Innovative Approaches in Agent-Based Modelling and Business Intelligence PDF
Preview Innovative Approaches in Agent-Based Modelling and Business Intelligence
Agent-Based Social Systems 12 Setsuya Kurahashi · Hiroshi Takahashi Editors Innovative Approaches in Agent- Based Modelling and Business Intelligence Agent-Based Social Systems Volume 12 EditorinChief HiroshiDeguchi,Yokohama,Japan SeriesEditors Shu-HengChen,Taipei,Taiwan,ROC ClaudioCioffi-Revilla,Fairfax,USA NigelGilbert,Guildford,UK HajimeKita,Kyoto,Japan TakaoTerano,Yokohama,Japan KyoichiKijima,Tokyo,Japan SetsuyaKurahashi,Tokyo,Japan ManabuIchikawa,Saitama,Japan ShingoTakahashi,Tokyo,Japan MotonariTanabu,Yokohama,Japan Aki-HiroSato,Kyoto,Japan This series is intended to further the creation of the science of agent-based social systems, a field that is establishing itself as a transdisciplinary and cross-cultural science. The series will cover a broad spectrum of sciences, such as social sys- tems theory, sociology, business administration, management information science, organizationscience,computationalmathematicalorganizationtheory,economics, evolutionary economics, international political science, jurisprudence, policy sci- ence, socioinformation studies, cognitive science, artificial intelligence, complex adaptivesystemstheory,philosophyofscience,andotherrelateddisciplines. The series will provide a systematic study of the various new cross-cultural arenas of the human sciences. Such an approach has been successfully tried several times in the history of the modern science of humanities and systems and has helped to create such important conceptual frameworks and theories as cybernetics, synergetics, general systems theory, cognitive science, and complex adaptivesystems. Wewanttocreateaconceptualframeworkanddesigntheoryforsocioeconomic systemsofthetwenty-firstcenturyinacross-culturalandtransdisciplinarycontext. Forthispurposeweplantotakeanagent-basedapproach.Developedoverthelast decade, agent-based modeling is a new trend within the social sciences and is a child of the modern sciences of humanities and systems. In this series the term “agent-based” is used across a broad spectrum that includes not only the classical usage of the normative and rational agent but also an interpretive and subjective agent. We seek the antinomy of the macro and micro, subjective and rational, functional and structural,bottom-up and top-down, global and local, and structure and agency within the social sciences. Agent-based modeling includes both sides oftheseopposites.“Agent”isourgroundingformodeling;simulation,theory,and realworldgroundingarealsorequired. As an approach, agent-based simulation is an important tool for the new experimentalfieldsofthesocialsciences;itcanbeusedtoprovideexplanationsand decision support for real-world problems, and its theories include both conceptual andmathematicalones.Aconceptualapproachisvitalforcreatingnewframeworks of the worldview, and the mathematical approach is essential to clarify the logical structureofanynewframeworkormodel.Explorationofseveraldifferentwaysof real-world grounding is required for this approach. Other issues to be considered intheseriesincludethesystemsdesignofthiscentury’sglobalandlocalsocioeco- nomicsystems. Moreinformationaboutthisseriesathttp://www.springer.com/series/7188 Setsuya Kurahashi • Hiroshi Takahashi Editors Innovative Approaches in Agent-Based Modelling and Business Intelligence 123 Editors SetsuyaKurahashi HiroshiTakahashi GraduateSchoolofBusinessSciences KeioUniversity UniversityofTsukuba Yokohama,Kanagawa,Japan Tokyo,Tokyo,Japan ISSN1861-0803 Agent-BasedSocialSystems ISBN978-981-13-1848-1 ISBN978-981-13-1849-8 (eBook) https://doi.org/10.1007/978-981-13-1849-8 LibraryofCongressControlNumber:2018963745 ©SpringerNatureSingaporePteLtd.2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface The purpose of this book is to thoroughly prepare the reader at an intermediate level for research in social science, organization studies, economics, finance, marketing science, and business science as complex adaptive systems. Those who are not familiar with a computational research approach may see the advantages of social simulation studies and business intelligence, and experienced modelers mayalsofindvariousinstructiveexamplesusingagent-basedmodelingandbusiness intelligence approaches to inspire their own work. In addition, the book discusses cutting-edgetechniquesforcomplexadaptivesystemsthroughtheirapplications. Business science studies so far have focused only on data science and analyses ofbusinessproblems.However,thesestudiestoenhancethecapabilitiesofconven- tionaltechniquesinthefieldshavenotbeeninvestigatedadequately.Theemphasis in this book is managing the issues of societies, firms, and organizations for achieving profit on interaction with agent-based modeling, human- and computer- mixed systems, and business intelligence approaches, as such a focus is also fundamentalforcomplexbutboundedrationalbusinessenvironments. Appropriate for a diverse readership, there are multiple ways to read this book depending on readers’ interests in the areas of application and on their level of technicalskills.Researchersfamiliarwithfieldssuchassocialscienceandbusiness sciencestudiesmaycomparetheideasexpressedhereusingsimulationmodelswith empiricalstudies. Innovative Approaches in Agent-Based Modelling and Business Intelligence encourages readers inspired by intensive research works by leading authors in the field to join with other disciplines and extend the scope of the book with their own unique contributions. Agent-based modeling and business intelligence with the latest results in this book allow readers who are researchers, students, and professionals to resolve their problems through the common challenges posed by computational social and business science researchers involved in both areas in ordertocreateavaluablesynergy. Thisbookcontains19chapters.Thefollowingaretheirsynopses.TakaoTerano discusses the basic principles and key ideas of EBMS which is a just started new fieldofbothscientificandpracticalactivitiesinhisChap.1. v vi Preface KotaroOhoriandHirokazuAnaipresentanovelresearchproject,whichtheycall “social mathematics,” to resolve social issues based on mathematical and artificial intelligencetechnologiesintheirChap.2. Chathura Rajapakse, Lakshika Amarasinghe, and Kushan Ratnayake present the details of an agent-based simulation model developed to study the impact of seepage behavior, which means the smaller vehicles moving forward through the gapsbetweenlargervehicleswithoutfollowingthelanesinthetrafficcongestionin theirChap.3. HiroshiTakahashidiscussestheinfluenceofinformationtechnologyonbusiness andfinance.Healsolookstoofferanoverviewofthekindofresearchagent-based modelsareenablinginhisChap.4. Takashi Ishikawa investigates the mechanism of coevolving networks using a generalizedadaptivevotermodelbasedonrelatedworkandthehomophilyprinciple which is known as a driving mechanism to form community structure in social networksinhisChap.5. YokoIshinoproposesanewmethodforobtaininganappropriatestructurefora BayesiannetworkbyusingsensitivityanalysisinastepwisefashioninherChap.6. Hiroaki Jotaki, Yasuo Yamashita, Satoru Takahashi, and Hiroshi Takahashi analyzestheinfluenceoftextinformationoncreditmarketsinJapanintheirChap.7. Itfocusesonheadlinenews,asourceofinformationthathasimmediateinfluenceon themoneymarketandalsowhichisregardedasanimportantsourceofinformation whenmakinginvestmentdecisions. Yasuo Kadono tries to develop an integrated approach of data obtained from issue-orientedlarge-scalefact-findingsurveys,statisticalanalysesbasedondynamic modeling,andsimulationsinhisChap.8. Hajime Kita shows the experience of 20-year study of the artificial market and discussesitsfutureinhisChap.9. Masaaki Kunigami introduces a new formulation called the Doubly Structural Network(DSN)Modelandshowsitsapplicationsinsocioeconomicsandeducation inhisChap.10. SetsuyaKurahashilooksbackatthehistoryofscienceinordertointroduceone method so that ABS can develop to more reliable social science in his Chap.11. It also overviews the validity of modeling, ABM as an inductive inference and deductiveinference. ZhenxiChenandThomasLuxapplythesimulatedmethodofmomentestimator proposedbyChenandLuxtoinvestigatetheherdingbehaviorintheChinesestock marketusinghigh-frequencydataintheirChap.12. AkiraOta,GadeaUriel,HiroshiTakahashi,andToshiyukiKanedadiscussfactors ofthepedestrianflowsintwodifferentundergroundmallsbyconductingmultiple regressionanalysiswithvisibilitymeasuresbasedonspacesyntaxtheoryandstore proximitymeasuresintheirChap.13. Makoto Sonohara, Kohei Sakai, Masakazu Takahshi, and Toshiyuki Kaneda focus on tourists’ evacuation behavior who don’t have enough knowledge of evacuationsitesandroutesintheirChap.14.Theirstudyshowsanagentmodeling techniqueusingasamplingsurveyoftheweb-basedquestionnaireconsideringthe Preface vii informationbehaviorsandtheearthquakeexperiencesandatouristevacuationagent modelusingthistechnique. Shingo Takahashi proposes “virtual grounding” as a grounding method for constructingvalidfacsimilemodelswhererealdataforbehavioralmodelparameter identificationarenotavailableinhisChap.15. Toru B. Takahashi proposes a problem-solving support agent that interactively supportshumanproblemsolvingactivitiesinhisChap.16.Priortothedevelopment oftheproblem-solvingsupportagent,heorganizedtheproblemsolvingprocessand researchedthetypesofmistakesinvolvedintheprocess. Wander Jager, Geeske Scholz, Ren´e Mellema, and Setsuya Kurahashi discuss theirexperienceswiththeEnergyTransitionGame(ETG)inGroningen,Tokyo,and Osnabrück,allineducationalsettingsintheirChap.17.TheETGisanagent-based gameinwhichrolesthatcanbeplayedareenergycompaniesandpoliticalparties. Auniqueaspectistheinclusionofanartificialpopulationofsimulatedpeople. ChaoYangproposesaco-evolutionaryopinionmodelofthesocialnetworkbased onboundedconfidence,referencerange,andinteractiveinfluenceinherChap.18. Finally,TakashiYamadareviewstheactivitiesofProf.DrTakaoTerano’slabo- ratoryatTokyoInstituteofTechnologyandbrieflyintroducesseveralrepresentative papersespeciallyinsocialsimulationliteratureinhisChap.19. Acknowledgements Astheeditors,wewouldliketothankProf.TakaoTerano.Hehasbeena leadingresearcherinagent-basedmodelingfieldsforseveraldecades.Finally,wewishtoexpress ourgratitudetoalltheauthors. Tokyo,Japan SetsuyaKurahashi Yokohama,Japan HiroshiTakahashi June2018 Contents 1 Gallery for Evolutionary Computation and Artificial IntelligenceResearches:WhereDoWeComefromandWhere ShallWeGo ................................................................. 1 TakaoTerano 2 MathematicalTechnologiesandArtificialIntelligenceToward Human-CentricInnovation................................................ 9 KotaroOhoriandHirokazuAnai 3 Study on the Social Perspectives of Traffic Congestion inSriLankaThroughAgent-BasedModelingandSimulation: LessonsLearnedandFutureProspects.................................. 23 ChathuraRajapakse,LakshikaAmarasinghe,andKushanRatnayake 4 InformationTechnologyandFinance .................................... 43 HiroshiTakahashi 5 TwoPhaseTransitionsintheAdaptiveVoterModelBased ontheHomophilyPrinciple ............................................... 53 TakashiIshikawa 6 SensitivityAnalysisinaBayesianNetworkforModelinganAgent .. 65 YokoIshino 7 AnalyzingtheInfluenceofHeadlineNewsonCreditMarkets inJapan...................................................................... 77 Hiroaki Jotaki, Yasuo Yamashita, Satoru Takahashi, andHiroshiTakahashi 8 ConsiderationonanIntegratedApproachtoSolvingIndustrial IssuesThroughSurveys,Statistics,andSimulations ................... 95 YasuoKadono 9 U-Mart:20-YearExperienceofanArtificialMarketStudy........... 111 HajimeKita ix x Contents 10 What Do Agents Recognize? From Social Dynamics toEducationalExperiments............................................... 123 MasaakiKunigami 11 ModelPredictionandInverseSimulation ............................... 139 SetsuyaKurahashi 12 IdentificationofHigh-Frequency HerdingBehaviorinthe ChineseStockMarket:AnAgent-BasedApproach .................... 157 ZhenxiChenandThomasLux 13 ADataAnalysisStudyonFactorsofthePedestrianFlows in Two Different Underground Malls Using Space Syntax Measures:CaseComparisonsinNagoya,Japan........................ 173 AkiraOta,GadeaUriel,HiroshiTakahashi,andToshiyukiKaneda 14 AStudyonAgentModelingofTouristEvacuationBehaviors inanEarthquake:ACaseStudyofanEvacuationSimulation ofHimejiCastle............................................................. 189 Makoto Sonohara, Kohei Sakai, Masakazu Takahshi, andToshiyukiKaneda 15 VirtualGroundingforAgent-BasedModelinginIncomplete DataSituation............................................................... 205 ShingoTakahashi 16 AnalysisofProblem-SolvingProcesses................................... 221 ToruB.Takahashi 17 TheEnergyTransitionGame:ExperiencesandWaysForward...... 237 WanderJager,GeeskeScholz,Ren´eMellema,andSetsuyaKurahashi 18 A Coevolutionary Opinion Model Based on Bounded Confidence,ReferenceRange,andInteractiveInfluencein SocialNetwork.............................................................. 253 ChaoYang 19 Prof.Dr.TakaoTeranoasaBrilliantEducator......................... 269 TakashiYamada
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