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

(1)

Delft University of Technology

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

Holtz, G; Alkemade, F; de Haan, F; Kohler, J; Trutnevyte, E; Luthe, T; Halbe, J; Papachristos, G; Chappin,

EJL; Kwakkel, JH

DOI

10.1016/j.eist.2015.05.006

Publication date

2016

Document Version

Final published version

Published in

Environmental Innovation and Societal Transitions

Citation (APA)

Holtz, G., Alkemade, F., de Haan, F., Kohler, J., Trutnevyte, E., Luthe, T., Halbe, J., Papachristos, G.,

Chappin, EJL., Kwakkel, JH., & Ruutu, S. (2016). Prospects of modelling societal transitions: Position paper

of an emerging community. Environmental Innovation and Societal Transitions, 17, 41-58.

https://doi.org/10.1016/j.eist.2015.05.006

Important note

To cite this publication, please use the final published version (if applicable).

Please check the document version above.

Copyright

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy

Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim.

This work is downloaded from Delft University of Technology.

(2)

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

Availableonline4June2015 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

(3)

transitionsarediscussed,whichleadstoanagendaforfuture activi-ties:(1)bettercooperationinthedevelopmentofdynamicmodels, (2)strongerinteractionwithothertransitionscholarsand stake-holders,and(3)useofadditionalmodellingapproachesthatwe thinkarerelevanttoandlargelyunexploredintransitionsstudies. ©2015TheAuthors.PublishedbyElsevierB.V.Thisisanopen accessarticleundertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

(4)

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

(5)

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.

(6)

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

(7)

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

(8)

(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 Pathways,2012).In asemi-structured process,thesenarrativeswereinitiallyquantiﬁedintothe

(9)

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

(10)

(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.

(11)

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.

(12)

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.

(13)

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.

(14)

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}

(15)

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

(16)

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.

References

Alexander,C.,1964.NotesontheSynthesisofForm.Harvard,Cambridge,MA.

Andersson, C., Törnberg, A., Törnberg, P., 2014. Societal systems: complex or worse? Futures 63, 145–157, http://dx.doi.org/10.1016/j.futures.2014.07.003.

Aughenbaugh,J.M.,Paredis,C.J.J.,2004.Theroleandlimitationsofmodelingandsimulationinsystemsdesign.In:ASME 2004InternationalMechanicalEngineeringCongressandExposition.ComputersandInformationinEngineering.PaperNo. IMECE2004-59813,pp.13–22,http://dx.doi.org/10.1115/IMECE2004-59813,10pages.

Auvinen, H., Ruutu, S., Tuominen, A., Ahlqvist, T., Oksanen, J., 2014. Process supporting strategic decision-making in systemic transitions. Technol. Forecast. Soc. Change, http://dx.doi.org/10.1016/j.techfore.2014.07.011 (in press) http://linkinghub.elsevier.com/retrieve/pii/S0040162514002364

Atkins,P.W.B.,Wood,R.E.,Rutgers,P.J.,2002.Theeffectsoffeedbackformantondynamicdecisionmaking.Org.Behav.Hum. Decis.Processes88(2),587–604.

Bankes,S.,1993.Exploratorymodelingforpolicyanalysis.Oper.Res.41(3),435–449.

Bankes,S.,2009.Modelsaslabequipment:sciencefromcomputationalexperiments.Comput.Math.Org.Theory15(1),8–10. Barabási, A.L., Albert, R., 1999. Emergence of scaling in random networks. Science 286, 509, http://dx.doi.org/

10.1126/science.286.5439.509.

Baron,J.,1998.JudgmentMisguided:IntuitionandErrorinPublicDecisionMaking.OxfordUniversityPress,NewYork. Barreteau,O.,2003.Thejointuseofrole-playinggamesandmodelsregardingnegotiationprocesses:characterizationof

associations.J.Artif.Soc.Soc.Simul.6(2).

Barreteau,O.,Antona,M.,D’Aquino,P.,Aubert,S.,Boissau,S.,Bousquet,F.,Daré,W.,2003.Ourcompanionmodellingapproach. J.Artif.Soc.Soc.Simul.6(1).

Barzel,B.,Barabási,A.L.,2013.Universalityinnetworkdynamics.Nat.Phys.,http://dx.doi.org/10.1038/NPHYS2741. Bedau,M.A.,1997.Weakemergence.Nous31,375–399.

(17)

Bergman,N.,Haxeltine,A.,Whitmarsh,L.,Köhler,J.,Schilperoord,M.,Rotmans,J.,2008.Modellingsocio-technicaltransition patternsandpathways.J.Artif.Soc.Soc.Simul.11(3).

Berlow,E.L.,Dunne,J.A.,Martinez,N.D.,Stark,P.B.,Williams,R.J.,Brose,U.,2009.Simplepredictionofinteractionstrengthsin complexfoodwebs.Proc.Natl.Acad.Sci.U.S.A.106,187–191.

Boero,R.,Squazzoni,F.,2005.Doesempiricalembeddednessmatter?Methodologicalissuesonagent-basedmodelsforanalytical socialscience.J.Artif.Soc.Soc.Simul.8(4),Retrievedfrom:http://jasss.soc.surrey.ac.uk/8/4/6.html

Bollinger,L.A.,Bogmans,C.W.J.,Chappin,E.J.L.,Dijkema,G.P.J.,Huibregtse,J.N.,Maas,N.,Schenk,T.,Snelder,M.,vanThienen,P., deWit,S.,Wols,B.,Tavasszy,L.A.,2014.Climateadaptationofinterconnectedinfrastructures:aframeworkforsupporting governance.Reg.Environ.Change14(3),919–931.

Brehmer,B.,1992.Dynamicdecisionmaking:humancontrolofcomplexsystems.ActaPsychol.81,211–241.

Brugnach,M.,Pahl-Wostl,C.,2008.Abroadenedviewontheroleformodelsinnaturalresourcemanagement:implicationsfor modeldevelopment.In:Pahl-Wostl,C.,Kabat,P.,Möltgen,J.(Eds.),AdaptiveandIntegratedWaterManagement–Coping withComplexityandUncertainty.Springer.

Brugnach,M., Pahl-Wostl,C., Lindenschmidt,K.E., Janssen,J.A.E.,Filatova, T., Mouton,A., Holtz, G.,van derKeur, P., Gaber,N.,2008.Complexityanduncertainty:rethinkingthemodellingactivity. In:Environmental Modelling, Soft-wareandDecisionSupport.StateoftheArtandNewPerspectives,vol.3.ElsevierB.V.,pp.49–68,Retrievedfrom: http://www.sciencedirect.com/science/article/pii/S1574101X08006042

Campo,P.C.,Bousquet,F.,Villanueva,T.R.,2010.Modellingwithstakeholderswithinadevelopmentproject.Environ.Model. Softw.25(11),1302–1321.

Chappin,E.J.L.,2011.SimulatingEnergyTransitions.DelftUniversityofTechnology.

Chappin,E.J.L.,Dijkema,G.P.J.,2010.Agent-basedmodelingofenergyinfrastructuretransitions.Int.J.Crit.Infrastruct.6(2), 106–130.

Cohen,R.,Havlin,S.,2010.ComplexNetworks:Structure,RobustnessandFunction.CambridgeUniv.Press.

Cressie,N.,Calder,C.A.,Clark,J.S.,VerHoef,J.M.,Wikle,C.K.,2009.Accountingforuncertaintyinecologicalanalysis:thestrengths andlimitationsofhierarchicalstatisticalmodeling.Ecol.Appl.19,553–570,http://dx.doi.org/10.1890/07-0744.1. deHaan,H.,2008.Thedynamicsoffunctioning–investigatingsocietaltransitionswithpartialdifferentialequations.J.Comput.

Math.Org.Theory14,302–319.

deHaan,F.J.,2010.TowardsTransitionTheory.ErasmusUniversityRotterdam,Rotterdam.

deHaan,J.,Rotmans,J.,2011.Patternsintransitions:understandingcomplexchainsofchange.Technol.Forecast.Soc.Change 78(1),90–102,http://dx.doi.org/10.1016/j.techfore.2010.10.008.

deHaan,F.J.,Ferguson,B.C.,Deletic,A.,Brown,R.R.,2013.Asocio-technicalmodeltoexploreurbanwatersystemsscenarios. WaterSci.Technol.68(3),714–721.

Diehl,E.,Sterman,J.D.,1995.EffectsofFeedbackcomplexityondynamicdecisionmaking.Org.Behav.Hum.Decis.Processes 61(2),198–215.

Dörner,D.,1980.Onthedifﬁcultiespeoplehaveindealingwithcomplexity.Simul.Games11(1),87–106.

Doyle,J.K.,Ford,D.N.,1999.Mentalmodelsconceptsrevisited:someclariﬁcationsandreplytoLane.Syst.Dyn.Rev.15,411–415. Eising,J.,vanOnna,T.,Alkemade,F.,2014.Towardssmartgrids:identifyingtherisksthatarisefromtheintegrationofenergy

andtransportsupplychains.Appl.Energy123,448–455.

Elder-Vass,D.,2010.TheCausalPowerofSocialStructures.CambridgeUniversityPress.

Epstein,J.M.,Axtell,R.,1996.Growingartiﬁcialsocieties:socialsciencefromthebottomup.In:ComplexAdaptiveSystems. BrookingsInstitutionPress/MITPress,Washington,D.C(p.xv,208p.).

Epstein,J.M.,2008.Whymodel?J.Artif.Soc.Soc.Simul.11(4),12.

Foxon,T.J.,2013.TransitionpathwaysforaUKlowcarbonelectricityfuture.EnergyPolicy52,10–24.

Foxon,T.J.,Hammond,G.P.,Pearson,P.J.G.,2010.DevelopingtransitionpathwaysforalowcarbonelectricitysystemintheUK. Technol.Forecast.Soc.Change77,1203–1213.

Frenken, K., 2006. Technological innovation and complexity theory. Econ. Innov. N. Technol. 15 (2), 137–155, http://dx.doi.org/10.1080/10438590500141453.

Galán,J.M.,Izquierdo,L.R.,Izquierdo,S.S.,Santos,J.I.,delOlmo,R.,López-Paredes,A.,Edmonds,B.,2009.Errorsandartefactsin agent-basedmodelling.J.Artif.Soc.Soc.Simul.12(1)http://jasss.soc.surrey.ac.uk/12/1/1.html

Geels,F.,Schot,J.,2007.Typologyofsociotechnicaltransitionpathways.Res.Policy36,399–417.

Girvan,M.,Newman,M.E.J.,2002.Communitystructureinsocialandbiologicalnetworks.Proc.NatlAcad.Sci.U.S.A.99, 7821–7826.

Gosen,J.,Washbush,J.,2004.Areviewofscholarshiponassessingexperimentallearningeffectiveness.Simul.Gaming35(2), 270–293,183.

Grin,J.,Rotmans,J.,Schot,J.,2010.TransitionstoSustainableDevelopment.Routledge,NewYork,London.

Gurung,T.R.,Bousquet,F.,Trébuil,G.,2006.Companionmodeling,conﬂictresolution,andinstitutionbuilding:sharingirrigation waterintheLingmuteychuWatershed,Bhutan.Ecol.Soc.11(2),36.

Halbe, J., Reusser, D.E., Holtz, G., Haasnoot, M., Stosius, A., Avenhaus, W., Kwakkel, J., 2014. Lessons for model use in transition research: a survey and comparison with other research areas. Environ. Innov. Soc. Trans., http://dx.doi.org/10.1016/j.eist.2014.10.001.

Hamarat,C.,Kwakkel,J.H.,Pruyt,E.,2013.Adaptiverobustdesignunderdeepuncertainty.Technol.Forecast.Soc.Change80 (3),408–418,http://dx.doi.org/10.1016/j.techfore.2012.10.004.

Hamarat,C.,Kwakkel,J.H.,Pruyt,E.,Loonen,E.,2014.Anexploratoryapproachforadaptivepolicymakingbyusing multi-objectiverobustoptimization.Simul.Model.Pract.Theory,http://dx.doi.org/10.1016/j.simpat.2014.02.008.

Hammond,G.P.,Howard,H.R.,Jones,C.I.,2013.TheenergyandenvironmentalimplicationsofUKmoreelectrictransition pathways:awholesystemsperspective.EnergyPolicy52,103–116.

Hansen,T.,Coenen,L.,2013.TheGeographyofSustainabilityTransitions:ALiteratureReview.PaperNo.2013/39.Lund Uni-versity,Sweden,Lund,Sweden.

Harary,F.,Norman,R.Z.,Cartwright,D.,1965.StructuralModels:AnIntroductiontotheTheoryofDirectedGraphs.Wiley,New York.

(18)

Hare,M.,2011.Formsofparticipatorymodellinganditspotentialforwidespreadadoptioninthewatersector.Environ.Policy Gov.21,386–402,http://dx.doi.org/10.1002/eet.590.

Haxeltine,A.,Whitmarsh,L.,Bergman,N.,Rotmans,J.,Schilperoord,M.,Köhler,J.,2008.Aconceptualframeworkfortransition modelling.Int.J.Innov.Sustain.Dev.3(1–2),93–114.

Holtz,G.,2010.ModellingSystemInnovationsinCoupledHuman–Technology–EnvironmentSystems.UniversityofOsnabrueck, Retrievedfrom:http://repositorium.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201006116333

Holtz,G.,2011.Modellingtransitions:anappraisalofexperiencesandsuggestionsforresearch.Environ.Innov.Soc.Trans.1 (2),167–186,http://dx.doi.org/10.1016/j.eist.2011.08.003.

Holtz,G.,2012.ThePSMapproachtotransitions:bridgingthegapbetweenabstractframeworksandtangibleentities.Technol. Forecast.Soc.Change79(4),734–743,http://dx.doi.org/10.1016/j.techfore.2011.10.005.

Holtz,G.,Pahl-Wostl,C.,2012.Anagent-basedmodelofgroundwaterover-exploitationintheUpperGuadiana,Spain.Reg. Environ.Change12(1),95–121,http://dx.doi.org/10.1007/s10113-011-0238-5.

Jahn,T.,Bergmann,M.,Keil,F.,2012.Transdisciplinarity:betweenmainstreamingandmarginalization.Ecol.Econ.79,1–10. Janssen,M.A.,Ostrom,E.,2006.Empiricallybased,agent-basedmodels.Ecol.Soc.11.(2).

Jones,N.A.,Perez,P.,Measham,T.G.,Kelly,G.J.,d’Aquino,P.,Daniell,K.A.,Dray,A.,Ferrand,N.,2009.Evaluatingparticipatory modeling:developingaframeworkforcross-caseanalysis.Environ.Manag.44(6),1180–1195.

Kelly,H.,Howell,D.,Glinert,E.,Holding,L.,Swain,C.,Burrowbridge,A.,Roper,M.,2007.Howtobuildseriousgames.Commun. ACM50(7),45–49,183.

Kleinmutz,D.N.,1993.Informationprocessingandmisperceptionsoftheimplicationsoffeedbackindynamicdecisionmaking. Syst.Dyn.Rev.9(3),223–237.

Köhler,J.,Whitmarsh,L.,Nykvist,B.,Schilperoord,M.,Bergman,N.,Haxeltine,A.,2009.Atransitionsmodelforsustainable mobility.Ecol.Econ.68(12).

Kwakkel,J.H.,Walker,W.E.,Marchau,V.A.W.J.,2010.Classifyingandcommunicatinguncertaintiesinmodel-basedpolicy analysis.Int.J.Technol.PolicyManage.10(4),299–315,http://dx.doi.org/10.1504/IJTPM.2010.036918.

Kwakkel,J.H.,Walker,W.E.,Marchau,V.A.W.J.,2012.Assessingtheefﬁcacyofadaptiveairportstrategicplanning:resultsfrom computationalexperiments.Environ.Plan.B:Plan.Des.39(3),533–550.

Kwakkel,J.H.,Auping,W.L.,Pruyt,E.,2013.Dynamicscenariodiscoveryunderdeepuncertainty:thefutureofcopper.Technol. Forecast.Soc.Change80(4),789–800,http://dx.doi.org/10.1016/j.techfore.2012.09.012.

Lang, D.J., Wiek, A., Bergmann, M., Stauffacher, M., Martens, P., Moll, P., Swilling, M., Thomas, C.J., 2012. Trans-disciplinary research in sustainability science–practice, principles, and challenges. Sustain. Sci. 7 (1), 25–43, http://dx.doi.org/10.1007/s11625-011-0149-x.

Laver,M.,2005.Policyandthedynamicsofpoliticalcompetition.Am.Polit.Sci.Rev.99,18.

Lempert,R.J.,Popper,S.W.,Bankes,S.C.,2003.ShapingtheNextOneHundredYears:NewMethodsforQuantitative,Long-Term PolicyAnalysis.SantaMonica,RAND.

Lempert,R.J.,Collins,M.,2007.ManagingtheRiskofUncertainThresholdResponse:ComparisonofRobust,Optimum,and PrecautionaryApproaches.RiskAnal.24(4),1009–1026,http://dx.doi.org/10.1111/j.1539-6924.2007.00940.x.

Lendaris,G.G.,1980.StructuralModeling–ATutorialGuide.IFEETransactionsonSystems,Man,andCybernetics.Volume10, No.12.

Liu,Y.,Gupta,H.,Springer,E.,Wagener,T.,2008.Linkingsciencewithenvironmentaldecisionmaking:experiencesfroman integratedmodelingapproachtosupportingsustainablewaterresourcesmanagement.Environ.Model.Softw.23(7), 846–858.

Liu,Y.Y.,Slotine,J.J.,Barabasi,A.L.,2012.ControlCentralityandHierarchicalStructureinComplexNetworks.PLOSONE7(9), e44459,http://dx.doi.org/10.1371/journal.pone.0044459.

Loorbach,D.,2010.Transitionmanagementforsustainabledevelopment:aprescriptive,complexity-basedgovernance frame-work.Governance23(1),161–183.

LutheT.,WyssR.(inrevision).Communityresiliencetoclimatechangeinacross-scaletourismgovernancecontext:acombined quantitative-qualitativenetworkanalysis.Ecology&Society.

Luthe,T.,Wyss,R.,Schuckert,M.,2012.Networkgovernanceandregionalresiliencetoclimatechange:empiricalevidencefrom mountaintourismcommunities.Reg.Environ.Change12(4),839–854,http://dx.doi.org/10.1007/s10113-012-0294-5. Malekpour,S.,deHaan,F.J.,Brown,R.R.,2013.MarryingExploratoryModellingtoStrategicPlanning:TowardsParticipatory

ModelUse.In:Presentedatthe20thInternationalCongressonModellingandSimulation(MODSIM2013).

Mathevet,R.,LePage,C.,Etienne,M.,Lefebvre,G.,Poulin,B.,Gigot,G.,Proréol,S.,Mauchamp,A.,2007.BUTORSTAR:Arole-playing gameforcollectiveawarenessofwisereedbeduse.Simul.Gaming38(2),233–262.

Mayntz,R.,2004.MechanismsintheAnalysisofSocialMacro-Phenomena.Philos.Soc.Sci.34,237–259.

Michael,D.,Chen,S.,2005.SeriousGames:GamesThatEducate,Train,andInform.Muska&Lipman/Premier-Trade. Mobjörk,M.,2010.Consultingversusparticipatorytransdisciplinarity:areﬁnedclassiﬁcationoftransdisciplinaryresearch.

Futures42(8),866–873.

Modarres,M.,2006.Predictingandimprovingcomplexbusinessprocesses:valuesandlimitationsofmodelingandsimulation technologies.In:Proceedingsofthe38thconferenceonWintersimulation,ISBN1-4244-0501-7,pp.598–603.

Ormerod,P.,Rosewell,B.,2009.Validationandveriﬁcationofagent-basedmodelsinthesocialsciences.In:Squazzoni,F.(Ed.), EpistemologicalAspectsofComputerSimulationintheSocialSciences.LectureNotesinComputerScience,vol.5466. SpringerBerlin/Heidelberg,pp.130–140.

Ostrom,E.,2007.Adiagnosticapproachforgoingbeyondpanaceas.Proc.Natl.Acad.Sci.U.S.A.104(39),15181–15187. Pahl-Wostl,C.,Hare,M.P.,2004.Processesofsociallearninginintegratedresourcesmanagement.J.Commun.Appl.Soc.Psychol.

14,193–206.

Papachristos,G.,2011.Asystemdynamicsmodelofsocio-technicalregimetransitions.Environ.Innov.Soc.Trans.1(2),202–233, http://dx.doi.org/10.1016/j.eist.2011.10.001.

Papachristos,G.,2012.TheEnvironmentalAttributesofManufacturingStrategy:AnEvolutionaryInstitutionalApproach. Uni-versityofPatras,Greece.