Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior

342 Pages·2012·13.59 MB·English

by  Dirk Helbing (auth.)

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HenryAbarbanel,InstituteforNonlinearScience,UniversityofCalifornia,SanDiego,USA
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Pe´ter E´rdi, Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy
ofSciences,Budapest,Hungary
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ScottKelso,CenterforComplexSystemsandBrainSciences,FloridaAtlanticUniversity,BocaRaton,USA
Markus Kirkilionis, Mathematics Institute and Centre for Complex Systems, University of Warwick,
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Ju¨rgenKurths,NonlinearDynamicsGroup,UniversityofPotsdam,Potsdam,Germany
LindaReichl,CenterforComplexQuantumSystems,UniversityofTexas,Austin,USA
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FrankSchweitzer,SystemDesign,ETHZurich,Zurich,Switzerland
DidierSornette,EntrepreneurialRisk,ETHZurich,Zurich,Switzerland
StefanThurner,SectionforScienceofComplexSystems,MedicalUniversityofVienna,Vienna,Austria
Understanding Complex Systems
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Dirk Helbing
Editor
Social Self-Organization
Agent-Based Simulations and Experiments
to Study Emergent Social Behavior
123
Editor
DirkHelbing
ETHZurich,CLUE1
ChairofSociology,inparticularofModelingandSimulation
Clausiusstrasse50
8092Zurich
Switzerland
ISSN1860-0832 ISSN1860-0840(electronic)
ISBN978-3-642-24003-4 ISBN978-3-642-24004-1(eBook)
DOI10.1007/978-3-642-24004-1
SpringerHeidelbergNewYorkDordrechtLondon
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Preface
Socialsystemsarethemostcomplexsystemsweknow.Theyareevenmorecomplex
thanphysicalorbiologicalsystems.Theircomplexityresultsnotonlyfrommultiple
interactions of individuals, but also from the complexity of cognitive systems.
I, therefore, agree with August Comte that sociology is the queen of science, the
ultimatescientificchallenge.Ifurthermorebelievethatthisfieldwillsoonbeoneof
themostdynamicscientificareas,notonlyforitsinterestingfundamentalquestions,
butalsobecauseofthemanypracticalproblemshumanityisfacinginthetwenty-
firstcentury.
The crucial question is, how one can make substantial progress in a field as
complicatedand multi-facetedas the social sciences. There are certainly different
possibilities, as Chap.1 discusses. Nevertheless, it seems that many characteristic
featuresofcomplexsocialsystemscanbeunderstoodfromsimplemodelsofsocial
interactions, and I am convinced that a number of challenging scientific puzzles
canbesolved,usingconceptsfromcomplexitytheory,includingself-organization,
coevolution, and emergence. Agent-based computational models and behavioral
experimentscan revealthe mechanismsunderlyingsuch phenomena,and the role
thatdifferentfactorsplayforthem.
Complex systems often display a counter-intuitive behavior. For example, as
will be shown in this book, the same kinds of social interactions can lead to
oppositeconclusions,wheninteractionsoccurwith neighbors,friends,colleagues,
or business partners rather than with average interaction partners (see Chaps.7
and8). Therefore,a simplenonlinearmodelmayexplainphenomena,whicheven
complicatedlinearmodelsmayfailtoreproduce.Nonlinearmodelsareexpectedto
shednewlightonsuchsocialphenomena.Theymayevenleadtoaparadigmshift
inthewayweinterpretsociety.
While part of this book is a compilation of recently published papers, some
chapters present variants of previous work or new materials, for example, on the
techniqueandfutureofagent-basedcomputersimulationandoncoordinationgames
in networks (a subject that is interesting to understand the competitive spreading
of innovations). The chapters do not need to be read in sequential order, but the
organizationofthisbookhasaclearlogic:
v
vi Preface
(cid:129) Chapter 1 discusses the issue of how to describe social systems. It highlights
different traditions in the social sciences and the respective advantages and
disadvantagesoftheseapproaches,stressingtheircomplementarycharacter.The
focus of this book, however, will be on simple models of social interactions,
explainingvariousaspectsofsocialself-organization.
(cid:129) Chapter 2discussesthemethodofagent-basedcomputationalmodeling,howto
doitright,andwhatarefutureperspectivesofthisapproach.Aparticularfocusis
putonthequestion,howtoavoidmistakesandhowtoderivemeaningfulresults.
(cid:129) Chapter 3 demonstrates the concept of self-organization for the example of
pedestrian crowds. We analyze the spontaneousoutbreak of social order under
everyday conditionsand its breakdownat extreme densities. The chapter illus-
trates how fundamental research can lead to useful models that enable an
understanding of macroscopic outcomes of social interactions. It also summa-
rizesrelatedempiricalandexperimentalworkaswellaspracticalapplications.
(cid:129) Chapter 4 provides a second example of agent-based modeling, namely a
continuousopinionformationmodel.Itshowswhypreviousmodelsdidnotsolve
thepuzzlewhyonefindsglobaldiversityinspiteoflocalconvergence.Strikingly,
itisthetendencyofindividualizationwhichpromotespluralismthroughtheself-
organizationofgroups.
(cid:129) Chapter 5 turns the attention from mobility in opinion space to mobility in
geographical space. Assuming social interactions with neighboring locations,
where the outcome of these interactions is quantified by “payoffs” as this is
commoningametheory,wefindtheself-organizationofspatiotemporalpatterns,
when success-driven mobility to neighboring locations occurs. Even when
startingwithauniformdistributioninspace,weobserveinterestingsegregation
phenomenaanddifferentkindsofagglomerationphenomena,dependingonthe
respective payoff structure. These come about when success-driven mobility
increaseslocaldifferencesandtherebydestabilizesahomogeneousdistribution
inspace.
(cid:129) Chapter 6 focuses on the problem of cooperationin social dilemma situations,
whereitappearsmoreadvantageoustoselfishindividualstoexploitothersthan
to cooperate with them. It is discussed in what ways social mechanisms can
effectively change the payoff structure and, thereby, the rules and character of
the“game”individualsareplaying.Itisshownthatdifferentmechanismssuchas
repeatedinteractions,reputationeffects,orsocialnetworkingcanimplydifferent
routestocooperation.Thechapteralsoprovidesaclassificationofdifferentkinds
oftransitionstocooperationandshowsthatadaptivegrouppressurecanpromote
cooperationintheprisoner’sdilemmagameevenwithoutchangingtheproperties
ofitsequilibriumsolutions.
(cid:129) Chapter 7 combines the elements of Chaps.5 and 6, i.e., it studies individuals
facing social dilemma situations in space, consideringsuccess-driven mobility.
While one would think that social cooperation and mobility are unrelated, it
surprisingly turns out that mobility is an important factor supporting human
sociality, and that it promotes a co-evolution of social behavior and social
environment. This model may shed new light on a number of fundamental
Preface vii
questionssuchthefollowing:Whydosimilarlybehavingpeopletendtoagglom-
erate, e.g., form groups or cities (an observation which is often referred to as
“homophily”)?Howare“socialmilieus”comeaboutandwhydotheyinfluence
thebehaviorofpeople?Whydotheypersistsolong?Andwhyareselfishpeople
more cooperativein reality than expected?What is the role of fluctuationsand
exploratorybehaviorfortheemergenceofcooperation?1
(cid:129) Chapter 8 looks at a further mechanism that has been suggested to promote
cooperation,namely “costly punishment.”It is shown that the considerationof
neighborhoodinteractionscanresolvetheso-calledsecond-orderfree-riderpuz-
zlethatwonderswhypeoplewouldinvestintothepunishmentofuncooperative
behavior,if theycanprofitfromotherpeople’ssanctioningefforts.Thechapter
also suggests that the spreading of morals and double moral behavior can
be understood with concept of evolutionary game theory. The related system
dynamicsshowsquiteanumberofsurprisingfeatures.2
(cid:129) Chapter 9 studies effects of network interactions and transaction costs in
coordination games. Developing a percolation theoretical description for the
related system dynamics allows one to analytically understand the competitive
spreading of innovations, opinions, or products from a new scientific angle.
Furthermore,wepointoutthatsystem-widecoordinationisadoubleedgesword.
(cid:129) Chapter 10 focuses on the implications of heterogeneity in the inclinations
of individuals. For this, we study the interaction of several populations with
incompatible preferences. This implies a large variety of different system
behaviors, such as the outbreak or breakdown of cooperation, the formation
of commonly shared norms, the evolution of subcultures, or the occurrence
of conflicts which may cause “revolutions”. Despite its richness, the model is
simple enough to facilitate an analytical understanding of the possible system
behaviors.3 Itwould behighlydesirableto testthe predictionsof themodelby
behavioralexperiments.4
1Thefollowingrelatedpapermayinterestthereaderaswell:D.Helbing,W.Yu,andH.Rauhut
(2011) Self-organization and emergence in social systems: Modeling the coevolution of social
environmentsandcooperativebehavior.JournalofMathematicalSociology35,177–208.
2The reader may be interested inthe related follow up work as well: D. Helbing, A. Szolnoki,
M. Perc, and G. Szabo´ (2010) Punish, but not too hard: how costly punishment spreads inthe
spatialpublicgoodsgame.NewJournalofPhysics12,083005;D.Helbing,A.Szolnoki,M.Perc,
andG.Szabo´(2010)Defector-acceleratedcooperativenessandpunishmentinpublicgoodsgames
withmutations.PhysicalReviewE81(5),057104.
3D. Helbing and A. Johansson (2010) Evolutionary dynamics of populations with conflicting
interactions:Classificationandanalyticaltreatmentconsidering asymmetryandpower.Physical
ReviewE81,016112.
4Recently,theemergenceofsocialnormshasbeenfurtherinvestigated,andtherelatedstudymay
beinterestingforthereaderaswell:D.Helbing,W.Yu,K.-D.Opp,andH.Rauhut(2011) The
emergenceofhomogeneousnormsinheterogeneouspopulations.SantaFeWorkingPaper11-01-
001,seehttp://www.santafe.edu/media/workingpapers/11-01-001.pdf.
viii Preface
(cid:129) Chapter 11, therefore, discusses ways in which social experiments should and
couldbedoneinthefuture,usingthehelpofcomputers.
(cid:129) Chapter 12 focuses on the example of a route choice experiment, in which
a sudden transition to turn-taking behavior is found after many interactions,
and it is shown that this transition can be understood with a reinforcement
learningmodel.
(cid:129) Chapter 13 analyzes the same route choice game for the case of several
participatingplayers.Itisfoundthat,ratherthanapplyingprobabilisticstrategies,
experimentalparticipantsdevelopspecializedandalmostdeterministicstrategies
over time. There is strong evidence of social differentiation. Furthermore, we
showhowanindividualizedinformationsystemcanbedevelopedthatsupports
socialadaptationandavoidsaself-defeatingprophecyeffect.
(cid:129) Chapter 14 then looks at social systems from a complex systems perspective.
In this way, it analyzes systemic socioeconomic risks and the underlying
mechanisms.Inparticular,itdiscussesfactorsthathaveprobablycontributedto
thecurrentfinancialcrisis.
(cid:129) Chapter 15 addresses the question, how to manage the complexity of social
systems,consideringthatclassicalcontrolconceptsareknowntofail.
(cid:129) Chapter 16 finally tries to identify fundamental and real-world challenges in
economics,therebysuggestingquestionsandapproachesforfutureresearch.5
Althoughthesecontributionshaveoriginallynotbeenwrittenaschaptersofabook,
theyarelargelycomplementaryto each otherandfollowa commonapproachthat
triestounderstandmacroscopicbehavioralpatternsfrominteractionsbetweenmany
individuals. All models pursue an agent-based approach, and for many chapters,
supplementary video animations are available at http://www.soms.ethz.ch . Most
of the models furthermore follow an evolutionary game theory perspective and
are mutually consistent. In fact, the wide spectrum of phenomena that can be
describedbyevolutionarygametheoreticalmodels,rangingfromcoordinationand
cooperation over social norms and conflict up to revolutions, suggests that this
theoreticalframeworkmaybeflexibleenoughtoformthebasisofafutureintegrated
theoryofsocioeconomicinteractions.Withthisvisioninmind,Ihopethereaderwill
findthisbookinspiring.
Zurich DirkHelbing
5Beyondproposingresearchquestions,theVisioneerWhitepapers“FromSocialDataMiningto
ForecastingSocio-EconomicCrises,”“FromSocialSimulationtoIntegrativeSystemDesign,”and
“HowtoCreateanInnovationAccelerator”makesuggestionshowtofosterscientificprogressin
thesocioeconomicsciences,seehttp://www.visioneer.ethz.ch.ThesearepublishedinEPJSpecial
Topics195,1–186(2011).
Acknowledgments
The research reported in this book and its production were partially supported
by the Future and Emerging Technologies programme FP7-COSI-ICT of the
EuropeanCommission throughthe projectQLectives (grantno. 231200),through
theCoordinationandSupportActionGSDP-GlobalSystemDynamicsandPolicy
oftheEuropeanCommission(grantno.266723),andtheFETFlagshipPilotProject
FuturICT(grantno.284709).
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