Exploring the Impacts of Networks on Informational and ...

Policy Research Project (PRP), LBJ School of Public Affairs, The University of Texas at Austin, May 2018.

Project Director: Dr. Varun Rai

ExploringtheImpactsofNetworksonInformationalandEconomicInterventionsinSolarPVAdoption

StudentTeam:AmaraUyannaandMatthewHaley

InternalTechnicalLead:D.CaleReeves

AdvisorandProjectDirector:Dr.VarunRai



Abstract

FornoveltechnologiessuchasdistributedPV,effectiveapproachestoacceleratingadoptionmustfacilitateacomplexhousehold-leveldecision-makingprocesscharacterizedbybotheconomicandinformationalbarriers.Usinganempiricallygroundedandvalidatedagent-basedmodel(ABM)ofsingle-familyhouseholdPVadoption,theprojectteamsimulatedarangeofinformation-basedstrategiesincludingrecruitinginterestedindividualsto“champion”installingsolarandconnectingcurrentsolarownerstopotentialadopters.Thesestrategiesaimtoincreaseadoptionbyexplicitlyleveraginginformationexchangeatthelocallevel,whichisknowntobeanimportantdriverofPVadoption.Resultsoftheinformation-basedstrategiesarecomparedtheresultstoasimplesimulatedeconomic-basedstrategy(subsidizedadoption).

Threekeyfindingsemergedfromtheanalysis.First,information-basedstrategiesthatbreakfreeofexistingsocialnetworksandcreatenew“weakties”amongpreviouslyunconnectedindividualsappeartobemoreeffectivethaninterventionsthatrelysolelyonexistingpersonalconnectionstospreadinformation.Second,whenpotentialadoptersarealreadydenselyconnectedandinformationflowsfreelybetweenindividualsbasedonpre-existingrelationships,providingadditionalnewinformationisnecessarytoincreaseadoption.Third,simulatedinformation-basedstrategiescanincreaseadoptionwithareasonableestimatedreturn-on-investmentcomparedtoasimulatedeconomic-basedstrategy.

Thesefindingsyieldtwoconclusions.First,information-basedstrategieshavethepotentialtoplayanimportantroleinPVadoptionwherepotentialadoptersfaceinformationalbarrierstoadoption.ThoseinterestedinPVadoptionmayusefullycontinuedesigning,implementing,andevaluatingstrategiesthatexplicitlyleverageinformationexchangeamongpeers.Second,information-basedstrategiesshouldaimtoformnewtiesandinformationexchangebetweenindividualsratherthansolelyleveragingpre-existingrelationships.Whenencouragingnewconnectionsisnotpossible,newinformation—asintraining—mightinsteadbeprovided.Futureresearchcouldexploretheeffectivenessofcombinationsofinformation-basedandeconomic-basedstrategieswhenappliedsimultaneously.



IntroductionRelationshipbetweenEconomicsandsolarPVadoptiondecision-makingOverthetwopastdecades,therehasbeenaglobalincreaseineffortstosupportsolaradoption.GlobalinvestmentinsolarPVismotivatedbyadesiretocurbglobalwarmingtransitiontoemissionfreeenergygeneration.EconomicincentiveshavebeenasignificantdrivingforcebehindresidentialsolarPVadoption.Federalinvestmenttaxcredits(FITC),feed-in-tariffs(FIT)aswellasvariousformsofrebatesandsubsidieshavebeenintroducedbygovernmentsworldwide.InBelgium,acombinationofsubsidies,taxcreditsandloanswereintroducedbetween2002and2015toincreasesolarPVadoption.By2012,BelgiumhadasolarPVadoptionrateof8.5%(DeGrooteetal.2016).In2002,Germanyintroducedfeed-in-tariffstoencouragetheadoptionofrenewableenergytechnologiesincludingsolarPV.By2011,theFITpolicieswereintheirsecondphaseofimplementation,andtherehadbeenasignificantincreaseinsolaradoptioninthecountry.Bymid2012,5.1%oftheGermannationalelectricityproductionwasfromsolarPVinstallations(Fulton,Capalino,&Auer2012).TheUnitedStateshasalsoadoptedpoliciestopromotesolaradoptionamongcitizens.In2011,thefederalgovernmentdisbursed$1.1billioninformoffinancialassistancetoincreasesolaradoption.Thisamountwasa500%increasefromtheamountallocatedin2007.Inthesameyear,legislatureacrosstenstatesincludedprovisionsforfinancialincentivestoincreasesolaradoption(Chernyakhovskiy,Ilya,2012).Figure1belowpresentstheobservedandpredictedgrowthofinstalledU.S.solarPVcapacityfrom2010-2023(WoodMackenzie2018).In2012,Austin'selectricutility–AustinEnergy-implementedthevalueofsolar(VOS)policy.TheVOSisaversionofnetenergymetering(NEM)policiesthatenablesthesecustomerstoreceivecreditforexcessrenewableenergygenerationexportedtothegrid.Typically,underNEMthesecustomersarecreditedatthesameretailrateforwhichthecustomerspurchaseelectricity.VOSdiscountsthecreditcustomersreceivebasedonthevalueofthesolartotheelectricitygrid,whichAustinEnergyassessesannually.InAustin,VOSreplacedNEMandisonlyapplicableinresidenceswithsolarPVsystemssmallerthan20kW(USDOE,2018).FollowingtheimplementationofNEMinTexas,statelegislatorsbegantointroduceNEMlegislationtotheir2013legislativesession.InQ1of2013,therewasa53%increaseintherateofsolaradoptionacrosstheUS.TocontinuegrowthinPVadoptionitisimportantforstateandlocalgovernmentstoprovideinformationandfinancialresourcestosustaintheincreaseinsolarPVadoption(Nolletal.2014).



Figure1EmpiricalandpredictedgrowthininstalledcapacityofSolarPVintheUnitedStatesfrom2010-2023

(WoodMackenzie2018)

RelationshipbetweenInformationandsolarPVadoptiondecision-making

Informationistheadhesivethatbindsdifferentchoices,businessstructuresanditisanintegralpartofaproduct’svaluechain(Evans&Wurster,1997).Wordofmouth(WOM)especially,isapowerfulmechanismforconveyinginformation(Jalilvandetal.,2011).Neighborhoodpeer-effectshavebeenparticularlyeffectiveindrivingtheinnovationofnew,riskyproductsandhavebeenidentifiedasverybeneficialwheninfluentialindividualsinsocialnetworksareconnectedinwaysthateaseinformationdistribution(Kumaretal.,2007).Potentialsolaradoptersfaceseveralbarrierstoadoptionbeyondeconomics.Uncertaintiesabouttechnologyperformance,adoptionmodels(buyorlease),localpolicycontextsandalackofindividuallyrelevantinformationcomplicatethedecisiontoadoptsolarPV(Rai&Robinson2013).Informationinterventions–suchassolarmarketingandutilityseminars-areapowerfulresourceforovercomingtheseuncertainties.PeereffectsarerecognizedtobeparticularlypowerfulinpromotingthediffusionofsolarPV.BollingerandGillingham(2012)findthateachinstallationofsolarPVintheCaliforniamarketincreasestheprobabilityofanotheradoptioninthesamezipcodeby0.78.Informationinterventionsthatareabletoleveragepeereffectsandwordofmouthhavepotentialtoreducetheuncertaintyandnon-monetarycostsassociatedwithPVadoption.InformationinterventionsareabletoidentifytherelativeadvantagesofsolarPV,demonstratethecompatibilityofsolarPVwithacustomer'sbeliefsystem,reduceperceptionsoftechnologyandpolicycomplexityandshowtheresultsofotherinstallations(Nolletal2014.)ThisresearchdevelopsanempiricallyjustifiedmethodformodellinginformationandeconomicinterventionsdesignedtoforincreasesolarPVadoption.WeusetheSECADmodel–anempiricallygeneratedagent-basedmodelofresidentialsolarPVadoptioninAustin,TX–totestandevaluatefour“real-world”policyinterventionsdesignedtoovercomenon-monetarybarrierstosolarPVadoption.WeassesstherateofPVdiffusionbyestablishingbaselineeffectivenessmetricsforvariousinterventionsinanempiricallyandtheoreticallygroundedsetting.



BackgroundOurresearchattemptstoimprovepolicyandbridgeexistinggapsbetweeninterventiontargetingbasedonnetworkstructuralcharacteristicsandreal-worldsolarPVinterventions.Wereviewtheavailableliteratureonthesetopicsandhighlightthefindingsthatinformourwork.ReviewofSolarInformationInterventions

TounderstandthecurrentrangeofinformationprogramsrelatedtosolarPVwesurveyedtheliterature.InparticularwefocusedonprogramspromotedbySolarCommunityOrganizations(SCOs.)SCOsareformalorinformalorganizationsthatattempttopromoteadoptionofsolarPV.SCOsprovideaccesstocredibleandtransparentinformationaboutthebenefitsofsolarPVandactivelycampaigntopromoteadoptionofsolarPVwithintheiroperationalboundaries(Nolletal2014.)TheresultsofthisreviewaresummarizedinTable1.Solarinformationprogramsarediverseandvarysignificantlyfrommarkettomarket.Thisislikelyduetothegeographicalvariationinsolarmarketsandsolarpolicyacrosstheworld.Assuchmostinformationcampaignsarelocalandtargetedtospecificcommunities.Mosttargetedprograms–suchastheSolarChampionsprogramortheHappyHourprograms-relyonindividualvolunteerstospreadinformationwithintheircommunities.Otherprograms,includingtheOregonSolarAmbassadorprogram,createdatabasestoconnectpotentialsolaradopterswithpeoplewhohavealreadyadoptedsolar.Criticaltothesuccessofsolarinformationinterventionsisestablishingtrustbetweentheorganizationsthatdistributeinformationandtherecipientsofinformation(Nolletal2014.)Manyinterventionsprimarilyfocusresourcesontrainingandeducatingprogramvolunteerswhohaveestablishedtrustedrelationshipswiththeircommunities.

ProgramType Description

UtilityMailers/Pamphlets/CallCenters/Massmarketadvertising

Themostextensiveinformationalappealstosolarconsumershavetraditionallybeenthroughmassmarketmechanisms(Costanzoetal.1986).Acursoryinternetsearchrevealsthatalmosteverysolarstakeholderreliesheavilyonbroadlydistributedmass-marketinformationsuchasmailers,electronicadvertisingandpamphletsasaprimarymarketingmechanism.Massadvertising,howeverisahighlypassiveformofinformationdiffusion.SurveysshowthatmassmediaisineffectiveatinfluencingmembersofthepublictoovercomeadoptionbarrierstoPV(Palm2016).Thehighattitudinalbarrierstosolaradoption(Raietal.2016)limitstheabilityofpassiveinformationtoleverageactivepeereffectstospreadinformation.

OpenHouseorSolarGuidedTours

SolartoursandopenhousesareanemergingPVadvocacymechanism.TheAmericanSolarEnergySocietycoordinatestheNationalSolarTour,wheremembersofthepublicareinvitedto



visitthehomesofpreviousPVadopters.In201290,000participatedintheASESNationalTour.Thetourengagesactivepeereffectsbygivingpotentialsolaradopterstheopportunitytointeractwithexistingadopters.SolartourshavealsobeeninstrumentalinthehighrateofPVdiffusiontheSwedishcommunityofBerg(Palm2016).

Education/SchoolEvents

SolarOregon,PGEandmanyotherutilitiesinstallPVonPublicSchoolsandhavedesignedsolarbasedcurriculumtrainingpackagesforteachersthatfitstateengineeringanddesignandsciencebenchmarks.Between2004and2009PGEsolarcurriculumwastaughttoover200,000students(CleanEnergyGroup2009).

SolarChampions

Manyutilitiesandsolaradvocacyorganizationsoffer"SolarUniversities."Theutilityrecruitsmembersofthepublicfromneighborhoodsinitsserviceareaandprovidesthemtechnicaleducationandtraining.Thesesolarchampionsarethenempoweredtocoordinate,trainandleadsolarprogramsintheirowncommunities.

PorchTalks

PermembersoftheMuellerMegawattprojectinAustin,themosteffectiveapproachtospreadingsolarinformationwaswhattheycalled“porchtalks.”ThesetalksinvolvedsolaradvocateswhowerecommunitymemberssittingontheirporchafterdinnerandaskinganypasserbyiftheyknewaboutsolarandwereinterestedinputtingPVontheirroof(Nolletal.2014)

CelebrityEndorsement

Celebrityendorsementsaredesignedtoincreasecustomerknowledgeandawarenessofaproduct(Kowalska-Pyzalska2017).CaliforniasolarprogramadministratorsattributepartofsolartakeoffinCaliforniatotheeffectofcelebrityendorsementfromtheGovernorArnoldSchwarzenegger(CleanEnergyGroup2009).

Local

Seminars,HappyHoursandWorkshops

Attendingseminars,workshopsandhappyhourshostedbySCOs,utilitiesorsolarretailersisinfluentialintheadoptionprocess(Palm2016).HappyhourswereidentifiedascrucialtothesuccessoftheMuellerMegawattproject(Nolletal.2014).SCOsalsocoordinateworkshopsandseminarstoeducatemembersaboutthebenefitsofsolar(Nolletal.2014).seminars,workshopsandhappyhourshostedbySCOs,utilitiesorsolarretailersisinfluentialintheadoptionprocess(Palm2016).HappyhourswereidentifiedascrucialtothesuccessoftheMuellerMegawattproject(Nolletal.2014).SCOsalsocoordinateworkshopsandseminarstoeducatemembersaboutthebenefitsofsolar(Nolletal.2014).



GroupCompetitions

SmartPowermanagedaprogramthatorganizedfriendlycompetitionsbetweenmunicipalitiestoencouragesign-upstogreenpowerprograms(CleanEnergyGroup2009).Similarprogramsareenvisionedforneighborhoodsolarinstallations.EnergyTrustofOregonapproachedcompaniesthathadpubliclyadvocatedforsolartoruninter-companychallengestoenlistemployeesasnewsolarcustomers(CleanEnergyGroup2009)

OregonSolarAmbassadorProgram

Oregoncreatedadatabasewhereprospectivesolaradoptersareputincontactwithexistingsolaradopterstodiscusswhytheychosetoadoptsolar.Adoptersareencouragedtosharekeyinformationthatenabledthemtoovercomeattitudinalbarrierstosolaradoption(Nolletal.2014).

Table1Descriptionofreal-worldsolarinformationinterventions

ReviewofNetworkInformationDiffusion

Theliteraturesuggeststhatasmallsubsetofinfluentialindividualscanshifttheattitudesofthemajority(Kovács&Barabási2015).Theseinfluentialindividualsarelocatedatcentralnetworknodes,thataremostefficientatdisseminatinginformationthroughouttheentirenetwork.Toinvestigatethestrengthofdifferentstrategies,weincorporatednetworkcentralityinouranalysis.Specifically,weexploredtwotargetingmethodsforseedinginformation:

o Randomlyseedingagentsasabaselinetoevaluatealternativestrategies(Araletal.2013).

o Targetinghighlyconnectedagents(Rai&Robinson2015;Centola&Macy2007).

ReviewofSECADABM

Agent-basedmodellingenablesustostudyinteractiveaspectsofanagent’slife.Theabilitytomanipulatevariousinteractionsenablesustoexplorethedifferentwaysthroughwhichindividualdecisionmakingcanmanifestasobservablemacroscopicprocesses(Rai&Robinson2015).UsingABM,wecanbridgethegapbetweenindividualbehavioralresponsesandmoreaggregatelong-termpossibilities(Hoganetal.2004).TheSECADABMfirstpublishedbyRobinson&Rai(2015)isatheoreticallybasedmodelofsolaradoptioninAustin,Texas.Themodelcontains~177,000agentsthatrepresenteachoftheGISmappedhouseholdsinAustin.Themodelassignseconomicandattitudinalattributesforeachagentbasedonsurveyandpropertyvaluedata.Themodelisempiricallyvalidatedandcanpredictadoptiontrendsonatestdata-setwithheldduringmodelfitting.

InterventionExperimentsWeusetheSECADmodeltounderstandhowinformationprogramscandriveadoption.Inthispaper,wejustifyhowinformationwillupdateattitudewithinthemodelandestablisha



methodofdeterminingthecostofinformationprograms.Inthisresearch,weexplorethenatureofsocialnetworksandtheirimpactsonincreasingsolarPVadoption.Thisworkwillattempttoidentifythecharacteristicsofeffectivesolarinformationprograms.Interventionsareassessedontheirabilitytoincreaseadoptionswhileminimizingcost.Thepurposeoftheseexperimentswillnottobetodesignanoptimalprogramthatmaximizesadoptionforminimalcost,buttotestseveralinterventionstrategiesandrationalizetheirresults.Weareaimingtoidentifysalientprogramfeaturesthatimproveadoption.

MethodsABMOverviewWeassesstheimpactofseveralpracticalsolarinformationprogramsthatcurrentlyarebeingimplemented.Fourrealworldinformationinterventions–chosenfromtheninepresentedabovewillbereplicatedwithintheSECADmodel.UsingtheSECADmodelwecanevaluatethestrengthofinformationinterventions,potentialreturnoninvestmentandtheintervention'sabilitytoleveragewordofmouthattitudediffusion.Wecanalsoevaluatethedifferentinformationstrategiesagainstoneanother.

TheSECADmodelcanbeusedavirtuallaboratorytotesttheefficacyofvarioussolarinterventions.TheSECADmodelallowstestingofpurelyinformationalinterventionswithinthemodel.TheseinterventionsareperformedwithinSECADbyestablishingconditionsthataltertheevolutionofagent'ssociallyinformedattitudeswithintheSECADmodel.WithinSECADthesociallyinformedattitudeofagentsisrepresentedbythenumericalparametersia.Onceanagentssiaexceedsacertainthreshold(siathreshold)theyareclassifiedasattitudinallyactivated.TheSECADmodelreliesonadualthresholdmodelforanagenttobeconsideredasolaradoptertheymustbebothattitudinallyandeconomicallyactivated(seeFigure2).Tobeeconomicallyactivatedanagent’sperceivedallowablepaybacktimeisgreaterthantheirempiricalpaybackperiod.TheseconditionsmayalsobealteredtostudytheoutcomeofeconomicinterventionswithinSECAD,howevereconomicinterventionsareoutsidethescopeofthiswork.ThedesiredoutcomeofinformationinterventionswithinSECADwillbetoincreaseindividualagent’ssiaanddrivesolaradoptionwithinthemodel.

Figure2FlowsheetforSECADadoptiondecisionprocess(Rai&Robinson2015)



TheSECADmodelallowsthemodelertounderstandtheimpactofinformationinterventionsovertime.Ateachtimestep(t=0,1,2,3,4....)agents'attitudesaboutsolarPV(sia)andtheuncertaintiesaroundthoseattitudes(U)areupdatedthroughinteractionswithotheragents.ThisallowsustodynamicallymonitorhowinformationinterventionscanmanifestasattitudechangesovertimewithintheSECADmodel.Thelayerofinteractionspossibleintheagent-basedmodelallowsustorecreatethehumanrealityinwhichvariousinformationdiffusionprocessareinterconnected.Interactionbetweenagentsismodeledusingarelativeagreement(RA)algorithm(Deffuantetal.,2002,2000;HegselmannandKrause,2002;MeadowsandCliff,2012).Ateachtimesteppairsofagent’sexchangeattitudeswithagentswithintheirsocialnetworkthatsharesimilarattitudestowardsolar(sia)anduncertaintyaroundthoseattitudes(U).AdetailedformulationoftheRAalgorithmispresentedinRai&Robinson2015andRobinson&Rai2015.Fortheinformationinterventionsmodelledinthispaper,theSECADmodelranfor24quarters(2008-2014),withfourtimestepsforagentupdatesperquarter.

Twoprimarytypesofnetworks–geographicalandsocial-aredefinedwithintheSECADmodel.Geographicneighborhoodsaroundanagentiaredefinedwithinthemodelasthecollectionofagentsthatarewithinacertainradius(2000ft)ofagenti.WithinSECADthemeannumberofagentswithineachgeographicalneighborhoodis498.

Socialnetworksarederivedfromgeographicalneighborhoods;however,theyarefurtherconstrainedbytheeconomicsimilarity(calculatedusinghomevalueasaproxyforwealth)ofagents.Therefore,onlyeconomicallysimilaragentswereallowedineachsocialnetwork.Finally,10%ofeachsocialnetwork’sconnectionwererandomlyrewiredwithagentsanywherewithintheSECADmodel,toaddadegreeofrandomizationtothesocialnetworks.ItisthesocialnetworksforeachagentwithinSECADthatdictatewhoeachagentupdateswithduringrelativeagreement.Agentswillonlyupdatesiawithotheragentswithintheirsocialnetwork.WithinthispaperweexploreonlythemanipulationswemaketoSECADtoperformourinterventionexperiments.ForacomprehensiveexplanationofthefullyvalidatedSECADmodelrefertoRai&Robinson2015andRobinson&Rai2015.

SelectionofRealWorldSolarInformationProgramsFromtheninesolarinformationinterventionswereviewedweselectfourtoreplicatewithintheSECADmodel.Thefourchoseninterventionsare:

• SolarChampions• PorchTalks• PhoneAmbassadors• CelebrityInterventions

Twoprimaryfactorsdictatedthechoiceoftheseprograms,theirsimilaritywithotherprogramsreviewedandtheirabilitytoleveragethesocialnetwork/wordofmouthinteractionsthatgoverntheSECADmodel.Forexample,pamphletsandmailerswereignoredfrommodelingsinceitisunlikelythattheywouldgeneratesignificantfollowonwordofmouthinteractions



SolarChampionswaschosensinceitaprogramthatleveragesthesocialnetworksofthepeoplewhoparticipateintheprogramtospreadinformation.Inthissense,itisliketheHappyHourandWorkshopsprograms.Inourmodeling,weusetheSolarChampionprogramasanumbrellaforsimilarprogramsthatrelyonparticipant’ssocialnetworkstospreadinformation.PorchtalkswaschosenforexperimentationinSECADsinceitaprogramthatutilizesthegeographicnetworksofparticipantsintheprogramtospreadinformation.Inthissense,itislikesolaropenhousesandguidedtours,inthattheinterventionisconfinedwithinalocalneighborhood.Inourmodeling,weusetheporchtalkprogramasanumbrellaforsimilarprogramsthatrelyonparticipant’sgeographicalneighborhoodstospreadinformation.Phoneambassadorsandcelebrityinterventionswerechosenbecauseoftheirabilitytospreadinformationbeyondtheexistingnetworkofindividuals.Theseprogramscanspreadinformationbetweenpeoplewhohavenopre-existingconnectiontooneanother.InformationalSeedingExperiments

SeedsandFollow-OnsSeedsaretheagentswhowegiveinformationtoowithintheexperimentalinterventions.Thesearetheinfluenceragentswhoareabletopassoninformationwithinthemodelthatupdatesotheragent’sattitudes.Forexample,inthecaseofthesolarchampionsprogram,theseedistheagentwhobecomesachampionbyreceivingonsolartrainingfromhisutility.Existingagentsarechosenasseedswithinthemodel(exceptforthecelebrityintervention)basedoneithertheirattitude(siai>siathresh)oriftheyareanadopter,inwhichcasetheywillstillhaveattitudegreaterthantheadoptionthreshold.Seedsbydefinitionhavehighsia.Withineachoftheinterventionexperimentsweassignbetween1and500agentsasseeds.

Follow-onsaretheagentsthattheinitialseedagentsareabletoinfluencewiththeinformationfromtheintervention.Follow-onsaretheagentswhoareinfluencedandupdatetheirattitudesonsolar.Dependingontheinterventionbeingmodeledpotentialfollow-onsareidentifiedasagentswithinaseedssocialnetwork,agentswithinaseedsgeographicalnetwork,agentsthatareinterestedinsolar(approachingattitudinalactivation)orcanhavenopre-existingconnectiontotheseedagent.Thefinalgroupoffollow-onsisdeterminedbyrandomlyselectingapercentageofagentsfromthepoolofpotentialfollow-onagentsidentifiedineachintervention.Thepercentageofagentsselectedasfollows-onsisexpressedasaratefrom0-1.Thisisknownasthefollow-onrate.Thisraterepresentshoweffectiveaseedagentisininfluencingtheirfollowers.

TheimpactofinformationwithinSECADTheSECADmodelusesadualthresholdconcepttodeterminewhetheranagentwilladoptsolarPV.Anagentmustbebothattitudinallyandeconomicallyactivatedifitistoadopt.Informationalandeducationalsolarincentiveprogramsaredesignedtoinfluenceaperson’sattitudetowardssolar.Tomodeltheeffectsofinformationalinterventionswithin



theSECADmodelwehavejustifiedtwoalternativesforhowanagent’sattitudesiawillbemodifiedwhenexposedtoinformationresources.

- MaximalEffectUpdating

Weassumethateachfollow-onagentthatencountersinformationdisseminatedfromourmodeledinterventionswillbecomeattitudinallyactivated(siai>siathresh).Theamountofsolaradoptiondrivenbythese“perfectinformationprograms”isatheoreticalupperlimittowhatevercanbeachievedforeachinterventionwithinthemodel.However,this“idealizedattitude”adjustmentallowsustoeasilyevaluatecompetinginformationalinterventionstrategiesagainstoneanotherbyevaluatingtheupperboundforeachstrategy.

- RelativeAgreementUpdating

RelativeagreementupdatingbuildsuponthetheorydevelopedwithintheoriginalSECADmodel.UnliketherelativeagreementprocesswhichtakesplaceintheSECADmodel4timesperquarter,interventionupdatesonlyoccurintimeperiodstheinterventionisactive(Fortheseexperimentsthisisalwaysatinitializationt=0).Ateachtime-step,seeds–whichhavehighattitude-willupdateusingrelativeagreementwithallthefollow-onstheyareconnectedto.Theeffectoftheseupdateswillbetoincreasefollow-onagentattitudesthroughoutthemodel.Howseedsareselected

Twotargetingmechanismsareusedforidentifyingseedagentswithintheinterventions:- Randomselection

Fromthepoolofpotentialseedagents,werandomlyselectseeds.Thistargetingstrategyrepresentsaminimumcostapproachtoselectinganinformationprogramparticipant.

- HighKselection

Fromthepoolofpotentialseedagents,weselectseedsthathavethehighestnumberofconnections(K)tootheragents.Essentially,weidentifytheagentswiththemostpotentialfollow-ons.Thistargetingstrategyrepresentsamaximumcostapproachtoselectinganinformationprogramparticipant,whopotentiallycaninfluencethemostfollow-onagents.Otheradvancedseedtargetingtechniqueswereconsidered,howeverimplementationofthesemethodsisbeyondthescopeofthisreport.

InterventionTimingWhenmodelingeachinterventionwithinSECADwegenerateseedandfollow-onagentsonlyonce–duringinitializationatt=0.While“inreality”informationprogramsarelikelytobecarriedoutoveradurationoftime,forthepurposeoftheseexperimentsweseedinformationonlyoncesotheeffectofeachinterventioncanbeobservedwithinSECADovertime.Seedingatmultiplytimestepswouldcomplicateseparatingtheadoptionsgeneratedbyeachindividualseedingevent.

InterventionDesign



ThedesignofeachindividualinterventionissummarizedintheTable2below.Interventionsaredesignedtoapproximatetherealityofeachofthefourselectedinterventions.Thesuccessofeachadoptionismeasuredbyitsabilitytogenerateadditionaladoptionsoverthebaseline(no-intervention)model.

Program Seeds/Follow-OnRate Mechanismwithinmodel

SolarChampions:

Seeds:AgentsthatareseededwithinformationareSolarChampions.SolarChampionsaretrainedbytheutility/Solaradvocacyorganizationanddisseminateinformationtotheirimmediatesocialnetwork.

FollowOn:Follow-onsaretheagentswithinthesolarchampionseedssocialnetwork.

InitialTargeting:Tobeachampion(seed)anagentmusthavesiagreaterthansiathreshold.Atinitializationagentswithhighestattitude(sia>siathreshold)areidentifiedaspotentialchampions.Fromthepoolofpotentialchampions,weselectagentsaschampions(seeds)randomlyorbasedonhighK.

Follow-onsareagentsthatarewiththechampionsimmediatesocialnetwork(Alters).

Operation:Theinformationeffectofagentsispassedontofollowonagentsusingrelativeagreementalgorithmor‘maximaleffect’.

SolarAmbassadors

Seeds:AgentsthatareseededwithinformationareSolarAmbassadors.Solarambassadorsareaselectgroup(phonebook)ofagentswhohaveadoptedsolar.

FollowOn:Follow-onsareagentswhoareinterestedininstallingsolarbutrequiremoreinformation.Theyreachouttoambassadorstogetmoreinformation.

InitialTargeting:Atinitializationagentswithwhohaveadoptedsolarareidentifiedaspotentialambassadors.Fromthepoolofpotentialambassadors,weselectagentsasambassadors(seeds)randomlyorbasedonhighK.

Potentialfollow-onsareagentswithinthemodelthatareclosetobeingattitudinallyactivated(Thisiscalculatedusingathresholdof0.6siathreshold.)Theseagentsdonotneedtohaveanypriorsocialorgeographicconnectionwithambassadors.

Operation:Anetworkedgethatconnectseachagentwithinourfollow-onpoolandourchosenambassadorsiscreated.Thisisactionedbyaddingtheambassadorstothetargetsalternetwork.

PorchTalk

Seeds:Agentsareseededwithinformationtohostporchtalks.Porchtalkersareagentswhoareadopters.Porchtalksdisseminateinformationtotheirimmediategeographicalnetwork.

FollowOn:Follow-onsaretheagentswithinthesolarchampionsgeographicalnetwork.

InitialTargeting:Atinitialization,fromapoolofalltheagentsinthemodelwhoareadopterswe-randomlyorusingHighK–selectasubsetofadopterstohostporchtalks.Thesearetheseeds.

Wethendetermineasubsetoftheseedsimmediategeographicalneighbors.Thispoolisrefinedusingaparticipationrate.Thesearethefollow-ons.

Operation:Theinformationeffectofseedagentsispassedontofollow-onagentsusingtherelativeagreementalgorithmor‘maximaleffect’.

CelebrityAdvocates

Seeds:Agentsthatareseededwithinformationarecelebrities.Celebritiesabletotranscendsocialandgeographicalnetworkswithinthemodelanddisseminateinformationtoanyagentwithinthemodel.Inthismodelonly20agentsareseeded.

InitialTargeting:ThecelebrityagentsweseedwithinformationdonotexistwithintheSECADmodel.Wegenerateattitudinallyactivatedcelebrityseedagentswithinthemodelwithsiabetween0.6and1(Unif[1,0.6,1])andU=1/sia.

Thepoolofpotentialfollow-onsforthisinterventionarealltheagentswithintheSECADmodel.



Table2:Describesthefourinterventionsmodelledinthisresearchandexplainstheiroperationwithinthemodel

SECADSetupEachinterventionissimulatedwithinSECADovera6-yearperiod.TogenerateadoptioninformationeachiterationoftheSECADmodelruns5batchesof24quarterindividualruns.

SeedsvsFollow-OnPhaseDiagramsMostourresultsarepresentedasphasediagramsthattrackadditionaladoptionsgeneratedbyeachinterventionasafunctionofseedquantityandfollowonrate.Phasediagramsallowvisualizationoftheeffectivenessofeachinterventionasafunctionofthenumberofseedsandtheseedsabilitytogeneratefollow-ons.Thisisausefulvisualtoolforattemptingtoviewchangesinadoptionbetweencausedbytheinterventionexperiments.Phasediagramsaregeneratedby1920separateparameter-blastiterationsofeachinterventiontoexploretheentireparameterspace.Parameter-blastsiterationsareconductedbyrandomlygeneratingtheseedquantity(1-500)andfollowonrate(0-1)priortoeachrun.ThesolarPVadoptionsplottedwithinthephasediagramresultsaretheadditionaladoptionsgeneratedbytheinterventioncomparedtotheempiricallyobservedadoptionsin2014.

Figure3Examplephasediagram.

FollowOn:Allagentsarepotentialfollow-ons.

Operation:Theinformationeffectofseedagentsispassedontofollow-onagentsusingtherelativeagreementalgorithmor‘maximaleffect’.



MeasuringtheValueofInformationandEconomicInterventionsWeusetheSECADvirtuallaboratorytotesttheefficacyofvarioussolarinformationinterventions.Asinformationalresourcesaredispersedwithinthemodeltheywillalteranagent’ssiaanddrivesolaradoptionwithinthemodel.ToassesstheefficiencyofinformationalexperimentswithinSECADitisnecessarytoestablishaframeworkformeasuringthevalueofinformationalinterventionswemodel.ThisallowsexperimentalinterventionswithintheSECADmodeltobecomparedtotheeffectivenessofreal-wordinformationalandeconomicinterventions.Tofacilitatethesecomparisons,weusethefollowingmethodtomeasurethecostofinformationalinterventionsinSECAD.Themethodweutilizeisacomparisonofthereturnoninvestment–measuredinadditionaladoptions-betweenourexperimentalinformationalinterventionandarandomlydispersedeconomicinterventionwithinthemodel.ReturnoninvestmentoradditionaladoptionsinSECADismeasuredbytotalincreasedsolaradoptionsovertheobservedempiricaladoptionsinAustinduring2008-2013.

𝐴𝐴 = 𝐴$ −𝐴& (1)

where:

AAisadditionaladoptionsAiistheamountofadoptionsforaninformationalintervention

Aeistheamountofobservedempiricaladoptions.TheeconomicinterventionwemodelinSECADsimplymeasurestheadditionaladoptionscreatedbyrandomlyselectingagentswithinthemodelandseedingthemwithsolarPV.WithinSECADthissimplyinvolveschangingtheseedagent’sadoptionstatusfromnon-adoptertoanadopteratinitialization.Tomodelthesameparameterspaceasourinformationinterventionswerandomlyseedbetween1and500agentsandrecordtheresultingadditionaladoptions.Tomeasuretheeffectivenessoftheeconomicinterventionweevaluatetheregression:

𝐴𝐴& = 𝛽& × 𝑆& + 𝑐& (2)where:

AAeisadditionaladoptionsfromtheeconomicintervention

beistheamountofadditionaladoptionsforonemoreeconomicseed

Seistheamountofeconomicseeds

ceisthemeanadditionaladoptionswith0economicseeds.

Thisresultofthisregressioncanthenbecomparedtothefollowingregressiononeachofthephasediagramsgeneratedbyourinformationinterventionexperiments:

𝐴𝐴$ = (𝛽$,. ×𝑆$) +(𝛽$,0 × 𝐹𝑂𝑅)+𝑐$ (3)



where:

AAiisadditionaladoptionsfromtheinformationalintervention

bi,sistheamountofadditionaladoptionsforonemoreinformationalseed

bi,fistheamountofadditionaladoptionsforincreasingfollow-onrate

Siistheamountofinformationalseeds

FORisthefollow-onrateceisthemeanadditionaladoptionswith0informationalseeds.

Theresultsofeachoftheseregressionscanbecomparedtoprovideanestimateofthedifferentialreturnoninvestmentprovidedbyaneconomicseedcomparedtoaninformationalseed.Weachievethisbycomparingtheregressioncoefficientsbeandbi,s,assumingbothregressionsarestatisticallysignificant.Ifweassumethecostofaneconomicseedissimplythecostofasolarsystemforthatseed,wetheratioofbi,s/beprovidesanestimateofthehowmanysolarsystemseachinformationalseedisworth.Thisresultgivesasimpleindicationofhowmucheachinformationseedisworth,andhowmuchareal-worldsolarinformationinterventionprogramshouldbewillingtopayeachseed.

ResultsInthissection,wepresenttheresultsoftheparameterblastsimulationsdescribedinthemethodssectionabove.Foreachintervention,resultsarepresentedonphasediagramsthatevaluateadditionaladoptionsgeneratedovertheseedandfollowonparameterspace.Foreachintervention,wepresenttheresultsforbothrandomandHighKseeding.Withinthephase-diagramswearelookingfordiscernabletrendsandgrowthinsolarPVadoptionresultingfromthemodeledinformationinterventions.SolarChampions

ThephasediagramsforbothHighKandrandom(Figure4)seedingofthesolarchampionsinterventionareshownbelow.Inbothsolarchampionmodels,weobservethattheinformationinterventionhasnotresultedinsignificantenoughadditionaladoptionstoovercometherandomstochasticityofadoptionsintheSECADmodel.Weseenodiscernableadoptiontrendacrosstheseedandfollowonparameterspace,andincreasesineitherparameterfailtogenerateadoptionpatterns.ThisresultisfurtherillustratedinFigure5whichpresentstheobservedempiricaladoptions(yellowcurve)inAustinoverthe2008-2014timespanagainsttheadoptionresultsgeneratedbythesolarchampionintervention(reddots,blackcurveismeanadoptions).Weseethattheadoptionsgeneratedbytheinterventionarespreadrelativelyevenlyaroundtheempiricaladoptionscurve,sometimesimprovinguponandsometimesunderperformingtheempiricaladoptions.Weseethatadditionaladoptionstracksempiricaladoptionsrelativelycloselyandourinterventionhasnotcausedasignificantchangeinsolaradoption.



Figure5Observedadoptionsofthesolarchampionsinterventionovertime(individualobservationsinredand

meaninblack)againstempiricallyobservedadoptionsbetween2008and2014.

PorchTalksThephasediagramsforbothHighKandrandom(Figure6)seedingoftheporchtalkinterventionareshownbelow.Bothphasediagramsgiveverydifferentresults,withrandomseedingsignificantlyoutperformingHighKseeding.ThisfindingprovidesaninterestinginsightintothenetworklocationsoftheHighKagentswithintheSECADmodel,andthepowerofthistargetingstrategy.ItappearsthatthefailureofHighKtargetinginthismodelisafunctionofthelocationofagentswithHighKcharacteristics.ItisapparentthatallHighKagentsexistwithinasmallnumberofneighborhoods.Thisissensiblewhenconsideringhowsocialnetworksaredefined(seeABMoverview),usinggeographical

Figure 4 Phase diagram of solar champion intervention: Random (on left) and HighK (on right)



neighborhoodsandanadditionalhomophilyconstraint.Therefore,smallanddenselypopulatedareasarelikelytocontainthemajorityofHighKagents.Asweaddnewseedswecontinuetore-engagethesamefollow-onnetworksthathavepreviouslybeentargetedandsolarinformationiscontinuouslyre-distributedwithinthesamefewnetworks.Thisstrategyfailstodriveanynoticeableadoptiontrendswithinthemodel,anddoesnotovercometherandomstochasticityofadoptionsintheSECADmodel.Alternatively,randomlyselectingagentsasseedsproducesanoticeableadoptiontrendwithinthephasediagram,andhasresultedinsignificantadditionaladoptions.Asthenumberofseedsandadoptionrateincreaseanobvioustrendofadditionaladoptionsemerges.Thisresultisobserveddespiterandomseedshavinglessfollow-ons(bydefinition)thenHighKseeds.Itappearstherelativesuccessofrandomseedingiscausedbythelargercumulativefollow-onnetworktheinterventioncanreachcomparedtoHighKseeding.Whileeachrandomseedmayhavelessindividualfollow-ons,itislikelythateachofthesefollow-ongroupsisunique.WhileontheotherhandHighKseedshavefollow-ongroups,itislikelythesegroupsarehighlysimilarandthecompleteinterventionfailstospreadsolarinformationandcausesolaradoption.

SolarAmbassadorThephasediagramsforbothHighKandrandom(Figure7)seedingofthesolarambassadorinterventionareshownbelow.Inbothphasediagrams,weobservethatthesolarambassadorinterventionhasresultedinsignificantadditionaladoptionsabovetheempiricallyobservedadoptionsin2014.Theadoptiontrendswithinthephasediagramsclearlyindicatethatthesolarambassadorprogramhassuccessfullydrivenadoptionwithin

Figure 6 Phase diagram of porch talk intervention: Random (on left) and HighK (on right)



themodel.Evenwithrelativelyfewseedsandlowfollow-onratesanobservableadditionaladoptiontrendemerges.TherandomandHighKmodelsforsolarambassadorbothgenerateverysimilarphasediagrams.Thisisexpectedasthesolarambassadorinterventiondoesnotrelyontheseedsexistingnetworkstospreadinformation,ratherinformationisspreadtofollow-onagentsbyconnectingthemtoaseedagentthattheywerenotpreviouslyconnectedto.Therefore,itisunlikelythattargetingseedagentsbasedontheirexistingnetworks(asisdoneinHighK)willgeneratesignificantlydifferentresultsthanrandomlyseedingagents.

Additionaladoptionsinthephasediagramincreasesignificantlyacrosstheverticalaxisofthephasediagram.Asthenumberoffollow-onsconnectedtoeachseedincreases,sodoesadoptionwithinthemodel.However,thephasediagramsappeartoshowaweakercorrelationbetweenthenumberofseedsandadditionaladoptionwithinthemodel.Thisresultisconfirmedbymultivariatelinearregressionoffollowonrateandnumberofseedsonadditionaladoption.Theregressionconfirmsthattheseedsparameterisnotrelatedtotheresponse.Thisresultisproblematicasitindicatesthatthenumberofseedsisunrelatedtotheresponse.Itislikelythatthisisanartefactofapoorlyspecifiedseedvariablewithinthemodel,sinceeachseedupdateswiththesamegroupoffollow-onswithinthismodelthemarginaleffectofadditionalseedsisrapidlyreducedafteronlyafewseedsareincludedinthemodel.

CelebrityAdvocatesThephasediagramsforbothHighKandrandom(Figure8)seedingofthecelebrityadvocateinterventionareshownbelow.Thecelebrityinterventiononlyexplorestheseedparameterbetween1and20.Inbothphasediagrams,weobservethattheintervention

Figure 7. Phase diagram of phone ambassador intervention: Random (on left) and HighK (on right)



hasresultedinsignificantadditionaladoptionsabovetheempiricallyobservedadoptionsin2014.Theclearadoptiontrendswithinthephasediagramsclearlyindicatethatthecelebrityadvocateshaveverysuccessfullydrivenadoptionwithinthemodel.Asthenumberofseedsandadoptionrateincreaseanobvioustrendofadditionaladoptionsemerges.Theeffectivenessofthecelebrityinterventionissomewhatexpected,ascelebritieshaveaccesstothelargestfollow-ongroupwithinthemodelanddonotrequireaconnectiontothefollow-ons.ThisresultisfurtherillustratedinFigure9,whichagaincomparestheobservedempiricaladoptions(yellowcurve)inAustinoverthe2008-2014timespanagainsttheadoptionresultsgeneratedbythecelebrityintervention(reddots,blackcurveismeanadoptions).Weseethatataveryearlytimetheadoptionsgeneratedbytheinterventionseparatesfromtheempiricaladoptionscurve.AninterestingobservationwithinthecelebrityphasediagramsistheextenttowhichrandomseedingoutperformedtargetedHighKseeding.HighKcangenerallybethoughtofasamore‘administrativelyexpensive’seedingtechniqueasitrequiresidentifyingnetworkcharacteristicsofeachagent.

Figure 8. Phase diagram of celebrity advocate intervention: Random (on left) and HighK (on right)



Figure9.Observedadoptionsofthecelebrityadvocatesinterventionovertime(individualobservationsinredand

meaninblack)againstempiricallyobservedadoptionsbetween2008and2014

DeterminingtheValueofInterventionsToevaluatetheeconomicsoftheinterventionsmodeledinthisworkweusethemethoddescribedpreviously.WefirstevaluatetheresultsofasimpleeconomicinterventionwherebywegiveeachseedagentsolarPVatinitialization(t=0).Thisinterventionhasaneasilyevaluatedeconomiccostofandvalueof:

𝐶𝑜𝑠𝑡($) = 𝑁 × 𝑃Value($)=𝑁 × 𝑃 ×𝛽&

(4)(5)

where:

Nisthenumberofseeds Pisthepriceofeachsolarsystem.

beistheamountofadditionaladoptionsforonemoreeconomicseed

Wethencomparetheperformanceofeachinformationinterventionagainsttheperformanceofaneconomicinterventiontodeterminethevalueofeachinformationalseedcomparedtothecostofaneconomicseed.



Figure10.Regressiononeconomicintervention

TheresultsoftheeconomicinterventionarepresentedinFigure10above.Weseefromsimplelinearregressionusingequation(2)ontheseresultsthateachadditionaleconomicseed(Se)isworthapproximately1.69(be)additionaladoptions.Wethenperformmulti-variatelinearregressionlinearusingequation(3)todeterminebi,sforeachoftheinformationalinterventions.TheseresultsaresummarizedinTable3below.

Program Targeting bi,s RelativeValue(bi,s/be)

SolarChampions Random 0.02 0.012

HighK -0.02 -0.012

PorchTalk Random 2.54** 1.5**

HighK -0.10** -0.06**

SolarAmbassador Random 0.03 0.018

HighK 0.07** 0.04**

Celebrity Random 168.1** 99**

HighK 78.7** 47**

**statisticallysignificant

Table3Assessingthevalueofinformationinterventions.

Theresultsofeachoftheseregressionsarecomparedintheright-handcolumnofTable3andprovideanestimateofthedifferentialreturnoninvestmentprovidedbyaneconomicseedcomparedtoaninformationalseed.Ifweassumethecostofaneconomicseedis

AA»(1.69xSe)+70



simplythecostofasolarsystemforthatseed,theratioofbi,s/beprovidesanestimateofthehowmanysolarsystems(oreconomicseedsSe)eachinformationalseedisworth.

DiscussionDesigningInterventionstoSucceedAkeygoalofthisresearchwastoidentifyfactorsthatcausesolarinformationinterventiontosucceed.Whilewehavenotattemptedtooptimizeinterventionsthemselves,ourresultsprovideusefulinsightsintothecharacteristicsthatdefinethesuccessofourmodeledinterventions.Interventionsthatrelyonexistingnetworkconnections(socialnetworkinSECAD)tospreadinformationarelesseffectiveatdrivingsolaradoptionthaninterventionsthatforceagentstocreatenewconnections.ThisismadeclearbyacomparisonoftheSolarChampionsprogramtotheotherinterventionswemodel.TheSolarChampionsinterventionreliesonagentsspreadinginformationaboutsolartotheagentswithintheirexistingsocialnetworks.Unliketheotherinterventionswemodel,seedsandfollowonagentsinthismodelarealreadyconnected,thustheeffectivenessofthisprogramessentiallyreliesonseedagentsknowingasmanyuniquefollow-onagentsaspossible.However,asourresultsdemonstrateevenwithupto500seedagentsthisinterventionisnotpowerfulenoughtogenerateobservableadditionaladoptions.Theseresultsindicatethatspreadinginformationusingtheexistingsocialnetworkofagentsisapoorstrategyforgeneratingsolaradoptions.Incontrasttothisresult,allinterventions(withtheexceptionofPorchTalks-HighKwhichisdiscussedlater)thatforgednewnetworkconnectionsbetweenseedandfollow-onagentsdemonstratedclearlyobservableincreasesinadoption.Theseinterventionscreateconnectionsbetweenseedagentsandfollow-onsoutsideofeachagentsexistingsocialnetwork.Informationaboutsolarisabletoescapebeyondtheseedsocialnetworkclustersthataretargetedinsolarchampionsandreachfollow-onagentsthatareconnectedwithinothernewsocialnetworks.Thisisafindingthatremainssensiblewhentranslatedtothe“real-world”,interventionsthatforcepeopletointerconnectandexchangeinformationarelikelytobemoresuccessful.AnotherkeyobservationwithinourmodelingistheextenttowhichrandomtargetingofseedagentsoutperformsHighKtargeting.Thiswouldappeartobecontradictoryfinding,asHighKisthoughtofasamoreexpensiveformoftargetingasitrequiresassessingthe“popularity”(K)ofeachagent.However,theresultinfacthighlightsthelimitationsofHighKtargeting.

ThelimitationsofHighKtargetingisbestevidencedintheresultsfromthePorchTalkprogram.LookingatFigure6weseethatwithinthePorchTalkprogramHighKtargetingsignificantlyunderperformscomparedtorandomtargeting.TheproblemwhentargetingseedsusingHighKisthatagentswithHighKhaveahighprobabilityofbeingconnectedtooneanother.InthecaseofthePorchTalkprogramitisclearthatamajorityoftheagentswithseededduetoHighKresidedwithinthesamenetwork.Therefore,werepeatedlyprovidedinformationtothesamegroupoffollow-onagentsbutwereunabletoreachany



agentsoutsidetheHighKneighborhood.Incontrastrandomseeding-whileinexact–managedbychancetospreadinformationtoasignificantlylargerfollow-onnetworkthanHighK’ssaturationofanindividualnetwork.

Theseobservationsinformthecentralfindingofthiswork,thatinformationprogramsthatareabletoreachthelargestuniquenetworkoffollow-onagentswillbethemostsuccessfulinpromotingdiffusingofsolarPV.Interventiondesignthatforcessolaradvocates(seeds)tointeractwithpeople(follow-ons)outsidetheirexistingsocialnetworksandwhomaynotsharesimilarcharacteristicsasthem(K)willleadtobetteroutcomes.

EvaluatingtheEconomicsofInterventionsFromTable3weseethatasexpectedcelebrityinterventionshavethehighestvalueofallseedsandareworthover50timesthecostofaneconomicseed.Therefore,asacoarseapproximationifthecosttohireacelebrityadvocatewaslessthan50solarunitsitwouldbeaneconomicaldecision.Thesameanalysiscanbeappliedtoeachoftheotherprogramstoprovideanestimateofathresholdvalueforeachinterventionseed.Finally,whileitmayseemobviousthatcelebritiesprovidethebestvalueofalltheinformationprograms,itisalsotruethatacelebritywouldbethemostexpensivepersontoemployasasolaradvocate.Whilethevalueofaporchtalksandsolarambassadorseedmayappearcomparativelysmall,theyarealsolikelytobeordersofmagnitudelessexpensivethanacelebrity.Therefore,itisimportanttonotethatifinterventionsgeneratestatisticallysignificantadditionaladoptionstheyareworthevaluatingagainstthecostofseeds.

ConclusionandPolicyImplicationsInthisresearchweidentifythatinformationinterventionsthatspreadbeyondexistingsocialnetworksaremoreeffectivethanthosethatrelysolelyonsocialnetworks.Putanotherway,wefindthatthecreationofnew“weakties”facilitatestheeffectivediffusionofinformation(Granovetter,1978).Wealsofindthatseedinghighlyconnectedagentsisinferiortorandomseeding.Thesefindingshaveseveralinterestingpolicyimplications.Basedontheseresultswerecommenddesigningsolarinformationinterventionstomaximizetheamountofinteractionsbetweenpeopleindifferentsocialgroups.Thisallowsinformationtotravelbeyondafinitenumberofexistingsocialgroupsandinsteadconnectsalargernumberofsocialgroups.Informationspreadswithintheselargerinterconnectedsocialgroupsovertimeviawordofmouthandpeereffects.Interventionsshouldthereforebedesignedsuchthateach“seed”orinfluencerisabletointeractwithpeoplebeyondtheirownsocialnetwork.Wealsofindthatrepeatedlyseedinghighlyconnectedagentsisanineffectivestrategy.Programsthatrepeatedlyidentifyseedsbasedonsimplecharacteristicssuchaspopularity,locationorwealthmayfindsimilarlimitations.Seedingbythesecharacteristicswillincreasethelikelihoodthateachnewseedisbelongstoasimilarsocialgroupasapreviousseed.Thereforeevenwithlargenumbersofseeds,theprogramreachesasubstantiallysmallernumberofsocialnetworks.Forsuccessfulinterventionsseedsshouldbeselectedthatareabletoreachthebroadestnumberofnewsocialgroups.



TheSECADmodelusedinthisresearchisvalidatedforAustin,Texas.Necessarymodificationsshouldbemadewhenapplyingthefindingspresentedinthispapertocitieswithdifferenteconomicandpolicycontexts.

FutureWorkThisresearchpresentsseveralopportunitiesforfurtherrefinementandstudy.Improvedmodellingofthecostofeachinformationinterventionwillfacilitatebettercomparisonsofthereturnoninvestmentofeachprogram.Onepotentialmethodwouldbetousethenumberoffollow-onsaseedagenthasatinitialization(t=0)asaproxyfortheircost.Forexampleitwouldbeexpectedthatacelebrityseedwith100,000follow-onsis10,000timesmoreexpensivethanaprogramvolunteerseedwith10follow-ons.Furthermore,increasinglygranularmodelingofthequalityofinformationhasthepotentialtoimprovethefindingofthiswork.Forexample,Rai,Reeves&Margolis(2015)findthattheinfluenceofneighborsandinstallerstobestrongmotivatorstowardPVadoption.Anabilitytomodelthe“qualityofinformation”ortrustworthinessofeachindividualseedwouldimprovethereliabilityofthiswork.Inthisresearchwetestonlytwoseedtargetingstrategies,randomandHighK.However,severalmorepowerfulnetworkcentralitytheoriesexist.Forexample,Kitsaketal.(2010),offeranoveltheoryfordeterminingthemost“influential”nodesinanetwork.FurtherworkcouldtestthesetheorieswithintheSECADmodeltoseewhethertheyareabletooutperformrandomandHighKseeding.Finally,allinterventionsmodeledinthisresearchwerepurelyinformational.Noconsiderationwasgiventomodelingeconomicinterventions–forexampleachangeinthePVrebate–incombinationwithinformationalinterventions.Furtherworkshouldexplorecombinationsofeconomicandinformationinterventionstounderstandiftheyofferbetterreturnoninvestment.



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