BillRand AssistantProfessorofBusinessManagement

PooleCollegeofManagementNorthCarolinaStateUniversity

An Introduction to Agent-Based Modeling

Unit 9: Advanced Topics

Using Big Data and Agent-Based Modeling to Understand Social Media Diffusion

William Rand

in collaboration withDavid Darmon, Jimpei Harada, Jared Sylvester, and Michelle Girvan

The Beauty of Big Data

• Individuals leave their footprints (and fingerprints) in the digital sand.

• These traces represent digital / social signals of human behavior.

• We can use these signals to explore the complexity of individuals and digital environments.

The Challenge of Big Data

• What do we do with all this data?

• Aggregation of the data eliminates the richness of it

• The real benefit is when the data is used at the individual level

• How to make sense of that individual-level information?

Agent-Based Modeling

• Agent-Based Modeling provides a way to understand individual-level interactions

• Traditionally, agent-based models use simple rules derived from theory

• If we could create ABMs directly from big data we would have an individual-level detailed model derived directly from digital traces

The Solution

• Use Machine Learning to derive individual-level rules for agents automatically

• Then use the resulting ABM to understand the overall emergent properties of agent interactions

Creating Epsilon Machines

Assume was generated by a conditionally stationary stochastic process.

Explicitly learn the predictive distribution by grouping together pasts x that give equivalent predictions.

The Computational Mechanics Framework

Causal State Model (CSM) built for each user usingCausal State Splitting Reconstruction (CSSR)

Begin with one state and divide as necessary.

Computational Mechanics

Call the model learned the

causal state model (CSM)

for each user. CSM has the unique property of being “maximally predictive and minimally complex”

Learn this state-space representation of the process using

Causal State Splitting Reconstruction (CSSR).

Predicting Twitter Engagement Using ML

Two Case Studies

• Predicting Twitter Behavior

• Identifying Optimal Timing for Messaging

Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media

David Darmon, Jared Sylvester, Michelle Girvan, and William Rand

SocialCom 2013

Predicting Twitter Engagement Using ML

Opportunity: Social Media is a great marketing channel. Challenge: However, there is a lot of noise, and its not

apparent when users are paying attention. Solution: Understand when users are active. Goal: Create a tool that can accurately predict when

different segments will post content.

Motivation

Twitter users embedded in a 15k user follower network.

Statuses of all users collected over 7 weeks.

Select 3k subset of most frequently tweeting users.

The Dataset

Predicting Twitter Engagement Using ML

2013-08-22 12:54:06 Is Your Gmail Social? How to Use Gmail Daily to Build an Engaged Community | Socially Sorted http://t.co/WVprF4bLPa via 2013-08-22 13:11:22 Facebook's Embedded Posts Now Available to Everyone http://t.co/Cc0q3cSTt62013-08-22 13:14:06 The Credible Hulk http://t.co/q17VrcSdBs2013-08-22 13:29:02 25 Things You Didn’t Know About Ninjas http://t.co/CcJz92sRyy2013-08-22 13:32:59 Twitter Users: Revoke and Reestablish Third Party App Access Now http://t.co/qL9LbnGO6e via @ShellyKramer2013-08-22 13:48:46 10 Brilliant Facebook Marketing Tactics to Increase Reach and Engagement http://t.co/Z4YsudlJ35 via @kathikruse2013-08-22 14:17:11 Google Now Adds Cards for NCAA Football Scores, Concert Tickets, Car Rentals and More http://t.co/BG6Yz2zDyU2013-08-22 15:18:03 What is the NSA Really Up To? [COMIC] http://t.co/hakoq1mFNX2013-08-22 15:39:04 6 Things Every Good Business Blog MUST Have http://t.co/KRGzZRCrbm

Tweet TextTimestamp

User: DanielZeevi

The Setup

Predicting Twitter Engagement Using ML

Bin (in time) Twitter data, giving a discrete time series for each user v at time t:

The Setup

0 1 0 1 1 1 0 1 1 1… …

— user v doesn’t tweet— user v tweets

Time

Predicting Twitter Engagement Using ML

User: HadiJayaPutra

Results

Testing Procedure

• Build model for each user separately

• Training: 45 days

• Testing: 4 days

• Look back 10 steps

• Predict ahead 1 step

• 0-1 Loss

• Compare to “majority vote” baseline

Predicting Twitter Engagement Using ML

CSM vs. ESN

CSM vs. ESN

User: DanielZeevi

Base Rate:

CSM Rate:

ESN Rate:

0.4506

0.9477

0.9419

Forecasting High Tide:Predicting Times of Elevated

Activity in Online Social MediaDavid Darmon, Jimpei Harada, William Rand,

and Michelle GirvanASONAM, 2015

Conclusions

• Machine Learning plus Agent-Based Modeling provides a robust and powerful solution to harnessing the power of big data

• This method can provide predictive power that exceeds traditional aggregate methods

• Limitation: Requires high-resolution time series data

Future Work

• Construction of a software platform that would automatically generate agent-based models in a uniform way

• Implementation on GPGPU architectures

• Tests of the long-range predictive power of these models

ElaboratedandRealisticModels

• ManyABMsareverysimple• ElaboratedandRealisticmodelsaretheopposite

• varietyofmechanisms• useempiricaldata• matchavarietyofoutcomes• moreeasilyfalsifiable

• ImagineaSchellingmodelthatmatchedChicago’surbanpatterning

CriticismsofERModels

• Highlycontingent• Maynotbegeneralizable• Verydifficulttounderstand

FalseDichotomy?

• WedonotneedtochoosebetweensimpleandERmodels

• PatternOrientedModeling(POM;Grimmetal.,2006)arguesthatonemodelshouldbeabletoreplicatepatternsatmultiplelevelsofgranularity

• ThiscouldentailamodelthatisbothERandgeneralizable

• However,suchasingularperfectmodelmightbedifficulttocreate

FullSpectrumModeling

• FullSpectrumModelingistheideathatweshouldcreateasuiteofmodelsatdifferentlevelsofgranularity

• Youcanconstructsimplemodelsandelaborateandrealizethemasnecessarytocreatespecificmodels

• Simplemodelsexplorethenecessityandimportanceofmechanisms

• ERmodelscanexplorespecificinstancesandmakeparticularforecasts

ASuiteofModels

• Ratherthanthinkingofmodelsassingularmodels,thinkofthemassuites

• “…killyourdarlings.”-Faulkner

TypicalModelingSetup

• TwoGroupsofResearchers• SubjectMatterExperts• ModelImplementers

• TypicalModelingLifecycle1. SMEsdesignmodel2. Modelimplementersbuildmodel3. ImplementerspresentresultstoSMEs

ProblemswithTypicalSetup

• Modeldesignsarerarelycomplete• Earlyresultscouldhavedramaticimplicationsformodeldesign

• Lackofcommunicationresultsinlackofunderstanding

• Saying“That’swhatthemodelshows.”isneverenough,modeldesignersneedtounderstandwhythemodelshowsthat

IterativeModeling

• Itisimportantformodeldesignerandimplementertocommunicateoften

• NewModelingLifecycle1. SpecifyMinimalModel2. ImplementMinimalModel3. CommunicateResults

A. ReviseModelDesignB. Collectadditionaldataasnecessary

4. ExpandMinimalModelMinimally,goto#1• FailFast• Just-In-TimeModel(JIT)Construction

ABMsthatarenotforResearch

• MostofwhatwehavediscussedisABMsinthecontextofscientificresearch

• ButABMcanbeusedinothercontextsaswell• Communication• Persuasion• Education• DecisionSupport

TheProblemwithComplexSystems

• ComplexSystemsoftenrequireknowledgeformultiplesubjectareas,butalsoknowledgefromdifferentbackgrounds

• e.g.,Urbanplanning• scientists• policymakers• citizens• businesses• citymanagers• emergencyservices

ABMasCommunication

• AnABMcanbecreatedasan“objecttothinkwith”(SeymourPapert),i.e.,asharedfocalobject

• Thiscanthenbepotentiallyunderstoodandexaminedbyallstakeholdersasacommunicationtool

• UsingalanguagelikeNetLogofacilitatesthisbyprovidingasimplelanguagetounderstand

• ImagineCitySimasasolutiontooururbanplanningproblem

ABMsasPersuasion

• Nowthatthemodelhasbeenconstructedandcommunicated

• StakeholderscanuseCitySimtoarguefortheirownpolicies

• “policyflightsimulator”-Holland,1996

ABMasEducation

• Thesamemodelcouldalsobeusedtoeducatestudentsabouthowcomplexsystemswork

• ABMhasbeenusedforchemistry,materialsscience,electromagnetism,andevolutionamongmanyothersubjects

• ABMenableslearningbecausemicro-rulesareofteneasiertounderstandthanmacro-behaviors

• ABMencouragesagenerativeunderstandingofphenomenon

ABMasDecisionSupport

• ABMcanbeusedtoexploretheeffectofawidevarietyofstrategies

• Forinstance,explorehowtouseABMtoidentifyoptimalword-of-mouthmarketingdecisions(inpress,JMR,ChicaandRand)

AnonymousProcedures

• AnonymousProceduresarebitsofcodethatwedonotgivenamestoandthenwecanpasstheseprocedurestootherprocedurestosolvecomplexproblems

• SETSETUP1[[]->RANDOM4]• SETSETUP2[[]->BLUE]• ASKPATCHES[SETPCOLORRUNRESULTSETUP1(orSETUP2)]

RUN/RUNRESULT

• RUNandRUNRESULTbothtakestrings,setsofcommandsoranonymousprocedures,i.e.,“code”andrunthem

• Thisallowsyoutodynamicallycreatecodeduringtherunofthemodel

• ThedifferenceisthatRUNRESULTreturnsaresult,i.e.,areporter

• RUN[BK1LT90RT90]• SETHEADINGRUNRESULT[90+45*2]

MAP

• MAPtakesareporterandalistandappliesthereportertoallelementsofthelist

• map[val->round(val/1000)][34678125000758902500035123]

• (companyrevenuein1000s)• (map[[revemp]->round((rev/emp)/1000)][34678125000758902500035123][10100203050])

• (companyrevenueperemployeeinthousands)• fromElFarol

• sum(map[[weightweek]->weight*week]butfirststrategysubhistory)

REDUCE

• REDUCEtakesalistandappliesonereportertoeachiteminthelistfromlefttoright• (reduce+[35104542835])/5

• Average• reduce[[input1input2]->ifelse-value(input2=35)[input1+1][input1]][035104542835]• Countsnumberofoccurrencesof35

ParticipatorySimulation

• ParticipatorySimulationisthecreationofsimulationthatincorporateinputfromhumanagentsalongwithsimulatedagents

• InNetLogo,thisisimplementedviaHubNet• Forinstance,intheHubNetDiseasemodelyoucancontrolhumanstryingtoavoidcatchingadisease

CreatingaHubNetModel

• Basicarchitecture• Onecomputeristheserver(host)• Othercomputers/terminalscanconnecttothiscomputer(clients)

• Steps1. InitializeHubNet(HUBNET-RESET)2. Listentoclients(HUBNET-MESSAGE-WAITING?,HUBNET-FETCH-MESSAGE)3. Processmessages(HUBNET-ENTER-MESSAGE?,HUBNET-EXIT-MESSAGE?,

HUBNET-MESSAGE-SOURCE,HUBNET-MESSAGE-TAG,HUBNET-MESSAGE)

BasicTechniques

• Manyapplicationsmapaclienttoaturtle• Oftendonebysettingaturtles-ownvariable

• Needtoprogrammaticallysendmessagesfromhosttoclients• Addelementstoclientinterface• HUBNET-SEND

• Customizewhataclientsees• ClientPerspectivesandClientOverrides

SystemDynamicsModeling

• SDMrepresentscomplexsystemsusingnumericalstatesoftheworldcalledstocksandchangesinthosestatescalledflows

• InNetLogothereisatoolcalledSystemsDynamicsModeler,whichissimilartoothertoolkits,e.g.,STELLA

• SDMcanbeusedincombinationwithABM

Wolf-SheepPredation(docked)

• ThismodelshowshowyoucancompareABMandSDMmodels

• ABMhasdiscreteunits,whileSDMtendstorepresentthingsincontinuousvariablessotheresultscanbedifferent

Tabonuco-YagrumoHybridModel

• ThismodelcontainsahybridofABMandSDM• TheABMcontrolsthelocationoftrees,whileSDMcontrolsthedeathandbirthoftrees

ExtensionsAPI

• TheextensionsAPIinNetLogogivesyoutheabilitytocreatenewcommandsfortheNetLogolanguage

• OftenthisisdonetogiveNetLogoaccesstoexternalsoftwarepackages

• e.g.,GISandNetworktools

GISExtension(gis)

• Givesyoutheabilitytoread,writeandinteractwithGISdatainNetLogo• gis:load-dataset• gis:envelope,gis:envelope-union-of,gis:intersecting

• gis:feature-list-of• gis:property-value• gis:draw,gis:set-drawing-color

NetworkExtension(nw)

• Givesyoutheabilitytogeneratestandardnetworksandcalculatestatisticsaboutthem• nw:generate-preferential-attachment• nw:generate-random• nw:betweenness-centrality

LevelSpaceExtension(ls)

• TheLevelSpaceExtensiongivesyouthepossibilitytocallothermodelsfromyourmodel• ls:reset• ls:create-models• ls:ask• ls:models

TheControllingAPIandtheMathematicaLink

• NetLogocanbeinvokedbyanotherprogramifthatprogramisrunningontheJavaVirtualMachine

• ThismeansyoucancallNetLogocodefromJava,Scala,Clojure,Groovy,JRuby,Jython,etc.

• TheMathematicaLinkoperatessimilarlyallowingyoutocallNetLogocodefrominsideMathematica

FutureofABM

AutomaticGenerationofAgentRules

• Moreworkneedstobedoneabouthowtocreaterulesfromdatasourcesautomatically

• Theserulesalsoneedtobevalidated• CausalStateModelingisoneexampleofthis• Newsourcesofdata:bigdata,administrativedata,naturallanguagedata,socialdata,appdata

ImprovedMethodsofValidationandCalibration

• Weneedrigorousguidelinestofollowtoshowthatourmodelshavebeenvalidatedappropriately

• A“statistics-like”suiteoftests• MaketoolslikeBehaviorSearcheasiertousesothatuserscaneasilycalibratemodels

ModelsUsingStreamingData

• Ifwecanbuildmodelsautomatically,andcontinuouslyvalidatethemthenwecouldconstructamodelthatwascontinuallyupdatingonthebasisofstreamingdata

• Wecouldthenusethistosupportdecision-makinginrealtime

Unit9Overview

• BigData+ABM• DesignGuidelinesofABM

• OtherUsesofABM• AdvancedProgrammingConstructs

• ParticipatorySimulation

• SystemDynamicsModeling

• Extensions• FutureofABM• Unit9Slides• Unit9Test