Prospects of modelling societal transitions: Position paper of an emerging community

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ContentslistsavailableatScienceDirect

Environmental

Innovation

and

Societal

Transitions

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / e i s t

Survey

Prospects

of

modelling

societal

transitions:

Position

paper

of

an

emerging

community

夽

Georg

Holtz

a,∗

_,

_Floortje

_Alkemade

b

_,

_Fjalar

_de

_Haan

c

_,

Jonathan

Köhler

d

_,

_Evelina

_Trutnevyte

e

_,

_Tobias

_Luthe

f,g

_,

Johannes

Halbe

h

_,

_George

_Papachristos

i

_,

_Emile

_Chappin

a,i

_,

Jan

Kwakkel

i

_,

_Sampsa

_Ruutu

j

a_Wuppertal_Institute_for_Climate,_Environment_and_Energy,_Germany

b_School_of_Innovation_Sciences,_Eindhoven_University_of_Technology,_The_Netherlands

c_Cooperative_Research_Centre_for_Water_Sensitive_Cities_and_School_of_Social_Sciences,_Faculty_of_Arts,

MonashUniversity,Australia

d_{Fraunhofer-Institut}_für_System-_und_{Innovationsforschung}_ISI,_Karlsruhe,_Germany e_University_College_London,_UCL_Energy_Institute,_London,_United_Kingdom

f_Institute_for_Tourism_and_Leisure,_University_of_Applied_Sciences_HTW_Chur,_Switzerland g_Centre_for_Key_{Qualiﬁcations,}_University_of_Freiburg,_Germany

h_Institute_of_{Environmental}_Systems_Research,_University_of_Osnabrück,_Germany

i_Delft_University_of_Technology,_Faculty_of_Technology,_Policy_and_Management,_The_Netherlands j_VTT_Technical_Research_Centre_of_Finland,_Finland

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received20December2014 Receivedinrevisedform14April2015 Accepted21May2015 Availableonlinexxx Keywords: Complexsystem Formalmodel Simulation Societaltransition Socio-technical

a

b

s

t

r

a

c

t

Societaltransitionsinvolvemultipleactors,changesininstitutions,

valuesandtechnologies,andinteractionsacrossmultiplesectors

andscales.Giventhiscomplexity,thispapertakesontheview

thatthesocietaltransitionsresearchﬁeldwouldbeneﬁtfromthe

furthermaturationandbroaderuptakeofmodellingapproaches.

Thispapershowshowmodellingcanenhancetheunderstanding

ofandsupportstakeholderstosteersocietaltransitions.Itdiscusses

thebeneﬁtsmodellingprovidesforstudyinglargesocietalsystems

andelaboratesondifferentwaysmodelscanbeusedfor

transi-tionsstudies.Twomodelapplicationsarepresentedinsomedetail

toillustratethebeneﬁts.Then,limitationsofmodellingsocietal

夽_We,_the_authors,_belong_to_a_group_of_modellers_who_aim_to_make_modelling_of_transitions_a_visible_and_fruitful

sub-ﬁeldofthesocietaltransitionsresearchﬁeld.WearerelatedtotheSustainabilityTransitionsResearchNetwork(STRN,

www.transitionsnetwork.org)andinviteallinterestedresearchersintheSTRNandbeyondtocontactandjoinus.

∗ Correspondingauthor.Tel.:+492022492313. E-mailaddress:georgho@wupperinst.org(G.Holtz).

http://dx.doi.org/10.1016/j.eist.2015.05.006

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transitionsarediscussed,whichleadstoanagendaforfuture

activi-ties:(1)bettercooperationinthedevelopmentofdynamicmodels,

(2)strongerinteractionwithothertransitionscholarsand

stake-holders,and(3)useofadditionalmodellingapproachesthatwe

thinkarerelevanttoandlargelyunexploredintransitionsstudies.

1. Introduction

Asocietaltransitionis“aradical,structuralchangeofasocietal(sub)systemthatistheresultofa coevolutionofeconomic,cultural,technological,ecological,andinstitutionaldevelopmentsat differ-entscalelevels”(RotmansandLoorbach,2009).Societal(sub)systemsasreferredtointhisdefinition coverkeyareasofhumanactivity,includingourtransport,energy,agrifood,housing,manufacturing, leisureandothersystems(STRN,2010).For studyingchangeofthesesystemssocietaltransitions researchadoptsabroaderperspectivethanotherapproachestosustainabledevelopment,and high-lightsthemulti-dimensionalinteractionsbetweenindustry,technology,markets,policy,cultureand civilsociety(STRN,2010).Societaltransitionsarehighlycomplexprocessesthatunfoldover time-spansofdecades,ratherthanyears,andinvolve“wicked”problemsforsocietiesthatrequireasystems approachtopolicy(RipandKemp,1998;Grinetal.,2010).Thefieldofsocietaltransitionsstudieshas developedwithtwomaininterrelatedagendas:(1)scientificprogress:tobetterunderstandhow struc-turalchangeoflarge-scalecomplexsocietalsystemscomesabout;and(2)impact:tomakeparticular societaltransitionshappenandnavigatedevelopmentstowardssustainability.

Theobjectiveofthispaperistoshowhowmodellingcancontributetotheagendaofsocietal transi-tionsresearch–bothforenhancingunderstandingandforincreasingimpact.Furthermore,wepropose anagendaforfutureactivitiesinouremerging(sub)communitytoincreasetheuptakeandeffectof modellingapproachesinthesocietaltransitionscommunityandbeyond.Westartfromthe observa-tionthattherealreadyhasbeenmodellingworkintheﬁeldofsocietaltransitions,asdemonstratedby aspecialissue(TimmermansanddeHaan,2008),variousconferencesessions,1_review_papers₍_Holtz,

2011;Safarzynskaetal.,2012;Zeppinietal.,2014;Halbeetal.,2014)andvariousPhDtheses(Holtz,

2010;deHaan,2010;Yücel,2010;Chappin,2011;Papachristos,2012).Despitealltheseactivities,

modelbasedstudiestodatehaveasmallerroleinthefieldthanwethinktheypotentiallycouldand shouldhave,andweareoftheopinionthatthesocietaltransitionsresearchfieldwouldbenefitfrom thefurthermaturationandbroaderuptakeofmodellingapproaches.Wedevelopourargumentas follows:Section2discussesfundamentalcharacteristicsofmodellingandtheassociatedbenefitsthat ariseforstudyinglargesocietalsystems.InSection3wethendiscussspecificchallengesformodel usethatarisefromthescopeandperspectiveofsocietaltransitionsresearch,andoutlinetypicalways howmodelshavebeenandcanbeusedinthesocietaltransitionsfield,andhowtheymakeuseof thepreviouslydiscussedfundamentalcharacteristicsofmodelling.InSection4wedemonstratethe benefitsbytwoexamples,whichwepresentatgreaterlength.InSection5limitationsfortheuseof modelsinsocietaltransitionsresearcharediscussed.InSection6weidentifypromisingavenuesfor usingmodelstostudysocietaltransitionsandtoincreasetheimpactoftransitionsstudiesthrough theiruse.Inthefinalsectionwedrawtheconclusionsfromourdiscussions.

1_There_have_been_a_week-long_{international}_workshop_on_{“Computational}_and_Mathematical_Approaches_to_Societal

Tran-sitions”attheLorentzCenteratLeidenUniversityin2007andsessionsatseveralconferences:ESSA2008inBrescia,Italy; ESSA2009inSurrey,Guildford,UK;WCCS2010inKassel,Germany;KSIConference2010inAmsterdam,TheNetherlands;EGU GeneralAssembly2013inVienna,Austria;ISTConference2013inZürich,Switzerland;ISTconference2014inUtrecht,The Netherlands.

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2. Characteristicsofmodelsandbeneﬁtsfortransitionresearch

A“model”,asweusethetermhere,isasimplified,stylisedandformalisedrepresentationof(apart of)reality.Modelsrangefrombeingspecificforaparticularreal-worldcase,suchastheDutch elec-tricitysystem,tobeingmoregeneral,suchasgeneralisedmodelsofconsumer-producerinteractions. Modellinginvolvesoutliningasystemboundaryandselectingaspectsofthestudiedsystemthatare consideredthemostimportantwithrespecttoaparticularresearchobjective.Then,aformal repre-sentationoftheseaspectsandtheirinterrelationsisdeveloped.Modelscanbeformulatedinmany ways,forexampleconceptually,mathematically,graphically,orascomputerprogrammecode,and theycanbeusedforavarietyofpurposes,mostimportantlytomakeforecasts,toimprovethe under-standingaboutmechanismsthatproduceacertainobservedphenomenon,toexploreconsequences ofhypotheses,andtofacilitatecommunication(Epstein,2008).Inthefollowingsectionsweidentify certainfundamentalcharacteristicsthatmodelsofagreatvarietyofdesignsshare,anddiscussthe benefitsfortransitionsresearchthatcanbederivedfromthesecharacteristics.

2.1. Modelsareexplicit,clear,andsystematic

Alltheorisingandconceptualisationrequiresmakingassumptions.Thevirtueofmodelsisthat theseassumptionstypicallyhavetobeveryexplicit(Epstein,2008).Modelshavetobewrittendown usingsomeformalmethodinordertoworkwiththem.Intheprocessofwritingdown,allthe assump-tionshavetobeexplicated,andthevariablesandtherelationsbetweenthemhavetobedeﬁned. Makingitconcretelikethis–developingdeﬁnitionsandforcingchoicesbetweenconcepts–leads todiscourseandcanrevealdifferencesinunderstandingbetweeninvolvedresearchersand stake-holdersthatmayremainunnoticedinlessexplicitapproaches.Theclarityofmodelshelpstobridge disciplinaryboundaries,astheformaldescriptionleaveslittleroomforambiguity2_and_can_provides

acommonlanguagetodescribeanddiscusstheanalysedsystem.Forthisreason,modelsarealso con-sideredusefultoolsinparticipatoryprocesses(cf.,Vennix,1996;vandenBelt,2004).Furthermore, modelsaresystematicinthesensethattheyfacilitatecapturingadiversityof(previouslyisolated) piecesofknowledgeinasingle,logicallycoherentrepresentation.Duringtheprocessofknowledge integration,easytooverlookinconsistenciesbetweenpartialpiecesofknowledgeandknowledge gapscanberevealedbecauseoftheneedforlogicalconsistency.Modelswithappropriate visualisa-tionanddataprocessingtechniquescanfurthermorehelptomakethestructureofcomplexsystems moreaccessible,e.g.,throughvisualrepresentationofinteractionnetworks,systematic representa-tionofinputs,keysystemelementsandoutputs,identiﬁcationoffeedback-loopsetc.Thiscanassist researchersandstakeholdersingettinganoverviewofthestudiedsystem.Insum,theprocessof modellingitself–irrespectiveofthemodellingoutcomes–facilitateslearningabouttheanalysed systemsandcanmakeourpresentunderstandingoftransitionsmoreexplicit,lessambiguous,and moreinterlinked.

2.2. Modelsallowinferencesofdynamicsincomplexsystems

Althoughsomeprocessesinvolvedinsocietaltransitions,suchasincreasingreturnstoscaleand diffusionofinnovations, arereasonablywellunderstoodinisolation,consideringseveralofthem simultaneouslyisadauntingchallenge.Thetransitiondynamicsemergingfromtheinterplayofthese processesisdifﬁculttooverseeandcomprehend,letaloneforesee.Thisisrootedinabasiclimitation ofthehumanmindtoimagineandcomprehenddynamicsincomplexsystems.Ithasbeenfoundthat thementalmodels3_which_humans_(consciously_or_{unconsciously)}_use_to_deal_with_complex_systems

aretypicallyeventbased,haveanopenloopviewofcausality,ignorefeedback,failtoaccountfortime

2_However,_the_{interpretation}_of_the_variables,_i.e._the_{understanding}_of_the_relation_between_the_formal_description_and

therealworld,mayinvolvemoreambiguity.Resolvingthesepotentialmultipleunderstandingsisanimportantaspectof participatorymodelling,moreonthislater.

3_The_term_mental_model_here_refers_to_someone’s_thought_process_about_how_something_works_in_the_real_world,_i.e._her/his

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delays,andareinsensitivetonon-linearity(Sterman,1994).Hence,essentialelementsofdynamics incomplexsystems,namelyfeedback,timedelaysandnon-linearity,cannotbeappropriatelydealt with.Consequently,mentalsimulationsofcomplexsystemsarehighlydefective,ashasbeen demon-stratedempiricallyinvariousstudies(Dörner,1980;Sterman,1989a,b;Brehmer,1992;Kleinmutz,

1993;Diehl andSterman,1995;Atkinsetal.,2002;Sastry,1997).Dynamicmodels4 _that_are_cast

mathematicallyorareimplementedassoftwaremodelsareabletocalculateorderivethedynamics thatarisefrommultipleinteracting(non-linear)processesandcanhencehelptheresearchertoinfer systembehaviourfromassumptionswithgreaterconﬁdencethanispossiblewithmentalsimulations

(Sterman,2002).

Inparticular,dynamicmodelsareusefultounderstandandexploreemergentphenomena. Emer-gentphenomenaresultfromtheinteractionsbetweenvariousparts,andanyexplanationoftheoverall systembehaviourdependsuponboththepropertiesofitspartsandthecharacteristicwaytheparts arerelated(Elder-Vass,2010).Emergentphenomenatherefore“...aresomehowconstitutedby,and generatedfrom,underlyingprocesses...”yetatthesametime“somehowautonomousfrom underly-ingprocesses”(Bedau,1997).Understandingemergentphenomenaishighlyrelevantfortransitions studies.Togivesomeexamples:theinertiaofaregime(partly)arisesfrominterdependenciesof elements,nichesarise,growandmerge,anddifferenttransitionpathwaysunfolddependingon par-ticularrelationsbetweenlandscape,regimeandnichelevels(GeelsandSchot,2007).Dynamicmodels allowtorepresentthepartsandtherelationsandtolettheirinteractions“generate”theemergent phenomenonfromtheunderlyingprocesses(EpsteinandAxtell,1996).Sincementalsimulationis pronetofailurewhenitcomestocomplexsystemsanddynamicmodelsaretheonlyother possibil-itytoinferdynamicsincomplexsystems,wearguethatunderstandingemergentphenomenawill stronglybeneﬁtfromtheuseofdynamicmodels.Bedau(1997)evengivesaphilosophicalargument thatemergentphenomenacanbeunderstoodonlythroughusingdynamicmodels.

2.3. Modelsfacilitatesystematicexperiments

Ithasbeenarguedthatmodel-basedscienceisverymuchlikeexperimentalscience(Bankes,2009). Inexperimentalscience,theresearchercreatesanexperimentinwhichvariousfactorsarecarefully controlled.Modelscanbeusedinthesameway,i.e.itispossibletofullycontrolthevariousfactors affectingthebehaviourofamodel.Consequently,onecanusemodelstotryoutthingsandanalyse theirconsequences,includingexperimentsthatwouldbeimpossible,impracticalorunethicalwitha realsystem,orinsystemconﬁgurationsthatdonot(yet)exist.Forexample,whenstudyingenergy systems,modelscanbeusedtoexperimentwithalternativepolicyoptionsforsteeringthesystem towardsmoresustainablefunctioning(ChappinandDijkema,2010).Suchexperimentationinthereal worldwouldbecostlyandcouldalsohavenegativesocialeffectsandconsequentlysuchacomparison betweenalternativepolicyoptionsisnexttoimpossibletoachieve(Kwakkeletal.,2012).Modelscan thusbeusedforsystematicandcontrolledwhat-ifanalyses,similartoexperimentalscience.Itis relativelycheaptoexecuteseriesofexperimentsinordertoexploretheeffectsofdifferentpolicies, toassesstheconsequencesofunresolveddeepuncertainties,ortoreplicateanexperimentalarge numberoftimesinordertostudytheconsequencesoftheinherentstochasticityofthemodelled system.

deﬁnitionisgivenby(DoyleandFord,1999,p.414)whodeﬁneamentalmodelas‘arelativelyenduringandaccessible,but limited,internalconceptualrepresentationofanexternalsystem(historical,existingorprojected)whosestructureisanalogous totheperceivedstructureofthatsystem’.

4_We_use_the_term_“dynamic_model”_to_refer_to_a_sub-class_of_models_that_relate_elements_and_their_interactions_and_are_able

toinferdynamicsthatarisefromthisstructure,e.g.computersimulationmodelsormodelscastinananalyticalornumerical mathematicaldescription.Dynamicmodelsofcomplexsystemsdonothavetobelargeandcomplicatedperse(i.e.,include manyvariablesandrelations),butinthetransitionscontextthereisacertaintendencytowardsthis,astransitionshappenin largecomplexsystems.

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3. Modelusesintransitionsstudies

Modelsdifferlargelyintermsoftheirformulation,levelofabstraction,epistemological founda-tions,applicationcontext,datarequirements,andpurpose.Thesedimensionshavetobecarefully balancedineachmodelstudytodesignausefulmodelthatisfitforpurpose.Thespecificbenefits andlimitationsofamodeldependontheparticulardesignanditsintendeduse.Itisthereforenot possibleinthescopeofthispapertoprovideacomprehensivediscussionofmodelusesand associ-atedbenefitsandlimitationsinthetransitionsfield.Instead,wediscusssomespecificchallengesthat societaltransitionsmodellingmustcopewith.Wethenpresentsomerathergenericclassesofhow modelshavebeenandcanbeusedbytransitionscholarsanddiscusshowthesemodelusesdraw onthecharacteristicspresentedinSection2andhowtheydealwiththespecificchallenges.Forthe discussionofmodeluses,weadopttheclassificationdevelopedbyHalbeetal.(2014)anddistinguish threeclasses:(1)understandingtransitions;(2)providingcase-specificpolicyadvice;(3)facilitating stakeholderprocesses.

3.1. Speciﬁcchallenges

Asoutlinedintheintroduction,theperspectiveoftransitionstudiesisespeciallybroad,covering multiplesectors.It alsoincludesinteraliainstitutions,markets,varioustypesofactorsand actor networks,technologiesandinfrastructures.Giventhisbroadperspective,modelsoftransitionshaveto eitherincludemanyelementsandrelationsmakingthemlargeandcomplicated,adoptacomparably highlevelofabstraction,orpurposefullylimittheirscopeofanalysis.Themodellingalsorequires theintegrationofknowledgefromdifferentdisciplinessuchassociology,(social-)psychologyand economics,includingtheirvarioussub-ﬁelds,aswellasthenaturalsciencesandengineering.Unlike inlessformalisedapproachesthisintegrationneedstobeexplicit,whichoftenrequiresthemaking ofchoicesanddevelopingcreativesolutionswherethingsdonotreadilycombine.5

Furthermore,transitionresearchadoptsahighlydynamicperspectiveandconceivestechnologies, infrastructure,institutions,actors,behaviourandvaluesasallbeingvariableduringthetransition process(STRN,2010), andthis includesdeepuncertainties (Lempertetal.,2003; Kwakkelet al.,

2010;Walkeretal.,2013),suchasthepotentialemergenceofagame-changingtechnologyorcrises.

Thischaracteristicoftransitionsrequiresattentionwhenmakingassumptionsabouttheontology ofdynamicmodels,aselementsofthis ontologymightchangeduringthesimulatedtimeperiod

(Anderssonetal.,2014),forexampleifcompletelynewactorgroupssuchas“prosumers”ofsolar

energyappearduringatransitionoftheenergysystem.Inprinciple,modellingcancopewitha chang-ingontologythroughchoosingthelevelofabstractionsuchthattherequiredchangeintheontology becomespartofthedynamicsofthemodel.Thiswillbeeasiertorealiseforhistoriccaseswherethe changeinontologycanbeestablishedafterthefact,whilethisismoredifﬁcultforprospectiveuse.

Aconcomitantissuetoontologyisthedevelopmentofmetricsandindicatorsfortransition pro-cesses.Theneedforthatisevidentinstudiesthattransfertheoreticalwork(e.g.,GeelsandSchot, 2007)tomodels(e.g.,Bergmanetal.,2008)whereaconceptualframeworkconducivetomodelling hastobedevelopedbeforebuildingthemodel(Haxeltineetal.,2008).

Finally,notallsocialprocessesinvolvedintransitionscaneasilybecapturedinmodels.Mayntz

(2004)distinguishesbetweenprocessesthatemergefromtheuncoordinatedactionsofmanyactors

(e.g.,increasingreturnstoscale,diffusionofinnovations,percolationeffectsinnetworks,etc.),and processesthatresultfromcoordinatedactionsordiscussionsoffewactors(e.g.,strategicactions, polit-icalprocesses).Thelatterareespeciallysensitivetoagencyofasingleorafewactors,andmoreover areoftenshapedbyveryspecificsetsofinstitutions,whichinfluencetheprocessanditsoutcomes. Thereforethesetypesofprocessesarecontingentonpotentiallyveryspecificcircumstancesofthe actorsinvolvedandtheinstitutionalsetting.Ontheonehand,omittingtheseissuesinmodelscan leadtomodelsthatessentially‘missthepoint’becausetheirdynamicsdonotincorporateagency

5_The_need_to_be_explicit_can_be_a_beneﬁt_if_it_stimulates_discussions_and_makes_unspoken_assumptions_visible_(see_Section

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whereitwouldbeappropriate.Ontheotherhand,incorporatingtheprocessesmayleadtovery spe-cificmodelsthatrequirehardtoobtaindataonspecificrationalesandmotivationsofsingleactors andwhicharedifficulttogeneralise.

3.2. Modelsforunderstanding

Allkindsofmodelscanenhanceunderstandingoftransitionsthroughmakingthestructureof com-plexsystemsexplicitandbydoingsosupportingtheidentiﬁcationofthemostrelevantelementsand processes.6_Dynamic_models_can_furthermore_enhance_{understanding}_through_linking_overall

dynam-icsandemergentphenomenatotheunderlyingelementsandprocesses.Theycanassisttheevaluation ofhistoricaltransitionnarrativesbytestingwhethertheproposedsetofassumptionscanactually gen-eratethedescribeddynamics(Bergmanetal.,2008;Holtz&Pahl-Wostl,2012;Yücel,2010),andbe usedtotestandreﬁneproposedtheoriesaboutthewaytransitionprocessesunfoldandhowcertain theorisedmechanismsproducecertaineffectssuchas,e.g.,lock-inorvarioustransitionpathways (see,e.g.,Eisingetal.,2014;deHaan,2008;Papachristos,2011;SafarzynskaandvandenBergh,2010;

Schilperoordetal.,2008;vanderVoorenandAlkemade,2012).Inallcases,beneﬁtsofmodeluse

resultfromtheabilityofdynamicmodelstosystematicallyintegratetheknowledgeaboutvariables andprocessesoftheanalysedsystem,andtolettheirinteractionsgeneratethephenomenonof inter-est.Theassumptionsareexplicittotheanalystandtheclarityofcausalfactorsenablesunderstanding oftheoperatingmechanisms.Furthermore,themodelcanbevariedinsystematicexperimentsto reﬂectavarietyofhypothesesaboutsimulatedcircumstances.Thisallowsidentifyingdifferentsets ofassumptionsthatdo,ordonot,qualifyaspotentialexplanationsforthephenomenonofinterest.

Someofthecitedmodelexercisestherebyadoptahigh-levelofabstractiontocoverthescope ofmultiplesectorsandtoaccountforchangesintheontology(Bergmanetal.,2008;Yücel,2010;

Schilperoordetal.,2008;deHaan,2008;Papachristos,2011),whileothersfocusonspeciﬁcsubsystems

tokeepthesizeofthemodelmanageable(Eisingetal.,2014;HoltzandPahl-Wostl,2012;Safarzynska

andvandenBergh,2010;vanderVoorenandAlkemade,2012).Thefocusonhistoricalcasesand

theoreticalpatternsallowsaccountingfordeepuncertainties,strategicactionandpoliticalprocesses aspre-deﬁnedboundaryconditionsforandpartsofthemodel.

3.3. Modelsforcase-speciﬁcpolicyadvice

Modelsforcase-specificpolicyadviceaimtoprovidepracticalpolicyrecommendationsonhowto influenceatransitioninaparticularcase.Apreconditionforthistypeofmodeluseisthatthe mod-ellersandstakeholdersinvolvedhavesufficientconfidenceinthetheory,hypothesesandassumptions behindthemodel.Thedynamicmodelthenmaybeusedtoproduceforecasts,projectionsoffuture statesoftheanalysedsystemgivenaninitialstateandacertainpolicyscenariothatcaptures(the resultsof)strategicactionandpoliticalprocesses.Asoutlinedabove,transitioncasesinvolvemany deepuncertainties,andconsequentlythedangersofrelyingonmodelforecastsasaccuratepredictions aresevere.Therefore,state-of-the-artmodelapplicationsacknowledgeuncertaintyandincorporate itinthemodelstudytoassessitsrelevanceandtoanalyseitsconsequences.Inrecentdecades, scho-larshavebeenadvocatinganapproachcalledExploratoryModelling(Bankes,1993),whichinvolves acknowledginguncertaintiesthroughanalysingmodelbehaviouroverrangesofparametervalues,and variationofcertainassumptionssuchasactorrationality.Thisapproachdoesnotresultinamodelthat producesaprediction,butratheronethatproducesaportfolioofpossiblefutures(see,e.g.,Chappin

andDijkema,2010;deHaanetal.,2013;Kwakkeletal.,2013).ExploratoryModellingisattheheartof

decisionsupportapproacheslikerobustdecisionmaking(e.g.,LempertandCollins,2007)and model-basedadaptivepolicymaking(Hamaratetal.,2013,2014).Theseapproachesaimatsupportingthe designofaplanthatperformsrobustlyinthefaceofthemanyuncertainties,ratherthanthe identi-ﬁcationofanoptimalplanthatonlyperformsoptimallyunderonenarrowlydeﬁnedfuturescenario. ExploratoryModellingissuggestedtobeakeywaytoincorporatemodellingintostrategicplanning

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(Malekpouretal.,2013).Throughdoingsystematicmodellingexperimentsandmappingthespaceof possiblefutures,dynamicmodelscanhencebeusedtotestpoliciesorapproachesforgovernanceand indicatehowtheyaffectthesetoflikelyfuturepathsforaparticularsystem.Furthermore,through theclarityofcausalfactorsandtheabilitytoscrutinisethemechanismsthatproduceresults,models provideinsightintotheconditionsunderwhichagiventypeoffuturewilloccur.Insum,dynamic modelscansupporttheidentiﬁcationofrobusttransitionpolicies,ofthresholdswhosecrossingleads tounwantedfuturedevelopmentswithhighprobability,andfacilitatediscussionsaboutpossibleand necessaryinterventionstosteerasysteminthedesireddirection.

3.4. Modelstofacilitatestakeholderprocesses

Modelstofacilitatestakeholderprocesseshavesofarreceivedlimitedattentionoftransition mod-ellers(Halbeetal.,2014),butweseebigpotentialforthismodeluse.Allkindsofmodelscanbe developedinaparticipatorywayandtherearevariouswaystoincludestakeholdersinmodelling processes(seeRengeretal.,2008;VoinovandBousquet,2010;Hare,2011),thereforethereisacertain overlapofthiscategorywiththeothermodeluses.Infront-andback-endparticipatorymodelling pro-cesses,stakeholdersareconsultedatearlyandatlatestagesofthemodelbuildingprocesstoprovide inputondefinitionsandvalidity,withoutextensiveparticipationinmodelconstruction(Hare,2011). Suchprocessesarecommonfordecision-supportandcommunicationofscientificfindings,and exist-ingmodelscanbeapplied.WegiveanexamplethatfallsintothisclassinSection4.1.Inco-construction participatorymodelling,theveryprocessofmodellingitselfbecomesaparticipatoryactivity(Hare, 2011).Byjointlybuildingamodel,stakeholdersexplicitlydiscussassumptionsandlearnabouteach other’sperspectives.Thedevelopedmodelsmaythenbeusedinasecondsteptoderiveforecastsand discusspolicies.Alsodifferentkindsofgamescanservemultiplepurposesinstakeholderprocesses. Forexampletheyallowthetestingofpoliciesandstrategiesandtoexperiencetheroleofanother actorinaconflictsituation.Wediscussco-constructionparticipatorymodellingaswellasgaming approachesaspromisingfutureavenuesinSection5.

4. Examples

Inthissectionwepresenttwoexamplesofmodellingstudiestodemonstratethatthebenefitsof modellingdiscussedabovecanberealisedinpracticalterms.Wefirstpresentastudythatapplies well-establishedmodelsdevelopedoutsidethe(core)transitionscommunityfortheexplorationof transitionpathwaystowardsasustainableelectricitysystem.Modelsthatrangefromstatisticaldata techniquestomoreadvancedmodelsfromthedisciplinesofeconomics,econometrics,engineering, environmentalandothernaturalsciences,ormodelsthatcross-cutthroughseveraldisciplines,such asenergy–economy–environmentmodels,arereadilyavailableorcanbeeasilyadaptedtobeusedin thetransitionsfield.Whilesuchmodelsarewellestablishedandwidelyusedforresearchandpolicy makingingeneral,thetransitionscommunityhasbarelyusedthemtodate,despitethearguments giveninSection2suggestingitmightbebeneficialtodoso.

Thesecondexamplecomplementstheﬁrstoneanddescribesadynamicmodelthathasbeen specif-icallydevelopedtouse(formalised)transitionconceptsforexploringtransitiondynamicstowards sustainablemobility.

4.1. Usingexistingmodelstoscrutinisenarratives

Anexamplethatdemonstratesthebeneﬁtsofusingalreadyexistingmodelsfortransitionsresearch comesfromtheRealisingTransitionPathwaysproject(RealisingTransitionPathways,2013).This projectexplorestheUKelectricitysystemtransitionin2010–2050.Inthisproject,transition scho-larsinstakeholderworkshopsandthroughdeskresearchdevelopedthreegovernancenarrativesfor thistransition:market-led,government-ledandcivicsociety-ledgovernancenarratives(Foxon,2013;

Foxonetal.,2010).Thesenarrativesconsistedof4–5pagesoftextaboutgovernancepatterns,choices

ofthekeyactorsandtheco-evolutionoftheseaspectsandelectricitydemandandsupply(Transition

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so-calledtransitionpathwaystoenablecommunicationwiththekeystakeholdersandfurtherdetailed assessmentofthenarrativesandpathways(Foxon,2013).Yet,whenthesepathwaysbecameusedfor wideraudiencesandpurposes,theywerecontinuouslychallengedandcouldnotalwayswithstand criticalfeedback.Forexample,noeconomicconsiderationsweretakenintoaccountwhendeveloping thepathways.Thisraisedconcernsoverhowrealisticthepathwayswere.

To addresstheconcerns and criticisms,a multi-model analysisof thenarrative andpathway ofthegovernment-ledtransitionwasinitiated(Trutnevyteetal.,2014).Thenarrativewaslinked witheightalreadyexistingmodels.Thesemodelsincluded(1)anenergydemandmodel,(2–4)three supply–demandmodels,(5)anenergy–economicmodel,(6)anenergy–behaviourmodel,(7)an eco-nomicappraisalmodel,and(8)anenergyandenvironmentalappraisalmodel.Theseeightmodels wereusedwithharmonisedassumptionstotailorthemtothegovernment-lednarrativeandwere thenappliedtoassessandfleshoutthenarrativeanditsquantificationinasystematicway.Asa resultofthisprocess,severallimitationsinthenarrativeanditsunderlyingassumptionswere iden-tified(Trutnevyteetal.,2014).For example,thenarrative wishfullyoverestimatedtheelectricity demandreductionlevelsandthiswasinconsistentwiththeresultsoftheenergy–behaviourmodel andenergy–economicmodel.Theuptakeofcostlymarinerenewables,envisionedinthenarratives, wasalsoquestionedbytheenergy–economicmodelandtheeconomicappraisalmodel.The narra-tivealsodepictedanirreplaceableroleofcarboncaptureandstorage(CCS)formeetinglong-term stringentgreenhousegasemissionstargets.Incontrasttothatassumedirreplaceability,allmodels, excepttheenergydemandmodelthatdidnotanalyseelectricitysupplyoptions,showedthat transi-tionpathwayswithoutCCScanalsomeettheemissiontargets.Infact,theenergyandenvironmental appraisalshowedthatifenergyrequirementsforextraction,processing/refining,transport,and fab-rication,aswellasmethaneleakagethatoccursincoalminingactivitiesarealsoconsidered,CCSis likelytodeliveronly70%reductioningreenhousegasemissionsinsteadofthecommonlyassumed

90%(Hammondetal.,2013).

Thedivergencebetweennarrativesandmodelsobservedinthiscaseisnotsurprisingbecause nar-ratives,envisionedbystakeholdersandevenexperts,oftentendtobeoverlyoptimisticandoverlook complexinterdependenciesinthesystems(Baron,1998;Trutnevyteetal.,2011,2012a).Themodels helpedtoidentifytheresultingquestionableassumptionsinthenarratives.Furthermore,themodels alsohelpedtoidentifyissuesthatwerenotconsideredinthenarrativesatall.Thenarrativesbarely touchedontheimportantchallengesofsupply–demandbalancing.Whentransitionpathways,as envisionedinthenarrativesweremodelled,theresultsofsevenmodelsshowedthatbalancingsupply anddemandwillbechallengingduetothesimultaneousdeploymentoflarge-scaleinflexiblepower plants,suchasnuclearpower,andsubstantialdeploymentofintermittentrenewableenergysources. Toensurethatthesupply–demandchallengewouldbemetintheenvisionedpathways,deployment offlexibleback-upcapacityandinterconnectorswithEuropewouldbeneeded.Themodellingresults drewattentiontotheseissuesandthusincreasedtheinferentialpowerofthestudyoverall.Such findingswillbeusedintheup-comingrevisionofthenarratives(Trutnevyteetal.,2014).

Thisexampleillustratesthatmodelscanbeusefultosupportconceptualandnarrativebased tran-sitionapproaches,increasetheirrobustness,enhanceconﬁdenceinthem,andimprovetheirpolicy relevance.Inparticular,theusageofexistingmodelsfromoutsidethecoretransitioncommunitycan helptoconsiderfactorsthattypicallyremainoutofscope(HansenandCoenen,2013;Trutnevyteetal.,

2012b).

4.2. Exploretransitiondynamicswithadynamicmodel

Dynamicmodelswhichintegratemultiplenon-linearprocessescanbedevelopedspeciﬁcallyto analysetransitionsorrelevantsub-processesthereofasphenomenathatemergefromaselection ofunderlyingelementsandprocesses. Todemonstratethepotentialofsuchdynamicmodelsfor analysingpossiblefutureswereportonamodelforassessingtransitionstosustainablemobility,more preciselypersonal(inland)transportationbehaviour(Köhleretal.,2009).Themodelimplementsan

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(extended)multi-levelperspectivewithtwoclassesofagents.7_There_are_eight_{“constellation}_agents”,

whichhaveaninternalstructureandrepresentsubsystemswithinsociety:(1)theregimeagent rep-resentstheinternalcombustionengine(ICE).Therearethreecar-basedniches:(2)ICE/electrichybrid cars,(3)biofuelcarsand(4)hydrogenfuelcellvehicles.Othernichesfollowingchangesinownership patternsare:(5)increaseduseofpublictransport,and(6)producttoserviceshift(fromcarownership tocarsharing).Nicheswithdecreasedmobilitydemandare:(7)adoptionofslowmodes(walkingand cycling)and8)urbaninformationandcommunicationtools(ICT)forhomeworking.A(much)larger number(1000inthereportedresults)ofsimpleagentsrepresentconsumers.

Allagentsarelocatedina“practicespace,”amulti-dimensionalcharacterisationofthefunctionality ofasocietalsubsystemandthepreferencesofconsumers.Thechosenpracticedimensionsare:CO2

emissionsofvehicles(gCO2/km),costoftransport(D/year),ICTuse,structureofthebuiltenvironment

(mixeduseofzonesaffectingmobilitydecisions)andprivateandpublicdemandsplit(measured inpersonkm/year).Eachtypeofconstellationagents(regime,niche,niche–regime)hasadifferent behaviouralalgorithmforitsmovementinthepracticespacebasedonpolicydrivenpartydynamics

(Laver,2005).Constellationagentsmayinteract,forexampletheregimemightabsorbanicheand

nichesmaymergeintoastrongerniche.

Consumerssupporttheconstellationagenttheyconsidermostattractiveandprovideresources tothis constellationagent.Inturn,theconstellationagentusestheseresourcesformovementin thepracticespaceorincreaseofstrength.Theattractivenessofanicheortheregimeforconsumers dependsonitsstrengthandthematchbetweenitspractices,expressedbyitslocationinthepractice space,andtheconsumers’preferences.Theconsumeragentsinthepracticesspacechangetheir posi-tiondependingonlandscapesignals,whichareexogenousinputstothemodel.Landscapesignals include:(1)climatechangethatshiftspreferencestowardslowerCO2emissions,(2)changein

con-sumerspriceacceptance,(3)ICTusageamongconsumers,(4)publictransportinvestments,and(5) planningofbuiltenvironmentasweakbutsteadilydecreasingtransportrequirementovertime.The modeldefinesatransitionasasignificantshiftinthesystem’sdominantpractices.Thefirstwayin whichatransitioncanhappenisthroughregimechange,whichoccurswhenanincumbentregime losessupportandstrengthandanotherconstellationagentwithdifferentpracticestakesitsplace.The secondwayinwhichatransitioncanhappeniswhentheregimesignificantlychangesitspractices throughadaptationand/orabsorptionofniches,movingtoasignificantlydifferentlocationinthe practicespace(cf.GeelsandSchot,2007).

Themodelrepresentsaverycomplexsystemwithfeedbackbetweentheconsumersontheone handandthenichesandregimeontheotherhand.Also, therearemutual interactionsbetween theregimeandtheemergentniches,andbetweenthenichesthemselves.Inaddition,thesystemis inﬂuencedbyasetofexogenouslandscapefactors.Themodellinkstheseprocessesinasystematic wayandprovidesanintegratedandlogicallycoherentperspectiveonthelargesystemanditsmany interdependencies.Simulationexperimentscanbeusedtoinferthedynamicconsequences,including particularpossibleemergentproperties:thedisappearanceoftheregimeandtheemergenceofanew regime.Themodelcanbeusedtoinvestigatetheconditionsofaregimeshift,makingtheseconditions explicitanddiscussablesincethemodelformulatesthevariouselementsandprocessesclearlyand assumptionshavebeenmadeexplicit.Throughthis,themodelcanbeusedtotesthypothesisabout necessaryandsufﬁcientconditionsfortransitions,andtoexplorefuturedevelopmentsgivencertain initialconditionsandassumptions.

ThemodelwasparameterisedusingUKdata(WhitmarshandNykvist,2008)andcalibratedto provideplausiblestrengthsoftheregimeandnichesin2000aswellas2010.Simulationresultsfor thetimeperioduntil2050showthathydrogenfuelcellvehiclescometodominate,butonlyinthe verylongrun(after2030),whilebiofuelsandICE-electrichybridsarethemainalternativestothe regimeinthenext10–30years,because(a)theyarealreadydevelopedand(b)theyﬁtbetterinto currentinfrastructures.Themodelshowsthattransitionsthroughtheadoptionofnewtechnologies aremostlikely,whereaslifestylechangetransitionsrequiresustainedpressurefromtheenvironment onsocietyandbehaviouralchangefromconsumers.

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Althoughtheresultsfromthemodelarepreliminary,therearethreepolicyimplications:(1)a large-scalechangeinconsumerattitudestogetherwithstrongandsustainedpolicysupportarerequiredfor atransitiontosustainablemobility;(2)thebestalternativeintheshortandmediumtermmaynotbe thebestoptioninthelongrun;andﬁnally(3)directingradicalinstitutionalandbehaviouralchange ismoredifﬁcultthanachievingtechnologicalchange.

5. Limitationsofmodeluseintransitionsresearch

We have discussed beneﬁtsof models and advocatedand illustrated theiruse in transitions research.However,asallmethods,modellingalsohaslimitations.Thespeciﬁclimitationsofamodel dependonarangeofmodeldimensions:modelpurpose,methodapplied,levelofabstraction, epis-temologicalfoundations, applicationcontext, and data requirementsand availability (Boero and

Squazzoni,2005;BrugnachandPahl-Wostl,2008;Brugnachetal.,2008;JanssenandOstrom,2006).

Thefollowingidentiﬁessometypicallimitationsoftransitionsmodels.Theselimitationsaresimilar tothosediscussedformodelsinotherﬁelds(e.g.,Cressieetal.,2009;Modarres,2006;Aughenbaugh

andParedis,2004),butsometimesgobeyondthelimitationsofmodellingingeneralastransitions

arecomplex,multi-facetedprocessesinvolvingsocialdynamicsinbigsystemsevolvingoverlarge timescales(seeSection3.1).

5.1. Conceptualisationandimplementationissues

Modellingtransitionsincludescreatingexplicitlinksbetweenpiecesofknowledgefromdifferent ﬁelds,usingsomeformallanguagefordoingso.Thisincludescombiningconceptualelementsthat weredevelopedwithdifferentbackgroundassumptionsandworld-views,andtheirintegrationoften requirescreativesolutions.Transitiontheoriesthatprovideanalreadyintegratedperspective,suchas themulti-levelperspective,usuallyhavetheformofheuristicsthatdonotreadilytranslateintothe formaldescriptionsneededformodels,butrequireadditionalassumptionstomakethemoperational formodelling.Theseissuesmayleadtomodelsthathaveaweaktheoreticalandconceptualfoundation

(Holtz,2011).

Furthermore,modellinginvolvesconceptualchoicesthathavetobemade.Amodelemploysa certainconceptualframetoexplainaspecificphenomenon,andthattypicallymeansotherexplanatory avenuesarenotexplored–thereisalwaysmorethatcouldbeincludedormodelpartsthatcouldbe designeddifferently.Whereasthewholepointofmodellingisexactlytofocusonspecificprocessesand abstractawayfromothers,therelevanceofco-evolutionacrossthedifferentsectors(markets,politics, culture,etc.)makesitespeciallydifficulttoselecttheprocessesthatneedtobeincludedintransition models,andtoidentifythosewhichmaybeneglected.Thesystemsanalysedbeinglargecreatesa tendencyfortransitionsmodelstobealsolarge,i.e.toincludemanyvariablesandparameters,what makesvalidationmoredifficult(seebelow).Asinglemodelthereforecanhardlyachievethegoalsof completenessanddetailednessatthesame(cf.Bollingeretal.,2014).

Finally,manytypesofmodels,especiallylargeandcomplicatedones,necessarilyincludesmall, adhocassumptionstomakethemodeloperational.Theseassumptionsaretypicallyconsiderednot toinﬂuencethemodellingresultsandthereforeoftenleftunmentionedinpublicationsandreceive limitedattentionduringtestingthemodel.But,theymightinsomecasesinﬂuenceresultsinsome unnoticedwayandleadtowrongconclusionsregardingthecausesfortheobservedeffects(Galán etal.,2009).Theinclusionofunmentioned smallassumptionsalsoseemstogoagainsttheclaim thatmodellingmakesassumptionsexplicit.However,ontheprovisothatthemodelismadefully available,8_all_assumptions_can_at_least_in_principle_be_checked_and_tested.

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5.2. Validationissues

Theconceptualisationissuessketchedabovedirectlyleadtoissueswithvalidation,understood astestingwhetherthemodelcapturesrealitysufﬁcientlywell(Windrumetal.,2007;Ormerodand

Rosewell,2009).Theconceptualdiversityincludedinthemodelandtheuncertaintiesassociatedwith

formalisationandintegrationmayyieldalargenumberoffreeparameters9_which_can_lead_to:₍₁₎_over

determinationofthemodel.Amodelwithenoughparameterscanreproducealmostanyempirically observedbehaviourwithanappropriatechoiceofparametervalues.Thisdiminishesthevalidityofthe modelandcanbedetrimentaltothetrustofstakeholdersinthemodel;(2)ahighdependencyondata to“fit”themodelbehaviour.Thismakesthemodelhighlyspecifictoacertaincasefromwhichthedata istaken,withlimitedpossibilitiestodrawgeneralinsightsfromit;and(3)ifnotfixedagainstdata, themodelmayhavewiderangesof,inprinciple,equallyvalidparametervalues,potentiallyyielding manyregimesofqualitativelydifferentmodelbehaviours.Thiscandiminishexplanatorypowerand reducetrustinasimilarwaytopoint(1).

Theavailabilityofdatacanbeanothersevereproblemforvalidation,evenmoresobecausesome ofthesedataarequalitativewhichmeansthattheyneedtobemappedortranslatedinaquantitative formatforcomparisonwith,orusein,themodel.Furthermore,forprospectivemodeluses,thereis anissueofunpredictabilitythatcannotberesolvedevenwithhugeamountsofdata.Validationofa modelagainsthistoricdatamayincreaseconﬁdenceinthemodelbutdoesnotnecessarilysaymuch aboutthevalidityofforecastsofthefuture.Thisissimplybecauseonecannotexpectthatthe(historic) circumstancesunderwhichthemodelproducedaccurateresultswillbequitethesameinthefuture (seeSection3.1).Infacthistoricaltransitionsandfuturetransitionstosustainabilityposeconsiderably differentdemandsontransitionsmodelling(Papachristos,2014).

5.3. Agencyandcontingency

AsoutlinedinSection3.1,transitionsareinﬂuencedbystrategicactionsofcoreactorsand politi-calprocesses,whicharehardtocaptureinprospectivemodeluses.Theycanbecapturedas(policy) scenariosunderwhichdiversefuturesunfolddifferently,butthecreativityofrealactorswhen endoge-nouslyrespondingtochangingcircumstancescannotbefullyberepresentedbypredeﬁnedpolicies. 5.4. Issuesrelatedtoexpectations,resultsandcommunication

Models,duetotheirsystematicnature,includealotofknowledgeandmanydifferentassumptions, allofwhichare(tovariousdegrees)relevantforthemodelresults.Amodelcanthereforenoteasily bereducedtosomethingsimpler,withoutneglectingatleastpartofthestory.But,fullyexplaining a(somewhatlargeandcomplex)modelandhowitgeneratescertainemergenteffectsoftenwould requiremorespacethanisavailableinpolicybriefsorevenresearcharticles,andtrulyunderstanding amodelrequiresdevotingaconsiderableamountoftimetoit(evenforothermodellers).Limited engagementwithandunderstandingofthemodelmayreducethetrustofstakeholdersinthemodel, especiallyiftheresultsdonotmatchtheirintuitiveexpectations.Onthecontrary,thefactthatmodels oftenproducenumbersorgraphscanconveyafalsesenseofprecisionandresultsmaybeinterpreted too“literal”orasunshakabletruths.Inordertodealwiththeseissues,modellersshouldmakesureto conveythecomplexityofthemodelandtheuncertaintyassociatedwithitsresults,especiallyifthey areusedasinputfordecisionsupport(Stirling,2010).

6. Avenuestopursue

Despitethehighpotentialwehavediscussedanddemonstratedbyexamples,theuptakeof tran-sitionsmodellingstudiesinthewider transitionscommunity andbeyondandtheircontribution 9_Free_parameters_are_those_which_are_not_{(sufﬁciently}_well)_speciﬁed_through_theory_or_empirical_data._Large_numbers_of

parameterscanslipintomodelsthroughotherroutesaswellobviously.ManythankstoProfessorAnaDeleticforpointingout theriskofoverdeterminationofmodelswhichcanbeeasilyoverlooked.

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toimpactoftransitions studieshasbeencomparablysmall.Thissectionthereforediscusses sev-eralavenuesalongwhichtransitionsmodellingcandevelopandincreaseboth,itscontributionto understandingtransitionsanditsimpact.

6.1. Strongercooperationinthedevelopmentofdynamicmodels

Wehavediscusseddynamicmodelsastoolstofostertheorybuildingandasmeanstomake pro-jectionsoffuturedevelopments.Theexistingsetofdynamicmodelsinour(sub)communityfordoing soishighlydiverseintermsofscope,levelofabstraction,conceptualapproachandmethodapplied. Thisdiversitycanbeseenasaresultofdifferentattemptstoaddressthespecificchallengesoutlined inSection3.1,andalsoattributedtothejuvenilenessofthefield.Duetotheconceptualandvalidation issuesdiscussedintheprevioussection,thereisoftenscopetoincreasetherobustnessof conclu-sionsderivedfromthesemodels,especiallyiftheyarelargeandcomplicated.Inordertopromotethe furthermaturationofdynamicmodelsoftransitionsweintendtoestablishastrongercooperation intheirdevelopmentsothatitisdoneinacumulativeway,andlearningfromexistingexercisesis transferred.SeveralmethodsforthishavebeenidentifiedbyHalbeetal.(2014).Amongtheseare:(1) thecomparisonofalternativemodelsthatdealwithasimilarproblemsituation.Thishelpstodevelop robustresultsandtoidentifycriticalassumptions.Acorollarywouldbetodevelop(more)modelsof thesameorsimilartransitioncasesinordertofacilitatecomparison.Aspecificactivitycouldbeto addressanopenpolicyissuerelatingtotransitionstotestandshowcasetheusefulnessofavariety ofmodels;(2)thedevelopmentofexistingframeworkssuchasthemulti-levelperspectiveintomore preciseversionsthatareconducivetomodellingexercisesandreducetheambiguityinvolvedinthe necessaryspecificationforusageinmodels(cf.deHaanandRotmans,2011);(3)thedevelopmentof asharedunderstandingandtoolboxofelementsandprocessesoperatingonlowerlevelsof abstrac-tion(e.g.,increasingreturnstoscale,diffusionofinnovations)toguidemodeldesignprocessesandto makemodelscomparable(cf.Ostrom,2007;Holtz,2011,2012).Theidentificationofasetofimportant lowerlevelmechanismsandtheirrelationtohigherlevelstructuresandprocesseswouldalsobea contributiontotheorydevelopmentinthetransitionfield;(4)todesignanduseprotocolsandtools fordocumentation,uncertaintyhandlingandqualityassurance.Thisservestoensurehighqualityof modelsandthefollowingofbest-practices.Transitionmodellerscanbuildonexistingtools,protocols, platformsandframeworksthathavebeendevelopedinotherfields(cf.Halbeetal.,2014).

Suchanintensiﬁedcooperationinthedevelopmentofdynamicmodelscanaddresslimitations relatedtoconceptualizationandeventuallyleadtothedevelopmentofafewcoretransitionmodels,10

whichwouldfacilitateaccumulationofknowledgeandexperienceandimprovethevalidityof

mod-els(Frenken,2006).Asteptowardssuchabettercooperationistheidentiﬁcationofoneormore

clearnichesfordynamictransitionmodelsinrelationtothebroadercontextofexistingmodelling streams,andtoidentifyasetofcharacteristicsa“dynamictransitionsmodel”shouldhavetobeable tocontributetocumulativeinsightsinthisniche.

6.2. Interactionwithothertransitionscholarsandstakeholders

Modelscanincreasetheimpactoftransitionsstudiesthroughsharpeningdiscussions,enhancing mutualunderstanding,andreducinguncertaintiesaboutpotentialfuturedevelopments–ormaking uncertaintiesandtheirconsequencesexplicitwheretheycannotbereduced.AlthoughSection4.1

providesanexampleofhowimpactcanbeachieved,thepotentialofaclosercollaborationbetween modellers,othertransitionscholars,andespeciallystakeholdersfrompracticesuchaspolicy mak-ersiscurrentlymostlyuntapped.Wethereforeintend todiscusstheroleof modelsfor reﬂexive

10_To_illustrate_the_idea_of_core_models:_Frenken₍₂₀₀₆₎_identiﬁes_three_core_models_of_{technological}_innovation:_ﬁtness

landscapemodels,complexnetworkmodelsandpercolationmodels.Thesecanberecombined,adaptedandextendedfor speciﬁccasesandresearchquestions,butprovideawidelysharedreferencethatcapturescertainimportantcharacteristics oftheanalysedsystem.Transitionsarebroaderanddifferentfromtechnologicalinnovation,thereforedifferentcoremodels shouldbedeveloped.

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governance11_and_policy_making_in_general_more_deeply_with_transition_scholars_who_are_active_in

theseﬁelds.Moreover,transdisciplinaryresearchinvolvingpractitionersdirectlyaffectedbythe tran-sitionprocessesandintegratingtheirproblemperspectiveaswellasquantitativeandqualitative knowledgeisapromisingavenuetoincreasethesocietalrelevanceofresearch(e.g.,Jahnetal.,2012;

Langetal.,2012;Mobjörk,2010).

However,duetothelimitationsoutlinedinSection5,thecomplexnumericalsimulationmodels whichhaveuptonowmostlybeendevelopedtostudythedynamicsoftransitionsoftenarenotmature enoughtobereadilyappliedtopracticalquestionsanddecisionmaking.Othermodellingapproaches existwhicharemoreparsimoniousregardingtheoryanddataneeds,andwhichmaybemoreuseful ifthedevelopmentanduseofcomplexnumericalsimulationmodelsisnotadvisable.Anexampleis theusageofapprovedexistingmodelsfromoutsidethecoretransitioncommunityaspresentedin Section4.1.Thereareotherapproacheswhichweconsiderpromisingtomakeuseofinfutureprojects thatintendtoachieveimpactthroughinter-andtransdisciplinaryresearch.Weintroducetheminthe followingsection.

6.3. Exploringandapplyingotherpromisingmodellingapproaches 6.3.1. Participatorymodelling

Asmentionedin Section2,modellingforces onetobeveryexplicitaboutone’sassumptions. Amongst these assumptions are theproblem framing and world viewthemselves. Participatory modelling12_can_assist_in_making_the_fundamental_and_often_unspoken_assumptions_of_stakeholders

visibleanddiscussablethroughinvolvingtheminamodellingexercise.Throughjointlydeveloping aformalrepresentationofthetargetsystemassumptionsheldbythevariousparticipantsbecome explicitandcanbemoreeasilyshared.Thedefinitionofvariablesinagroupdiscussionrevealsif stakeholdersusedifferentwordsforthesameconcept,refertodifferentconceptswiththesame words,oruseconceptsthatoverlapbutdonotmatchexactly,andthediscussionofrelationships betweenvariablesrevealsdifferentviewsandbackgroundknowledge.Discussingassumptionscan helpstakeholdergroupstoreachconsensusoratleastidentificationofunderlyingcausesof disagree-mentandthussupportscommunicationandlearningbetweenmodellers,decisionmakersandother stakeholders(cf.,Liuetal.,2008;Serrat-Capdevilaetal.,2011).Suchexercisescanfurthermore sup-porttheintegratedanalysisofissuesacrossscalesanddisciplinaryboundariesandthedevelopment ofasharedlanguagethatsupportscommunication(Sendzimiretal.,2006;Ruthetal.,2011). Partic-ipatorymodelling,apartfromservingthecreationofsharedunderstanding,isalsoheldtoincrease legitimacyandacceptanceoftheresultingmodelanditsoutcomes(Jonesetal.,2009).Wearguethat participatorymodellinghasmuchtooffertoreflexivegovernanceapproaches.Forexample,itfits verywellwithinthe“strategicactivitycluster”oftransitionmanagement,whichincludes participa-toryproblemstructuringtofindacommonlanguagebetweenactorsandasharedconceptualization ofthesystemathand(Loorbach,2010).Auvinenetal.(2014)provideaframeworkandcasestudyin whichparticipatorymodellingisintegratedintoawiderparticipatoryprocessthatincludesforesight, impactassessment,andsocietalembedding.Thecasestudyillustratestheabilityofsuchaprocess tosupporthands-ondecisionmakingandpolicyplanningfortransitionsinpassengertransportin Finland.

6.3.2. Gamingapproaches

A“game”herereferstoasettinginwhichoneorseveralactorsinteract(s)witha(simulated) environment(includingotherplayers)accordingtospeciﬁcrules.Sincegamesinthissenseareformal representationsofaparticularsystemofinterestweconsiderthemtobeaparticularkindofmodels.

11_We_use_the_term_reﬂexive_governance_to_refer_to_various_governance_approaches_that_aim_at_inducing_and_navigating

complexprocessesofsocio-technicalchangebymeansofdeliberation,probingandlearning(Voßetal.,2009).Important examplesinthetransitionﬁeldaretransitionmanagementandstrategicnichemanagement.

12_We_focus_on_{“co-construction}_{participatory}_modeling”_in_which_the_very_process_of_modelling_itself_becomes_a_{participatory}

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Therearedifferentkindsofgamesthatweconsiderusefulfortransitionsstudiesthataimatmaking impactthroughtheinvolvementofstakeholdersandthegeneralpublic.

Roleplayinggamesarebehaviouralsimulationsthatallowstakeholdergroupstoexploreactor dynamicsandtheiroutcomesontheeconomy,societyorenvironment(Barreteau,2003).Roleplaying gamesprovideamodelofactorpreferencesandrelationshipsthatcanbeincludedinboardorcard games,orinroledescriptionsthatstakeholderscanadoptinacreativeway(cf.,Pahl-WostlandHare, 2004).Byplayingthesegames,stakeholderscanconstructivelyinteractwitheachotherandexplore andunderstandthemechanismsthatleadtospeciﬁcproblemsituations.Roleplayinggamescanalso beanopportunitytoexperiencetheroleofanotheractorinaconﬂictsituation(forinstance,afarmer couldplaytheroleofawatermanager),andthroughthisincreasemutualunderstanding.

Seriousgames(MichaelandChen,2005)canservemultiplepurposes,suchaseducationalpurpose

(GosenandWashbush,2004),orsupportofcommunicationaboutacomplextopic(Kellyetal.,2007).

Chappin(2011)developedaseriousgamebaseduponatransitionsimulationmodelonCO2policies

andelectricitymarkets.Thegamewassuccessfullytestedbystudentsandyoungprofessionalsand resultedinadeepenedunderstandingofparticipantsintermsofthefunctioningofelectricityand CO2marketsaswellasrelateddecision-makingprocesses.Suchgamescanbewidelydistributedor

offeredonline(e.g.,Poplin,2012)sothatahighnumberofactorscangainexperienceinaparticular problemareaandlearnaboutpotentialsolutions.

Companionmodellingintegratesroleplayinggamesandagentbasedmodels(e.g.,Barreteauetal., 2003)forconsciousness-raising(e.g.,Mathevetetal.,2007),forimprovinglocalandexperts’ knowl-edge(e.g.,Campoetal.,2010),aswellasinmediation(e.g.,Gurungetal.,2006)andnegotiation (e.g.,Barreteau,2003).Theroleplayinggamecanrevealdecision-rulesorotherbehaviouralelements appliedbystakeholderswhicharelaterimplementedintheagent-basedmodel.Theeffectsofthese behaviourscanbetestedthroughtheagent-basedmodelwhichcanrevealimpacts.Theseresultscan bediscussedwithandreﬂecteduponbystakeholders.

6.3.3. Structuralmodelling

Structural modellingis amethodthat usesqualitative structural(geometric, topological, etc.) aspectsofthesystembeingmodelledtoderiveconclusions,withoutsimulatingthedynamicsofthe system.Itisrootedinengineeringandpurelytechnologicalcontexts(Alexander,1964;Hararyetal.,

1965;Warﬁeld,1976;Lendaris,1980)butisnowadaysalsousedfortheanalysisofecological(e.g.,

Berlowetal.,2009)andsocio-ecologicalsystems(LutheandWyss,inrevision;Lutheetal.,2012).

Structuralmodellingcanbuilduponparticipatory,qualitative-conceptualmodelling(suchascausal loopdiagrams)andextendsuchapproachesbyrepresentingthesystemasanorderednetworkwith elementssuchaspeople,carsortreesbeingthenodesandtheinteractionsbetweenthembeingthe links,andbyanalysingitsnetworkstructure.Thepotentialofstructuralmodellingtoproduceinsights arisesfromthefactthattopologiesofvarioustypesofcomplexsystemsshareuniversalcharacteristics suchasscale-freeness,small-worldproperties,communitystructure,anddegreecorrelationswhich caninﬂuencethedynamicsoftherespectivesystem(CohenandHavlin,2010;WattsandStrogatz,

1998;BarabásiandAlbert,1999;GirvanandNewman,2002).Examplesforstructuralelementsthat

influencethedynamicsofacomplexsystemarehighlycentralhubswithleverage,controllinga sys-temanditspropertiesbytheirmanyconnections(Liuetal.,2012),and‘asymmetrichubs’withfew incomingbutmanyoutgoinglinkswhicharecomparablyeasytocontrolbuthaveconsiderableimpact. ThemostrecentadvanceinthatfieldhasbeenmadebyBarzelandBarabási(2013)whoproposea theoryontheuniversalinterplaybetweennetworktopology(structure)andnetworkdynamicsand findthat“acomplexsystem’sresponsetoperturbationsisdrivenbyasmallnumberofuniversal char-acteristics.”(p.7).Thissuggeststhatmeasuringcertainnetworkmetricscanprovidecrucialinsights inthesystem’sdynamicsandfacilitatestheidentificationofinterventionpoints.

We propose that structural modelling has potential for transitions studies in various ways. Regardingtheorybuilding,itcanforexamplebeusefultomaketheconceptsofregimeandnichemore tangiblethroughpreciselyandsystematicallymappingthemasareasofdenseinteraction,andto ana-lysethelinkagesthatbondthem.Similarlythekindofinteractionsbetweenregimeandnichescanbe analysedmoreprecisely.Furthermore,importantactorswhobridgeandcontrolexistingsubgroups canbeidentiﬁed,andthoseactorscanthenbespeciﬁcallyaddressed.Structuralmodellinghasaswell

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valueforcommunicatingcomplextopicsandaspectstostakeholdersandespeciallypractitionersby graphicallystructuringinterdependenciesinsocietalsystems(Lutheetal.,2012).

7. Conclusions

Modelsprovidesomeparticularadvantages forstudyingsocietaltransitions:(1) theyprovide explicit,clearandsystematicsystemrepresentationsthatinducelearningandfacilitate communi-cationaboutthetargetsystem,(2)theyallowmakinginferencesaboutdynamicsincomplexsystems andgeneratingemergentphenomenafromunderlyingelementsandprocesses,and(3)theyfacilitate systematicexperiments.Wehavearguedthatduetothesecharacteristicstransitionsmodellingcan contributetotheorybuildingandsupporttransitionsstudiestoachievemoreimpact.

Theorybuildingisrelevantforthescientificmaturationofthefield,andinthelongtermalso beneficialformoretargetedpolicydevelopment.Transitiontheorymustrelatecertaincircumstances toresultingtransitiondynamics,and beabletoexplainwhyandhowthesedynamicsresult.We haveshownthatdynamicmodelsareusefultostudysuchrelationsincomplexsystemsandtomake thedynamicstraceableandunderstandable. Furthermore,modelsfacilitateexperimentsinwhich varioushypothesescanbetestedandconfirmedorrejectedascandidatesforexplanatorytheory. However,societaltransitionsposeseverechallengestomodelbuildinganddevelopmentand mat-urationoftheorywillrequireintensecollaborationbetweenmodellersandempiricalresearchers,a bettercooperationinthedevelopmentofdynamicmodels,usageofadvancedmodellingtechniques andsupportivemethodssuchasprotocols–andaconsiderableamountoftime.

Fromtheperspectiveofpressing(environmental)issuesthetimeforactionisnow,andsoundand broadlyagreedtheoryisnotyetalwaysavailabletosupportthisaction.Hence,ascomplementsto dynamicmodelsoftransitions,lesstheoryanddatadependentapproaches,whicharereadilyavailable tobeintegratedintransitionsstudiesshouldbeusedtosupportpolicydevelopmentandstakeholder processes.Wehaveidentiﬁedaspromisingcandidatestheusageofexistentmodelsfromvarious disciplines,participatorymodelling,gamingapproachesandstructuralmodelling.Weinvitetransition scholarstoengageintodiscussionswithmodellers,whoarekeentoadaptexistinganddevelopnew approachestoﬁttheneedsoftransitionsstudies.

Acknowledgements

We thankGönenc Yücel,Jochen Markard,Koen Frenken andthree reviewersfor veryhelpful commentsonpreviousversionsofthisarticle.

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