNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2005)DecisionMakingfortheEnvironmentSocialandBehavioralScienceResearchPrioritiesNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2012)ComputingandsustainabilityNationalAcademyPressWashingtonDC

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

NYERGESTLampJankowskiP(2010)RegionalandurbanGISAdecisionsupportapproachNewYorkGuilfordPress

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  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 4: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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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

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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)

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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

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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

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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

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1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

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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

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  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 5: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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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)

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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

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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

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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

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NATIONALRESEARCHCOUNCIL(2005)DecisionMakingfortheEnvironmentSocialandBehavioralScienceResearchPrioritiesNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2012)ComputingandsustainabilityNationalAcademyPressWashingtonDC

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

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httpjassssocsurreyacuk1717html 11 16102015

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 6: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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)

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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

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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

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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

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1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

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NATIONALRESEARCHCOUNCIL(1996)UnderstandingRiskInformingDecisionsinaDemocraticSocietyNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2005)DecisionMakingfortheEnvironmentSocialandBehavioralScienceResearchPrioritiesNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2012)ComputingandsustainabilityNationalAcademyPressWashingtonDC

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

NYERGESTLampJankowskiP(2010)RegionalandurbanGISAdecisionsupportapproachNewYorkGuilfordPress

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PEIRCECS(NODATE)WhatisaSignMS404httpwwwiupuiedu~peirceepep2ep2bookch02ep2ch2htm

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httpjassssocsurreyacuk1717html 11 16102015

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 7: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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

KONSTANJAandChenY(2007)OnlineFieldExperimentsLessonsfromCommunityLabProceedingsoftheThirdAnnualConferenceone-SocialScienceConferenceAnnArborMI

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

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NATIONALRESEARCHCOUNCIL(2005)DecisionMakingfortheEnvironmentSocialandBehavioralScienceResearchPrioritiesNationalAcademyPressWashingtonDC

NATIONALRESEARCHCOUNCIL(2012)ComputingandsustainabilityNationalAcademyPressWashingtonDC

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

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httpjassssocsurreyacuk1717html 11 16102015

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 8: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 9: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

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

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HILPINENR(2007)OntheObjectsandInterpretantsofSignsCommentsonTLShortsPeircesTheoryofSignsTransactionsoftheCharlesSPeirceSocietyAQuarterlyJournalinAmericanPhilosophyVolume43Number4Fall2007pp610ndash618

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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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 10: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

1Thetermpublicparticipationincludesorganizedprocessesbyelectedofficialsgovernmentagenciesorotherpublicorprivate-sectororganizationstoengageaffectedpartiesandtechnicalspecialistsinenvironmentalassessmentplanningdecisionmakingmanagementmonitoringorevaluationTheseprocessessupplementtraditionalformsofpublicparticipation(votingforminginterestgroupsdemonstratinglobbying)bydirectlyinvolvingthepublicinfunctionswhichwhenconductedingovernmentaretraditionallydelegatedtopublicsectorexecutives

References

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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]

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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]

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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

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FOXE1971HistoryingeographicperspectiveTheotherFranceNewYorkWWNorton

FOXE1980TherangeofcommunicationsandtheshapeofsocialorganizationCommunication5275ndash287

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

GASTILJReedyJandWellsC(2007)WhenGoodVotersMakeBadPoliciesAssessingandImprovingtheDeliberativeQualityofInitiativeElectionsUniversityofColoradoLawReview78(4)1435-1488

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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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References
Page 11: An Agent-Based Model of Public Participation in Sustainability Managementjasss.soc.surrey.ac.uk/17/1/7/7.pdf · Modeling an Agent Object for Public Participation in Decision Making

[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

  • Abstract
  • The Three Domains of Sustainability Sustainability Science Sustainability Information Science and Sustainability Management
  • Modeling an Agent Object for Public Participation in Decision Making
  • Research Design for a Simulated Online Field Experiment
    • Social amp Geographic Properties of Agents
    • Conceptual Properties of Agents
    • Changes in the Conceptual Properties of Agents
    • Symbolic Properties of Agents
      • Results
        • Scaling did not affect conceptual change on a per agent basis
        • Scaling may affect the choices agents make
          • Conclusion
          • Acknowledgements
          • Notes
          • References

Public Participation Guide - United States Environmental … · 2014-05-22 · Public Participation Guide EPA launched the International Public Participation Guide on its website,

Public Participation Guide - United States Environmental … · 2014-05-22 · Public Participation Guide EPA launched the International Public Participation Guide on its website,

Model Participation Rules Version 4 - ARNECC · Participation Rules – Version 4 – April 2017 Page 3 . ... SCHEDULE 9 – IDENTITY AGENT CERTIFICATION..... 52. Participation Rules

Model Participation Rules Version 4 - ARNECC · Participation Rules – Version 4 – April 2017 Page 3 . ... SCHEDULE 9 – IDENTITY AGENT CERTIFICATION..... 52. Participation Rules

Appendix B PUBLIC PARTICIPATION - SAHRIS | SAHRA · Project number 113223 File Public Participation Report -KwaZulu-Natal.docx, 6 November 2017 Revision 0 6 PUBLIC PARTICIPATION PROCESS:

Appendix B PUBLIC PARTICIPATION - SAHRIS | SAHRA · Project number 113223 File Public Participation Report -KwaZulu-Natal.docx, 6 November 2017 Revision 0 6 PUBLIC PARTICIPATION PROCESS:

Public Participation Training

Public Participation Training

Public Participation Plan - bart.gov · 7/8/2011  · include: public participation design factors; a range of public participation methods to provide information, to invite participation

Public Participation Plan - bart.gov · 7/8/2011  · include: public participation design factors; a range of public participation methods to provide information, to invite participation

Public Participation Manual: Chapter 6 – PUBLIC ... · CHAPTER 6 PUBLIC PARTICIPATION TASKS AND TECHNIQUES. ... 6-24 Exhibit 6-8 Public Participation Plan Outline ... Public Participation

Public Participation Manual: Chapter 6 – PUBLIC ... · CHAPTER 6 PUBLIC PARTICIPATION TASKS AND TECHNIQUES. ... 6-24 Exhibit 6-8 Public Participation Plan Outline ... Public Participation

IDP Public Participation: Learning Points - Western Cape · 2005-07-25 · IDP Public Participation: Learning Points. 2 Contents Public Participation : ... Regional Plenaries: Select

IDP Public Participation: Learning Points - Western Cape · 2005-07-25 · IDP Public Participation: Learning Points. 2 Contents Public Participation : ... Regional Plenaries: Select

Public participation in eia

Public participation in eia

Public and Stakeholder Participation

Public and Stakeholder Participation

Public Participation in Environmental Decision Making in ...€¦ · Public Participation – Part II Public Participation in Environmental Decision Making in Alberta: Part II Appeals

Public Participation in Environmental Decision Making in ...€¦ · Public Participation – Part II Public Participation in Environmental Decision Making in Alberta: Part II Appeals

Public Participation Adjourn

Public Participation Adjourn

PUBLIC PARTICIPATION PUBLIC PARTICIPATION PROCESS … · PUBLIC PARTICIPATION PUBLIC PARTICIPATION PROCESS ... Tamara Crewe-Brown 27 February 2013 E-mail Further to my ... subdivision

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Application of Public Participation Principles in ... 1 Application of Public Participation Principles in EIA Strengthening Public Participation in Environmental Management in Indonesia

Application of Public Participation Principles in ... 1 Application of Public Participation Principles in EIA Strengthening Public Participation in Environmental Management in Indonesia

Public Transportation Participation (PTP) Pilot Grant Program · Public Participation Practice and the Development of Dots & Dashes Public participation in its various forms has become

Public Transportation Participation (PTP) Pilot Grant Program · Public Participation Practice and the Development of Dots & Dashes Public participation in its various forms has become

Public Participation in Environmental Decision Making in ...elc.ab.ca/Content_Files/Files/ProcessGuidePartI_4Feb2010.pdf · Public Participation – Part I Public Participation in

Public Participation in Environmental Decision Making in ...elc.ab.ca/Content_Files/Files/ProcessGuidePartI_4Feb2010.pdf · Public Participation – Part I Public Participation in

PUBLIC PARTICIPATION REPORT - mawenje.com PARTICIPATION REPORT... · Koedoespoort Landfill Site Public Participation Report MCA-Public Participation Report 7 1.4 PUBLIC PARTICIPATION

PUBLIC PARTICIPATION REPORT - mawenje.com PARTICIPATION REPORT... · Koedoespoort Landfill Site Public Participation Report MCA-Public Participation Report 7 1.4 PUBLIC PARTICIPATION

PUBLIC PARTICIPATION NOTICE

PUBLIC PARTICIPATION NOTICE

Training Session PUBLIC PARTICIPATION PUBLIC PARTICIPATION Catarina Freitas Environmental Planning Department City Council of.

Training Session PUBLIC PARTICIPATION PUBLIC PARTICIPATION Catarina Freitas Environmental Planning Department City Council of.

Public Participation Plan: A Strategy for Citizen Involvement · public participation strategy and procedures, as well as the federal mandates that inform DVRPC’s public participation

Public Participation Plan: A Strategy for Citizen Involvement · public participation strategy and procedures, as well as the federal mandates that inform DVRPC’s public participation

Public Participation 2.0

Public Participation 2.0