BillRand AssistantProfessorofBusinessManagement

PooleCollegeofManagementNorthCarolinaStateUniversity

An Introduction to Agent-Based Modeling

Unit 1: What is ABM and Why Should You Use It?

Picture by Milo Bostock (https://www.flickr.com/photos/milesmilo/25121357602) Used under Creative Commons Attribution 2.0 (https://creativecommons.org/licenses/by/2.0/)

TheBoidsModel (CraigW.Reynolds,SIGGRAPH,1987)

• Howdobirdsflock?• Isthereacentralleader?• Dotheyknowexactlywheretobeatalltimes?• Isitadeterministicprocess?• Cantheyactbasedonlocalinformation?

08/29/09

ThreeRulesofBoids

• Cohere• Movetowardthecenterofyourflockmates

• Align• Moveinthesamedirectionasyourflockmates

• Avoid• Donotgettooclosetoanyofyourflockmates

CourseStructure

1. WhatisAgent-BasedModelingandWhyShouldYouUseIt?2. BeginningwithSimpleModels3. ExtendingModels4. AFullModel5. TheArchitectureofanAgent-BasedModel6. AnalyzingAgent-BasedModels7. Verification,Validation,andReplication8. ApplicationandHistoryofABM9. AdvancedABM

CourseInstructors

William(Bill)Rand-LeadInstructor

AnamariaBerea-AssistantInstructor

ContactingUs

• Email:abm@complexityexplorer.org• Twitter:@intro2abmor@billrand• GoogleHangouts:

• WeeklyonTuesdayfrom11-12EST• Linkswillbeposted

Assignments

• Quizzes-Interspersedthroughtheunits• Typically2-3questions

• Tests-Attheendofeveryunit• Longerthanquizzes• Mayrequiresomemodelrunningorprogramming

• FinalProject-Dueattheendofthecourse• Checkpointsalongtheway• Developedovertheentirecourse

Software

• NetLogo• http://ccl.northwestern.edu/netlogo• Gothroughthetutorial

• R• http://www.r-project.org/• ManyTutorialsAvailable

RecommendedBook

• AnIntroductiontoAgent-BasedModeling• UriWilenskyandWilliamRand

• AvailableatMITPressandAmazon

https://mitpress.mit.edu/books/introduction-agent-based-modeling

http://www.intro-to-abm.com/

YourFirstAssignment

• ParticipantPoll• Wewanttofindoutwhoyouareandwhatyourbackgroundissowecantailorthiscourse

• DifferentfromthesurveythatComplexityExplorerwillbesendingout

WhatisaModel?

Anabstracteddescriptionofaprocess,object,oreventExaggeratescertainaspectsattheexpenseofothers

“Essentially,allmodelsarewrong,butsomeareuseful” (GeorgeBox,1987)

WhatisanAgent-BasedModel?Anagentisanautonomousindividualelementwithpropertiesandactionsinacomputersimulation

Agent-BasedModeling(ABM)istheideathattheworldcanbemodeledusingagents,anenvironment,andadescriptionofagent-agentandagent-environmentinteractions

ToolkitsforABM

WhyareweusingNetLogo?

NetLogoisapremieragent-basedmodelinglanguageanddevelopmentenvironment,designedbyUriWilenskyatNorthwesternUniversity.

ItisthemostwidelyusedABMenvironment.

It’stheeasiesttolearn.

TheNetLogoDesignPrinciple• Lowthreshold

– Novicescanbuildsimplemodelsatfirstuse– Pre-collegiatecurriculumincludescomplexsystemsandmodeling– Universitycoursestoincludemodel-basedinquiry– NewsandMediatoincludemodelsasevidenceforarguments

• Highceiling– Languageshouldbeexpressiveenoughtoenablehighendcomplex

models– Researchersto“read/write”andpublishmodels– Narrow/eliminategapbetweenmodelerandprogrammer– Enableinteractivedevelopmentandresearch– Easytosharemodels– Easytoverifyand/orchallengemodels

TheBirthoftheTurtle

Logowasfirstdevelopedin~1969bySeymourPapertandcolleagues

HowBig/AdvancedCanitGet?

• TensofThousandsofAgentsandPatches• ComplexDecisionMakers• ManyAgentTypes• ModelsofWholeCities• AdditionalToolsAllowforIntegrationwithotherSoftware

Redfish Group

Redfish Group

GrowingCities

[Lechneretal.,2006]

PolicyAnalysis

Part 2: ABMPart 1: GIS

landscape

Transit network

social-economic

data

Fuel Price

Create initial environment

Initialize households

Households relocate

Households choose travel modes

Part 3: TDM

Households make trips on

highway network

Households decide to own a car or not

Income Car

OwnershipCar Use

Low Density Land Use

Auto-dominant Transportation

System

Highway/Transit SystemPublic Policies Investment

OwnershipTax

Fuel taxZoning

Affordable carAuto financing

Employment sprawlResidential sprawl

Private sectors

Other socioeconomic

factors

Household formationFemale workforce

Transit agency

1995 TAZ

Rail network

Six counties

Environment in ABM

Year

Tran

sit S

hare

L2

L3

Point of no return

T1

T2T3

L1

Points of government intervention

Yandan Lu, 2009

ViralMarketing

Image Credit: Forrest Stonedahl

with Forrest Stonedahl and Uri Wilensky, 2010

08/29/09 VisualizationCourtesyofForrestStonedahl

InferringSocialNetworks

with Michael Trusov and Yogesh Joshi, 2010

DecisionSupportSystems

withManuelChica,2016NetworkVisualizationsbyJaredSylvester

WhoshouldIincentivizeandwhy?

WhatisComplexSystems?

• Asystemcomposedofmanyinteractingpartsinwhichtheemergentoutcomeofthesystemisaproductoftheinteractionsbetweenthepartsandthefeedbacksbetweenthatemergentoutcomeandindividualdecisions

http://ccl.northwestern.edu/netlogo/models/TrafficBasic NOMAD-http://www.flickr.com/photos/lingaraj/2415084235/sizes/l/CCBY2.0

Emergence

• Emergence=‘theactionofthewholeismorethanthesumoftheparts’(Holland,2014)

Feedbacks

• Theeffectoftheemergentresultonthedecisionsoftheindividuals

https://www.flickr.com/photos/thefrankfurtschool/1313097473/CCBY2.0

HowdoyouunderstandComplexSystems?

• ComplexSystemscanbedifficulttopredict,controlandmanage,whichinmanywaysisthegoalofpublicpolicy

• Agent-BasedModelingandComplexSystemsanalysisistoprovidea‘flightsimulator’ratherthanaperfectprediction(Holland,1996;Sterman,2000)

LeveragePoints

• Leveragepointsareplaceswherethecomplexsystemcanpotentiallybeshiftedfromoneregimetoanotherwiththeleasteffort(Bankes,1993)

• Relatedto:– TippingPoints:placeswhereasmallchangeinaninputcandramaticallyaffecttheoutcome(Scheffer,2010)

• ComplexSystemsanalysisoftengivesyouthemostwhenittellsyoutheleast

http://ccl.northwestern.edu/netlogo/models/Fire

PathDependence

PathDependenceiswhenthecurrentpossibilitiesarelimitedbypastchoices

Brownetal.,2005,IJGIS

SensitivitytoInitialConditions

– SensitivitytoInitialConditions(TheButterflyEffect):initsstrongformaconditionofchaoswhichsaysthateverystartingpointisarbitrarilyclosetoanotherstartingpointwithasignificantlydifferentfuture(Lorenz,1972)

• Chaos:whenthepresentdeterminesthefuture,buttheapproximatepresentdoesnotapproximatelydeterminethefuture.—Lorenz

– WeakVersion-Whereyoustartmatterssignificantly

https://www.flickr.com/photos/syobosyobo/304122319/CCBY2.0

Non-LinearityandDynamics

• Inputsdonotnecessarilyaffectoutputsinalinearmanner

• Interactionsbetweenvariousinputsmeanthatyoucannotjustsolveproblemsbybreakingthemdownone-by-one

http://ccl.northwestern.edu/netlogo/models/GiantComponent

Robustness

• Robustnessiswhenasystemmaintainsitscharacteristicbehaviorevenafterperturbation(Bankes,2002)

NetLogoSegregationModel

DiversityandHeterogeneity

• IndividualsinComplexSystemsareoftensignificantlydiverseandheterogeneous(Page,2010)

• Mosttraditionalmodelingapproachesfailtoaccuratelycapturetheheterogeneityofindividuals

InterconnectednessandInteractions

• Individualsareconnectedandaffecteachother’sdecision

BinLadenRetweetNetwork HurricaneSandyRetweetNetwork

US2012ElectionRetweetNetwork

Representation

• Representationisthekeytounderstandinganyphenomenon

• Asanexample,imaginewritingtheFlockingmodelasaseriesofequationsthatdescribewherethebirdsareandhowtheyaffecteachother

• Inmanycases,agent-basedrepresentationsareappropriate

BenefitsofAppropriateRepresentation

• Newrepresentationscanhelpussolveproblemswecouldnotsolvebefore

• Changingrepresentationscanhelpusasknewquestions

• Agent-basedrepresentationscanhelpustocommunicateourresults

RepresentationofComplexSystems

• Complexsystemsarecomposedofmanyinteractingparts

• Thosepartsareoftenconnectedincomplexways

• Agent-basedmodelingprovidesapowerfulwaytorepresentthoseconnections

AThirdWayofDoingScience(Axelrod,1997)

• Twotraditionalwaysofdoingscience• Induction-inferringfromparticulardataageneraltheory

• Deduction-reasoningfromfirstprinciplestoageneraltheory

• ThirdWay• Generative-usingfirstprinciplestogenerateaparticularsetofdatathatcancreateageneraltheory

IntegrativeUnderstanding

• Ifoneknowsthefirstprincipledrules,canyoudeterminetheaggregatepattern

• Thisisoftendifficult,andABMprovidesusawaytounderstandthis

DifferentialUnderstanding

• Whatiftheaggregatepatternisknownandyouwanttofigureouttheindividual-levelrules?

• Thisissimilartotheflockingmodelexercisewepreviouslyexplored

• Wecanproposerulesandseeiftheygeneratethephenomenonweobserve

WhentouseABM?

• MediumNumbers• Heterogeneity• ComplexbutLocalInteractions• RichEnvironments• Time• Adaptation

MediumNumbersCasti,1996

• Toofewagentsandthesimplemaybetoosimple• Gametheoryandethnographyworkwell

• Toomanyagentsandmeansmaydescribethesystemwell• Mean-fieldapproachesandstatisticaldescriptions

• Thekeyisthatthenumberofagentsthatcanaffecttheoutcomeofthesystembeamediumnumber

Heterogeneity

• Agentscanbeasheterogeneousastheyneedtobe

• Manyotherapproachesassumehomogeneityoverindividuals

ComplexbutLocalInteractions

• ABMcanmodelcomplexinteractions• Historydependent• Propertydependent

• Theassumptionisthatthesearelocal• Noglobalknowledge

RichEnvironments

• Theenvironmenttheagentsinteractincanbeextremelyrich

• SocialNetworks• Geographicalsystems• Theenvironmentcanevenhaveitsownagent-likerules

Time

• Almostallagent-basedmodelsfeaturetime• ABMisamodelofprocess• Nearlynecessary• Thereareexceptions

• Solvingcomplexequilibriumproblems

Adaptation

• Adaptationiswhenanagent’sactionsarecontingentontheirpasthistory

• Anagentmaytakedifferentactionsdependingonitsownpastexperience

• Usuallysufficient• VeryfewmodelingapproachesbesidesABMfeatureadaptiveindividuals

Agent-BasedModeling(ABM)vs.Equation-BasedModeling(EBM)

• ManyEBMsmaketheassumptionofhomogeneity• EBMsareoftencontinuousandnotdiscrete

• Thenano-wolfproblem(Wilson,1998)• EBMsrequireaggregateknowledgeinmanycases• OntologyofEBMsisatagloballevel• EBMsdonotprovidelocaldetail• EBMsareTop-Down,ABMsareBottom-Up• EBMsaregeneralizable,butrestricted• ABMcanbebuiltfromanalyticalmodels,andcancomplementEBMs

ABMandStatisticalModeling

• Hardtolinktofirstprinciplesandbehavioraltheory

• Needtohavetherightkindofdata• ABMcancomplementbybuildingfromfirstprinciplestostatisticalresults

ABMvs.LabExperiments

• Labexperimentscangeneratetheory• Labexperimentsarerarelyscaledup• ABMcanbecreatedfromlabexperiments

• ABMcanexploremacro-implicationsoflabexperiments

• ABMcangeneratenewhypotheses• ABMcandeterminesensitivityofresults• ABMcancomparegenerativeprinciples

ABMvs.AggregateComputerModeling

• SystemDynamicsModelingembracesasystem-levelapproachtothinkingabouttheworld

• However,itoftenlackstheindividual-levelrepresentation

• Hybridmodelsarepossible

Limitations

• HighComputationalCost– Benefitofmoreinsightanddatatointermediatestages

• ManyFreeParameters– Simplyexposingparametersthatothermodelsassume

• MayRequireIndividual-LevelBehavioralKnowledge– Providesbetterinsight

WhytheResistance?

• LackofEducationaboutComplexSystems• TheDrunk,TheKeysandTheStreetlight

– Peoplewanttosearchforsolutionswhereitiseasy

• CentralizedandDeterministicMindset(ResnickandWilensky,

1993)

– Peopleexpecttheirtobeacentralleader– Peopleexpectthateverythinghappensfora“cause”andnegatethepossibilityofchance

UsesofABM

• Description• Explanation• Experimentation• Analogy

• Education• Touchstone• ThoughtExperiments

• Prediction

Description

• AnABMisadescriptionofareal-worldsystem• Asimplifieddescriptionbutstilladescription• Modelsthatarenotsimplifiedareuseless• “Makeyourmodelassimpleaspossiblebutnosimpler.”-AlbertEinstein

Explanation

• AnABMprovidesanexplanationofpotentialunderlyingphenomenonthatcontrolasystem

• Theyareaproof-of-conceptthatsomethingispossible

• Theyilluminatethepowerofemergence

Experimentation

• ABMscanberunrepeatedlyunderslightlydifferentconditionstoobservetheresultantchanges

• Wecanchangethemodelandseewhathappens

• Wecanthengobacktothereal-worldandvalidatetheseexperiments

Analogy

• ABMshelpustounderstandothersystemwithsimilarpatternsofbehavior

• Forinstance,themodelofflockingbirdscanhelpusunderstandfishandevenlocusts

• Theycanevenhelpusunderstandengineeredsystems,e.g.,drones

Education/Communication

• ABMshelpuscommunicateourresultstoothers

• Theyencapsulateknowledgeinawaythatiseasilytransferrable

• Theyencourageexplorationaboutdifferenttheories

Touchstone

• ABMscreateafocalobject• Papert(1980)callsthemanobjecttothinkwith

• Theygiveusacommonlanguagetodescribeaphenomenonandtoargueaboutitscauses

• Theyturncomplexsystemsintoasetofsimplerules

ThoughtExperiments

• ABMscanexplorethingsthatmaynotevenexistintherealworld,orareveryidealizedexamplesoftherealworld

• ABMgivesusthepowertosaywhatwillhappenifweassumeafewbasicrules

Prediction

• ABMisoftenusedtothinkaboutpossiblefuturescenarios

• Butthevalidityofapredictionisdeterminedbyhowwellthemodelhasbeenvalidated

• Itisdifficulttoassessthevalidityofanymodelforaneventthathasnotyetoccurred

• Predictioncanoftenbereducedtodescriptionandexplanation

ComplexSystems,Agent-BasedModelingandPsychohistory• PsychohistoryisafictionalscienceusedbyIsaacAsimov’scharacter,HariSeldon,intheFoundationseries.

• Psychohistorycombineshistory,sociology,andmathematicstomakeapproximatepredictionsaboutthefuturebehavioroflargegroupsofpeople.

• ComplexSystemshasthepotentialtohelpusunderstandhowlargegroupsofindividualsandorganizationswillreacttofutureevents,potentiallypavingthewayforarealpsychohistory

• However,thegoalisnottomakespecificpredictions,butcanhelpustoembraceuncertainty

https://www.flickr.com/photos/uflinks/4955882191CCBY2.0

ThankYou

SeveraloftheseslidesbenefitedfromsignificantcontributionsfromForrestStonedahl,UriWilensky,andothers.