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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:[email protected]• 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.