Table Of ContentAgent-Based Social Systems
Volume 9
EditorinChief:
HiroshiDeguchi,Yokohama,Japan
SeriesEditors:
Shu-HengChen,Taipei,Taiwan,ROC
ClaudioCioffi-Revilla,Fairfax,USA
NigelGilbert,Guildford,UK
HajimeKita,Kyoto,Japan
TakaoTerano,Yokohama,Japan
Forfurthervolumes:
www.springer.com/series/7188
ABSS–Agent-Based Social Systems
Thisseriesisintendedtofurtherthecreationofthescienceofagent-basedsocialsystems,
a field that is establishing itself as a transdisciplinary and cross-cultural science. The se-
rieswillcoverabroadspectrumofsciences,suchassocialsystemstheory,sociology,busi-
nessadministration, managementinformationscience,organization science,computational
mathematicalorganization theory,economics,evolutionary economics,international politi-
calscience,jurisprudence,policyscience,socioinformationstudies,cognitivescience,artifi-
cialintelligence,complexadaptivesystemstheory,philosophyofscience,andotherrelated
disciplines.
Theserieswillprovideasystematicstudyofthevariousnewcross-culturalarenasofthehu-
mansciences.Suchanapproachhasbeensuccessfullytriedseveraltimesinthehistoryofthe
modernscienceofhumanitiesandsystemsandhashelpedtocreatesuchimportantconceptual
frameworksandtheoriesascybernetics,synergetics,generalsystemstheory,cognitivesci-
ence,andcomplexadaptivesystems.Wewanttocreateaconceptualframeworkanddesign
theory for socioeconomic systems of the twenty-first century in a cross-cultural and trans-
disciplinarycontext.Forthispurposeweplantotakeanagent-basedapproach.Developed
overthelastdecade,agent-basedmodelingisanewtrendwithinthesocialsciencesandisa
childofthemodernsciencesofhumanitiesandsystems.Inthisseriestheterm“agent-based”
isusedacrossabroadspectrumthatincludesnotonlytheclassicalusageofthenormative
andrationalagentbutalsoaninterpretiveandsubjectiveagent.Weseektheantinomyofthe
macroandmicro,subjectiveandrational,functionalandstructural,bottom-upandtop-down,
globalandlocal,andstructureandagencywithinthesocialsciences.Agent-basedmodeling
includesbothsidesoftheseopposites.“Agent”isourgroundingformodeling;simulation,
theory,andreal-worldgroundingarealsorequired.
Asanapproach,agent-basedsimulationisanimportanttoolforthenewexperimentalfields
ofthesocialsciences;itcanbeusedtoprovideexplanationsanddecisionsupportforreal-
worldproblems,anditstheoriesincludebothconceptualandmathematicalones.Aconcep-
tualapproachisvitalforcreatingnewframeworksoftheworldview,andthemathematical
approachisessentialtoclarifythelogicalstructureofanynewframeworkormodel.Explo-
rationofseveraldifferentwaysofreal-worldgroundingisrequiredforthisapproach.Other
issuestobeconsideredintheseriesincludethesystemsdesignofthiscentury’sglobaland
localsocioeconomicsystems.
EditorinChief
HiroshiDeguchi
ChiefofCenterforAgent-BasedSocialSystemsSciences(CABSSS)
TokyoInstituteofTechnology
4259Nagatsuta-cho,Midori-ku,Yokohama226-8502,Japan
SeriesEditors
Shu-HengChen,Taipei,Taiwan,ROC
ClaudioCioffi-Revilla,Fairfax,USA
NigelGilbert,Guildford,UK
HajimeKita,Kyoto,Japan
TakaoTerano,Yokohama,Japan
Koen H. van Dam (cid:2) Igor Nikolic (cid:2) Zofia Lukszo
Editors
Agent-Based
Modelling of
Socio-Technical
Systems
Editors
KoenH.vanDam ZofiaLukszo
FacultyofTechnology,Policy FacultyofTechnology,Policy
andManagement andManagement
DelftUniversityofTechnology DelftUniversityofTechnology
Delft,theNetherlands Delft,theNetherlands
IgorNikolic
FacultyofTechnology,Policy
andManagement
DelftUniversityofTechnology
Delft,theNetherlands
ISSN1861-0803 Agent-BasedSocialSystems
ISBN978-94-007-4932-0 ISBN978-94-007-4933-7(eBook)
DOI10.1007/978-94-007-4933-7
SpringerDordrechtHeidelbergNewYorkLondon
LibraryofCongressControlNumber:2012948330
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Foreword
The first tentative efforts at ‘simulating societies’ using agent-based models were
madeintheearly1990s.Sincethen,therehasbeenanexplosivegrowthintheap-
plicationofagent-basedmodellinginthesocialsciences,withapplicationsinnearly
thewholesuiteofdisciplines,includingeconomics,sociology,geography,political
science,anthropology,linguisticsandevensocialhistory.Atfirst,theemphasiswas
mainlyonsmall,simplemodelsthatcouldilluminatebasicsocialsciencetheories.
For example, we saw models showing how cultures could differentiate, residen-
tialsegregationcouldemergefromhouseholdmovingdecisions,politicalopinions
could diverge towards the extremes and other similar examples. In addition, there
was a cross-fertilisation with game theory, since agent-based modelling enabled
scholars to break free of the constraints imposed by the need to obtain analytical
solutionsfrommathematicalformulations.
Ithasonlybeenwithinthelastdecadethatthestateofthearthasadvancedtothe
pointwhereithasbecomepossibletoenlargethescopeofagent-basedmodellingto
encompassmoreappliedandpolicy-orientedtopics.Thiswasaresultofagradually
improving understanding of the strengths and limitations of agent-based models,
togetherwithhugeimprovementsinthesupportinginfrastructure:muchbetterde-
velopmentenvironmentsandmuchfastercomputers,whichmadelargerandmore
complicatedmodelsfeasibletodevelopandtest.
Theteamwhowrotethisbookhavebeenpioneersinthismovetomoreapplied
models. Over the last decade, they have been accumulating experience about how
tobuildpolicy-orientedmodels,andwhatsuchmodelscanand,asimportant,what
they cannot be used for. This experience is the basis for this book. The book is
an immensely valuable resource because it draws on what the team have learned
fromalongseriesofapplicationsofagent-basedmodellingtotheunderstandingof
policy options for infrastructure systems and services. Although the team’s focus
is on infrastructure (such as electricity and transport), these lessons can easily be
transposed to other areas, such as health care, education,flood defence,the provi-
sionofrenewableenergysources—allofwhichhavebeenthetopicofagent-based
modellingeffortsinthelastyearortwo—andmanyothers.
v
vi Foreword
Agent-basedmodellinghasanumberofmajoradvantagesasasupportforpolicy
making.First,thebasicideaisaccessibleandeasytograsp,evenforthosewhoare
unfamiliar with the approach. As its name implies, an agent-based model consists
ofanumberof‘agents’representedinacomputerprogram.Eachagentcorresponds
toarealperson(ororganisation,firm,departmentorothergroup)intherealworld.
Theseagentsareprogrammedtointeractinthesamewaysastherealactorsdoand
to experience the same constraints and have access to the same knowledge. This
one-to-one correspondence between what we see in the policy world and what is
represented in the model makes it easy to grasp what the agent-based modelling
approach is about, especially in these days of computer games (which are often
rather similar to agent-based models, other than that they are designed to be fun,
ratherthantobegoodsimulations).
Agoodagent-basedmodelcanberelatively‘transparent’toinspectionbydeci-
sionmakers.Forexample,onecantryoutwhathappensinthemodelwhenagents
are given certain attributes, or are allocated particular behavioural rules, and can
checkthatthe modelbehavesin a plausiblefashion.If dataareavailable,thenthe
modeloutcomescanbecomparedwithwhatactuallyhappened.
Athirdadvantageofagent-basedmodellingisthatitcandealwithcomplexity.
Itisincreasinglybeingrealisedthatthesocialworldhastobeunderstoodasacom-
plexadaptivesystem,meaningthattheinteractionsbetweenitspartsarenon-linear
andmulti-level.Theseaspectscanbesimulatedinanaturalwaywithagent-based
models.Moreover,astheauthorspointout,whenoneisdealingwithquestionsof
infrastructure,onehastocontendnotwithjustasocialsystem,norwithjustatech-
nicalorphysicalsystem,butalsowiththecomplexinteractionsbetweenthesetwo.
Because both the social and the technical have an influence, this kind of mod-
ellingrequiresadisciplinarybreadth,asiswellillustratedinthepagesofthisbook.
One needs to understand and model a host of issues such as consumer behaviour,
political and organisational stresses, the operation of markets, the technicalities of
thedesignandmanufactureofelectronicproducts,andenvironmentalsustainability,
tolistjustsomeoftheareascoveredbythecasestudiesinthisbook.
Thisneedtobeajack-of-all-trades,aswellashavingskillsinmodelling,isoneof
thechallengesofthisstyleofpolicy-relevantsimulation.Anotheristoavoid‘over-
selling’one’smodel.Astheauthorswiselynote,theinterestsofthosewhowantto
use models and those who build models are rarely completely aligned. Modellers
are usually acutely aware of the limitations of their models and the way in which
theirbehaviourdependsontheassumptionsthathavebeenmade.Incontrast,those
whousemodelstoguidepolicydecisionswouldprefersimple,definiteandprecise
predictions of what the future will bring. There is thus a constant temptation for
modellerstopandertothedesiresoftheir‘clients’,andfordecisionmakerstoover-
interprettheadvicethemodelprovides.Ifagent-basedmodellinginthepolicyarena
is to avoid gaining a poor reputation, it needs to navigate the difficulty territory
between offering over-simple answers and being thought to be over-complicated
andunabletomakeanyworthwhilecontribution.
One refuge in which modellers have often sought shelter is to insist that their
models are only able to help with understanding the underlying mechanisms and
Foreword vii
cannot make predictions about the future. The distinction between understanding
and prediction is however a difficult and unrewarding one: while it is rarely pos-
sible to make point forecasts about the future behaviour of any complex system,
even developing a model for the purpose of understanding involves making some
predictions.Whatmodellersdoneedtodo,however,istocomprehendthelimitsof
prediction—forinstance,whiletheymaynotbeabletosaywhatwillhappen,itis
oftenpossibletosaywithrelativecertaintywhatwillnothappen.
Theauthorsemphasisetheimportanceofinvolving‘stakeholders’throughoutthe
modelbuildingprocess.Thishelpstokeepthemodelontrack,butitalsomeansthat
the stakeholders will have a stake in the model’s design, and will be less likely to
dismissitasthecreationofignorantoutsiders.Theadvicetoinvolvestakeholders
atallpointsindevelopingamodelisjustoneofthemanyinvaluablehintsthatthe
authorssprinklethroughthebook.
One direction that the development of policy-oriented modelling seems likely
to take is to involve non-modellers more and more closely in the design process.
Perhaps one day there will be an agent-based modelling tool as easy to use as the
spreadsheet,butevenifthathappens,therewillstillbeaneedforaclearandwell-
testedprocessforconceptualising,designingandvalidatingagent-basedmodelsof
socio-technicalsystems.Thisbookshowswhatthatmightlooklike.
Guildford,UK Prof.NigelGilbert
Preface
The book you have in front of you is the synthesised result of several research
projects executed at the Energy and Industry (E&I) group, part of the faculty of
Technology,Policy and Management of the Delft University of Technology in the
Netherlands.Since2004,theE&Igroupusestheagent-basedparadigmtodevelop
socio-technical models of infrastructure systems and services and their evolution.
Theresearcherssoonrealisedthatwecouldbemoreefficientbyworkingtogether
andbuildingontopofeachother’smodels,butalsothattherealityoftoday’ssocio-
technicalsystemsrequiresacomplexsystemsapproachwhichallowsustoexplic-
itly capture the multi-disciplinary nature of infrastructures, industrial systems and
services.
While many elements of our work have been published in PhD theses, book
chapters,journalsandproceedingsofinternationalconferences,untilnowtherewas
nosingleresourcewhichdescribedourcommonview,presentedthemethodology
which emerged from our work, and collected the lessons learned modelling as a
team.Inthisvolumewehavecollectedthebackground,theoryandpracticalsteps
and brought them together with a number of inspiring case studies that show how
thisapproachworksinpracticeandhowsuchmodelscansupportdecision-making.
Allauthorswhocontributedtothisbookplayedanimportantroleindeveloping,
crystallising and testing the methodology. So first of all, we would like to thank
themfortheirvaluablecontributionsandforsharingtheirexperience.Furthermore,
wewouldliketothanktheNextGenerationInfrastructuresFoundationandtheDelft
UniversityofTechnologyforthefinancialsupportofourresearchprojectsandfor
makingitpossibletowritethisbook.Aseditorswearemorethangratefulforthe
supportwereceivedfromDeborahSherwoodinproofreadingandlanguageediting
thetexts.
Finally, one small comment about the use of the word agent. We consider an
agent to be a representation of a decision-making entity in the real world, be it
an individual or an organisation, and it is a stylised part of the model. As such,
throughoutthisbookwehavereferredtoanagentas‘it’ratherthanwithapersonal
pronoun.
ix
x Preface
We hope students, researchers and business professionals using this book will
appreciatethetheoreticalbaseandpracticalguidelinesfordevelopingagent-based
models and that it will help successfully cope with the complexities of the design
andmanagementofsocio-technicalsystems.
Delft,theNetherlands KoenH.vanDam
IgorNikolic
ZofiaLukszo
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
G.P.J.Dijkema,Z.Lukszo,andM.P.C.Weijnen
1.1 WhyThisBook? . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 InfrastructuresasComplexAdaptiveSocio-technicalSystems . . . 2
1.3 BetterDecision-MakingNeeded. . . . . . . . . . . . . . . . . . . 4
1.4 Agent-BasedModellingforDecisionSupport . . . . . . . . . . . 5
1.5 ABookinTwoParts . . . . . . . . . . . . . . . . . . . . . . . . . 7
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
PartI TheoryandPractice
2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
I.NikolicandJ.Kasmire
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.2 StructureoftheChapter . . . . . . . . . . . . . . . . . . . 12
2.1.3 Example:WestlandGreenhouseCluster. . . . . . . . . . . 13
2.2 Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 HistoryofSystemsThinking . . . . . . . . . . . . . . . . 14
2.2.2 Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.3 WorldViews . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.4 Observer-Dependence . . . . . . . . . . . . . . . . . . . . 21
2.2.5 SystemBoundaries . . . . . . . . . . . . . . . . . . . . . 24
2.2.6 SystemNestedness. . . . . . . . . . . . . . . . . . . . . . 25
2.3 Adaptive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 AdaptationVersusEvolution . . . . . . . . . . . . . . . . 27
2.3.2 Evolution—MorethanjustBiology . . . . . . . . . . . . . 28
2.3.3 AdaptationinItsManyForms . . . . . . . . . . . . . . . . 30
2.3.4 DirectionofAdaptation . . . . . . . . . . . . . . . . . . . 31
2.3.5 CoupledFitnessLandscape . . . . . . . . . . . . . . . . . 32
2.3.6 Intractability . . . . . . . . . . . . . . . . . . . . . . . . . 34
xi
