Granular Computing and Decision-Making: Interactive and Iterative Approaches

369 Pages·2015·10.583 MB·English

by  Witold Pedrycz, Shyi-Ming Chen (eds.)

Studies in Big Data 10
Witold Pedrycz
Shyi-Ming Chen Editors
Granular
Computing
and Decision-
Making
Interactive and Iterative Approaches
Studies in Big Data
Volume 10
Serieseditor
JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland
e-mail:[email protected]
AboutthisSeries
Theseries“StudiesinBigData”(SBD)publishesnewdevelopmentsandadvances
in the variousareas of Big Data- quickly and with a high quality. The intent is to
coverthetheory,research,development,andapplicationsofBigData,asembedded
inthefieldsofengineering,computerscience,physics,economicsandlifesciences.
The booksof the seriesrefer to the analysisand understandingof large,complex,
and/ordistributeddatasetsgeneratedfromrecentdigitalsourcescomingfromsen-
sors or other physical instruments as well as simulations, crowd sourcing, social
networksor other internet transactions, such as emails or video click streams and
other.Theseriescontainsmonographs,lecturenotesandeditedvolumesinBigData
spanningtheareasofcomputationalintelligenceincl.neuralnetworks,evolutionary
computation,softcomputing,fuzzysystems, as wellas artificialintelligence,data
mining, modern statistics and Operationsresearch, as well as self-organizingsys-
tems. Of particular value to both the contributorsand the readership are the short
publicationtimeframeandtheworld-widedistribution,whichenablebothwideand
rapiddisseminationofresearchoutput.
Moreinformationaboutthisseriesathttp://www.springer.com/series/11970
·
Witold Pedrycz Shyi-Ming Chen
Editors
Granular Computing
and Decision-Making
Interactive and Iterative Approaches
ABC
Editors
WitoldPedrycz Shyi-MingChen
DepartmentofElectricalandComputer DepartmentofComputerScienceand
Engineering InformationEngineering
UniversityofAlberta NationalTaiwanUniversityof
Edmonton,Alberta ScienceandTechnology
Canada Taipei
Taiwan
ISSN2197-6503 ISSN2197-6511 (electronic)
StudiesinBigData
ISBN978-3-319-16828-9 ISBN978-3-319-16829-6 (eBook)
DOI10.1007/978-3-319-16829-6
LibraryofCongressControlNumber:2015937370
SpringerChamHeidelbergNewYorkDordrechtLondon
(cid:2)c SpringerInternationalPublishingSwitzerland2015
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Preface
Fuzzy decision-making - including its underlying methodology, plethora of
algorithmicdevelopmentsandarichanddiversifiedslewofapplicationstudiesform
acornerstoneoffuzzysets.Inspiteoftheongoingresearchandalonglistofaccom-
plishments, there are still a numberof open and intriguingissues worth pursuing.
Inparticular,thereisagenuineneedtocometogripastothedominantaspectsof
themethodologyandresultingtechnologyalongwiththeirsystematicandcoherent
usage.
Theoverarchingthemeofthiseditedvolumedwellsuponthetwokeyfeaturesof
decisionmakingprocessesthatbecomequitecommonlypresentthroughoutnumer-
ousstudiesandapplications,anddealwithinteractiveanditerativenatureofthese
processes.Decision-makingisinherentlyinteractive.Fuzzysetshelprealizehuman-
machinecommunicationinanefficientwaybyfacilitatingatwo-wayinteractionin
afriendlyandtransparentmanner.Thisinteractioniscarriedoutatasuitablelevel
ofinformationgranularitynotconfiningtheusertotheuseofprecisenumericdata.
Subsequently any decision support system releases non-numeric findings, which
becomehelpfultocomprehendtheresultsandassesspotentialimpactofdecisions
beingmade.Human-centricinteractionisofparamountrelevanceasaleadingguid-
ing design principle of decision support systems. The facet of interaction comes
handin handwithiterativeprocessesofdecision-making.Resultingrecommenda-
tionsissued bythe system can beassessed andmay triggeran iterativescheme of
gatheringadditionalevidence,refininggoalsandconstraints,assessingthefindings
and,ifnecessary,proceedingwiththenextiterativeloop.Forinstance,aniterative
natureofdecision-makingbecomesapparentinconsensusbuildingwherea series
ofiterations(sessions)amongtheparticipantsiscommonlyobserved.
The facets of interactivityand the iterativeway in which decisionsare formed,
giverisetothetitleofthisvolume-I2FuzzyDecisionMaking,forbrief.Weareof
opinionthateventhoughtheseaspectsofthedecisionprocessesarequitecommon
and vividly present in numerous studies, they have not been fully and systemati-
callyexploitedandsupportedbythetechnologyoffuzzysets.Theobjectiveofthis
volumeistorevisittheunderlyingparadigmandpracticeoffuzzydecision-making
emphasizingpivotalfacetsoftheirrealizationinaninteractivityanditerativeway.
VI Preface
The ultimate objectives of the proposed edited volume is to provide the reader
with an updated, in-depth material on the conceptually appealing and practically
soundmethodologyandpracticeofI2FuzzyDecisionMaking.
The book engagesa wealth of methods of fuzzy sets, bringsnew concepts, ar-
chitecturesand practiceof fuzzydecision making.The chapterscovera wealth of
ideas,algorithms,andapplications.Thegrowingroleofinformationgranules,Gran-
ularComputingandComputingwithWords(CWW)aswellastype-2fuzzysetsis
fullyreflectedinthebookwhereanumberofcontributionsaredirectlydevotedto
thissubjecteitherbyofferingsomemethodologicalinsightsorpresentinginterest-
ingapplications.Thereisagroupofchaptersdealingsequentialdecision-making,
consensusbuildingandgroupofdecisionmaking.Representativeapplicationstud-
iesincludingairquality,economics,librarymanagement,andimageprocessingare
a testimony of the leading role of the technology of fuzzy sets and information
granules.
Given the leading theme of this undertaking, the book is aimed at a broad au-
dience of researchersand practitioners. Owing to the nature of the material being
coveredand a way the main threadshave been organized,the volume will appeal
tothewell-establishedcommunitiesincludingthoseactiveinvariousdisciplinesin
whichdecision-makingprocessesplayapivotalroleandserveasavehicletopro-
duce solutions to existing problems. Those involved in operations research, man-
agement,variousbranchesofengineering,socialsciences,logistics,andeconomics
willbenefitfromtheexposuretothesubjectmatter.
In virtue of the way in which the edited volume has been arranged, this book
may serve as a useful and timely reference material for graduate students and se-
niorundergraduatestudentsincoursessuchasthoseondecision-making,Computa-
tionalIntelligence,operationsresearch,patternrecognition,risk management,and
knowledge-basedsystems.
Wewouldliketotakethisopportunitytoexpressourdeepthankstothecontrib-
utorstothevolumeforsharingresultsoftheiradvancedandoriginalresearchand
delivering their views at the rapidly expanding areas of fundamental and applied
research.Thereviewersdeserveourthanksfortheirconstructiveandtimelyinput.
We greatly appreciate a continuous support and encouragement coming from the
Editor-in-Chief,ProfessorJanuszKacprzykwhoseleadershipandvisionmakethis
bookseriesauniquevehicletodisseminatethemostrecent,highlyrelevantandfar-
fetchingpublicationsinComputationalIntelligenceanddecision-makingandtheir
variousapplications.Theeditorialstaff atSpringerhasdonea meticulousjoband
workingwiththemwasapleasantexperience.
Wehopethatthereaderswillfindthisvolumeofgenuineinterestandtheinnova-
tiveideasputforwardinthisvolumewillbecomeinstrumentalinfosteringprogress
inresearch,education,andnumerouspracticalendeavorsinthisexcitingdomain.
WitoldPedrycz
Shyi-MingChen
Contents
GranularityHelpsExplainSeeminglyIrrationalFeaturesofHuman
DecisionMaking ............................................... 1
JoeLorkowski,VladikKreinovich
AComprehensiveGranularModelforDecisionMakingwithComplex
Data ......................................................... 33
YingXie,TomJohnsten,VijayV.Raghavan,RyanG.Benton,WilliamBush
GranularityinEconomic DecisionMaking:An Interdisciplinary
Review ....................................................... 47
Shu-HengChen,Ye-RongDu
DecisionMakers’ OpinionsChangingAttitude-DrivenConsensus
ModelunderLinguisticEnvironmentandItsApplicationinDynamic
MAGDMProblems............................................. 73
BapiDutta,DebashreeGuha
Using Computing with Words for Managing Non-cooperative
BehaviorsinLargeScaleGroupDecisionMaking ................... 97
FranciscoJoséQuesada,IvánPalomares,LuisMartínez
AType-2FuzzyLogicApproachforMulti-CriteriaGroupDecision
Making....................................................... 123
SyibrahNaim,HaniHagras
Multi-criteriaInfluenceDiagrams–AToolfortheSequentialGroup
RiskAssessment ............................................... 165
AleksandarJanjic´,MiomirStankovic´,LazarVelimirovic´
ConsensusModelingunderFuzziness–ADynamicApproachwith
RandomIterativeSteps ......................................... 195
PasiLuukka,MikaelCollan,MarioFedrizzi
VIII Contents
DecisionMaking-InteractiveandInteractiveApproaches ............. 219
WeixiaLi,ChengyiZhang
CollaborativeDecisionMakingbyEnsembleRuleBasedClassification
Systems....................................................... 245
HanLiu,AlexanderGegov
A GDM Method Based on Granular Computing for Academic
LibraryManagement ........................................... 265
Francisco Javier Cabrerizo, Raquel Ureña,
JuanAntonioMorente-Molinera,EnriqueHerrera-Viedma
Spatial-Taxon Information Granules as Used in Iterative
Fuzzy-Decision-MakingforImageSegmentation .................... 285
LaurenBarghout
Group Decision Making in Fuzzy Environment – An Iterative
ProcedureBasedonGroupDynamics ............................. 319
MahimaGupta
FuzzyOptimizationinDecisionMakingofAirQualityManagement ... 341
Wang-KunChen,Yu-TingChen
AuthorIndex ..................................................... 365
SubjectIndex ..................................................... 367
Granularity Helps Explain Seemingly Irrational
Features of Human Decision Making
JoeLorkowskiandVladikKreinovich
Abstract.Startingfromwell-knownstudiesbyKahmenanandTversky,researchers
havefoundmanyexampleswhenourdecisionmakingseemstobeirrational.Inthis
chapter, we show that this seemingly irrationaldecision making can be explained
ifwetakeintoaccountthathumanabilitiestoprocessinformationarelimited;asa
result,insteadoftheexactvaluesofdifferentquantities,weoperatewithgranules
that contain these values. On several examples, we show that optimization under
suchgranularityrestrictionindeedleadstoobservedhumandecisionmaking.Thus,
granularityhelpsexplainseeminglyirrationalhumandecisionmaking.
Keywords: Decisionmaking,Granularity,Seeminglyirrationalbehavior.
1 Seemingly Irrational HumanDecisionMaking:Formulation
ofthe Problem
In the Ideal World, People Should make Perfect Decisions. In many real-life
situations, we know what is best for us, and we know the exact consequences of
eachofouractions.Inthiscase,arationalpersonshouldselectanactionthatleads
tothebestpossibleoutcome.
This assumptionunderliesbasic (idealized)economicmodels:in these models,
our decision making may hurt others but every person is interested in selecting a
decisionwhichisthebestforhim/herself.
IntheRealWorld,People’sDecisionsarenotPerfect.Intheperfectworld,peo-
ple should make perfect decisions. It is well known, however, that our world is
notperfect, and that manypeople make decisionswhich are not in their own best
J.Lorkowski·V.Kreinovich
DepartmentofComputerScience,UniversityofTexasatElPaso,500W.University,ElPaso,
Texas79968,USA
e-mail:[email protected], [email protected]
(cid:2)c SpringerInternationalPublishingSwitzerland2015 1
W.PedryczandS.-M.Chen(eds.),GranularComputingandDecision-Making
StudiesinBigData10,DOI:10.1007/978-3-319-16829-6_1