Modeling preferences, strategic reasoning and collaboration in
agent-mediated electronic markets
Citation for published version (APA):
Robu, V. (2009). Modeling preferences, strategic reasoning and collaboration in agent-mediated electronic
markets. Technische Universiteit Eindhoven. https://doi.org/10.6100/IR642817
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10.6100/IR642817
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Graduationcommittee:
Prof.dr.A.G.L.Romme(chair)
Prof.dr.HanLaPoutr´e(promotor)
Corereadingcommittee:
Prof.dr.NicholasR.Jennings(Univ.ofSouthampton,UK)
Prof.dr.DavidC.Parkes(HarvardUniversity,US)
Prof.dr.TonG.deKok(TechnischeUniversiteitEindhoven)
Prof.dr.EmileH.L.Aarts(TUEindhoven&PhilipsResearch)
Additionalmembers:
Prof.dr.CatholijnJonker(TechnicalUniversityofDelft,NL)
Prof.dr.CeesWitteveen(TechnicalUniversityofDelft,NL)
SIKSDissertationSeriesNo.2009-19
TheresearchreportedinthisthesishasbeencarriedoutatCentrumvoorWiskundeen
Informatica(CWI),AmsterdamundertheauspicesofSIKS,theDutchResearchSchoolfor
InformationandKnowledgeSystems.
ISBN:978-90-386-1816-6
AcataloguerecordisavailablefromtheEindhovenUniversityofTechnologyLibrary
PublishedbyTechnischeUniversiteitEindhoven,2009
PrintedondemandthroughLulu.com
Copyright c 2009byValentinRobu.Allrightsreserved.
Coverillustrations:
Front:Afractalmodelofthebrain-courtesyofSvenGeier.
Agent-Mediated Electronic Markets
PROEFSCHRIFT
terverkrijgingvandegraadvandoctoraande
TechnischeUniversiteitEindhoven,opgezagvande
rectormagnicus,prof.dr.ir.C.J.vanDuijn,voor
eencommissieaangewezendoorhetCollegevoor
Promotiesinhetopenbaarteverdedigen
opdonderdag2juli2009om16.00uur
door
ValentinRobu
Firstandforemost,I'mdeeplygratefultomyadvisor,HanLaPoutr´eforhavingmeashis
PhDstudentandforhispatientguidancealltheseyears. Ourdiscussionsprovidedmewith
lotsofinsightsandmotivation(attimeswhenitwaslacking),andhadadeningcontribution
inshapingmyresearchinterestsandcareer. IhadagreatdealtolearnfromHan,notonly
aboutwhatitmeanstogetsolidresearchresults,butalsoaboutacademiclifeingeneral.
Also,aspecialthankyoutoCatholijnJonker,who,asmy Masterthesissupervisor
attheFreeUniversityAmsterdamintroducedmetotheworldofmulti-agentsystems,and
toautomatednegotiationinparticular.Herenthusiasmanddedicatedguidancewerehighly
motivatingindeterminingmetopursueaPhDaftergraduation.
Iamalso gratefultoNickJenningsandDavid Parkes,my externalthesiscommitteee
membersforthecarefulreadingofmydissertationandtheirsuggestions,whichmadethe
presentation ofthisthesis much stronger. Furthermore, Iwouldliketothank professors
Ton de Kok, Emile Aarts andCees Witteveenforreading my thesis andtheirinsightful
comments. I alsoacknowledgeprofessorIoanAlfredLetia(TUCluj-Napoca, Romania),
whorstrecommendedmetopursueamasterdegreeintheNetherlands.
ThisthesiswouldnotbewhatitiswithouttheexcellentcollaboratorsandcolleaguesI
hadovertheyears.Iwillbeginbymyco-authors(and,attimes,co-advisers)atCWI:Koye
Somefun,PieterJan'tHoen,SanderBohte,HanNoot,PeterBosmanandEnricoGerding
(whomIhadthepleasureofmeetingagaininSouthampton).Thebreadthoftheirresearch
interestsandtheideasweexchanged(someofwhichmaterializedinpublications)werean
assettomyresearchcareer.
Also,manythankstoprof.CeesWitteveenandthegroupfromDelft:MathijsdeWeerdt,
TomasKlos(whowasalsomyroom-mateatCWIforayear),RenzeSteenhuizen,Yingqian
Zhang,SiccoVerwerandTamasMahr.Theinvitationtolecturethere(part-time)forayear
wasagreatlearningexperienceforme,sinceIgottoseersthandthechallengesinvolved
inteachingsideofacademiclife. AlsoKoenHindriksandDmytroTykhonovfromDelft
providedmanyinterestingdiscussions.
AnotherdeningmomentinmyPhDresearchwasattendanceoftheSantaFeComplex
SystemsSummerSchool.Iamindebttoallteachersandfellowparticipants,butin
partic-ulartoHarryHalpin(EdinburghandMIT)andHanaShepherd(Princeton). Theexcellent
collaborationforgedwiththemledtoaveryinterestingandsuccesfulllineofresearchon
collaborativetagging,whichresultedinachapterofthisthesis.Furthermore,Ihadthe
andhisenthusiasmandinsightsmotivatedmetolearnalotaboutwebscience.
Iwouldalsoliketothank TakayukiIto(Nagoya University andMIT),Minjie Zhang
(WallongongUniversity,Australia),ShaheenFatima(Liverpool),TokuroMatsuo(Nagoya)
andHiromitsuHattori(KyotoUniv.-whoalsovisitedusatCWI).Ihadthepleasureof
co-organisingwiththemasuccesfulworkshoponcomplexautomatednegotiations(thisyear,
forthethirdyearrunning),aswellascoordinatingonanAAMASconferencetutorial.
IamalsogratefultothepeoplefromAlmendeBV,Rotterdam,whoofferedmea
year-longinternshipduringmymasterthesisdays, ofwhichImentionhere: PeetvanTooren,
HansAbbink,TamasMahr,JPLarsenandStefanKroon. Besidesimprovingconsiderably
my programmingskills, thisinternshipgavemeagreatperspectiveonthe challenges
in-volvedindeployingagentsystemsinpractice,whichIwouldnevergetfromapurely
aca-demiccareer. ThelivelyargumentswithPeetalsotaughtmealotabouttheimportanceof
keepinganopenmindanddefendingthepracticalrelevanceofone'swork. Also,Iwould
liketothankWillem-JanvanSchijndel,fromourprojectpartnerVosLogistics,forhistime
inworkingwithustogetaconcreteapplicationfromourworkonauction-basedallocation.
DuringmytimeatCWI,Ihavebeenpriviligedtosupervisetwoexcellentmaster
stu-dents: Sandervander Putten(TechnologyManagement, Eindhoven)andLonnekeMous
(Econometrics, Univ. Rotterdam). Both hadexcellentresults(includingcumlaude
grad-uationsandprizesforbestthesisintheeldsintheNetherlands). Sanderhelpedusclose
the conceptualgapbetweenthe way AIresearchersandlogistic managerssee theworld.
TheworkwithLonnekeonusingpricedoptionstosolvetheexposureprobleminsequential
auctionsdenitelyledtooneofthemostinterestingdirectionsofworktoemergefromthis
thesis,onethatIdenitelyintendtopursefurtherinmyresearchwork.Moreover,Lonneke
isagreatpersontoworkwith,andIrememberfondlythetravelsthroughthenarrow(and
sunny)streetsofLisbonwithLonnekeandAnke.
WhichbringsmetooneofthemostimportantgroupsofpeopleImetduringmyPhD.
I'mrefering,ofcourse,totheothermembersofthegangoffourPhDstudentsofHan:
AnkeHutzenschreuter,IvanVermeulenandMengxiaoWu(ofwhich,inthatorder,therst
twowerekindenough tobemy paranimfem,while the lasttwowere myroom-matesat
CWIforthepast3years).Ourdiscussionscoveredeverythingfrombargainingmodelsand
distributedpatientschedulinginhospitalstofoodrecipesandthevirtuesofwheatbeer.Also,
ourculinaryexplorationsofthedelightsofAmsterdamwillbeveryfondlyremembered.
Also,I'dliketothankallmyfriendsinAmsterdam,startingwithmyRomanianfriends:
Tudor,Radu,Ghita,VladandLiviu,whohavebeenagreathelpandsupportwhenmoving
tothe Netherlands, andwerealwaysthereto shareabeerinCafe Uilenstede(andmove
furniture,anotherconstantinthelifeofaPhDstudent). Also,manythanks tomy many
otherDutchfriends:Benno,Abel,Richard,Leopold,Jan,Onno,RuggieroandJoost. You
showedme thatdoingaPhDinAmsterdamcanhaveitsfairshareofexcitement,besides
fromtheprofessionalexcitementofbrowsingthelatestAAMASconferenceproceedings.
Finally,my familywhohavehelped methroughthisallalong, especiallymymother
1 Introduction 1
1.1 Agentsandelectronicmarkets . . . 1
1.2 Negotiation(bargaining)vs.auctionprotocols . . . 2
1.3 Designingforstrategicbehaviour:marketmechanismsvs. individualagent strategies . . . 3
1.4 Modelingpreferencesandutilitiesinagent-mediatedmarketsettings . . . . 5
1.5 Emergenceofcollaborationandstructureinmulti-agentsystems . . . 6
1.6 Positioningofthecontributionsofthisthesis. . . 7
1.7 Modelingofcombinatorialpreferences(multi-issueormulti-item)in bilat-eralnegotiations. . . 8
1.7.1 Pareto-optimaloutcomesinmulti-issuenegotiation . . . 9
1.7.2 Modelingmulti-attributenegotiationwithincompletepreference in-formation . . . 11
1.7.3 Non-linearandcombinatorialpreferencesinnegotiation . . . 11
1.7.4 Modeling multi-itemnegotiationsoverk-additiveutilityfunctions usingutilitygraphs . . . 13
1.7.5 Individualpreferencesandsocialinuence . . . 14
1.7.6 Learningthe structureofutilitygraphsusedinmulti-item negotia-tionthroughcollaborativeltering . . . 15
1.8 Preferencesunderuncertaintyandbiddinginsequentialauctions . . . 16
1.8.1 Sequentialauctionsandtheexposureproblem. . . 16
1.8.2 Designingsequentialauctionstrategiesforrisk-aversebidders . . . 17
1.8.3 Optionsmechanismsinsequentialoptions . . . 18
1.8.4 Usingpricedoptionstosolvetheexposureproblem . . . 19
1.9 Applicationstotransportationlogistics . . . 20
1.9.1 Auction-basedallocationoftransportationloadsinmulti-party trans-portationlogistics. . . 20
1.10 Preferencesinsocialwebcommunitiesandonlinemarkets . . . 21
1.10.1 Thecomplexdynamicsofcollaborativetaggingsystems . . . 22
1.10.2 Anempiricalanalysisofsponsoredsearchmarkets . . . 23
1.11 Structureofthethesis . . . 23
1.12 Publicationsrelatedtoeachchapter . . . 26
I Modelingcombinatorialpreferencesinbilateralnegotiation
(bar-gaining) 31
2 AnAgent ArchitectureforCooperativeMulti-Attribute NegotiationWith
In-completePreferenceInformation 33
2.1 Introduction . . . 33
2.2 Themulti-attributenegotiationmodel . . . 34
2.2.1 BidUtilityDeterminationandPlanningComponent . . . 35
2.2.2 TheAttributePlanningComponent . . . 36
2.2.3 TheTargetEvaluationPlanningComponent . . . 37
2.2.4 EstimationofOpponent'sParametersComponent. . . 39
2.2.5 GuessCoefcientsComponent. . . 40
2.3 Implementation&Experimentalvalidation . . . 41
2.3.1 Experimentalset-up . . . 41
2.3.2 Anexamplenegotiationtrace . . . 42
2.3.3 Comparingtracesfromthesametestset . . . 45
2.3.4 Comparingresultsfromalltestsets . . . 48
2.3.5 Human-computerexperiments . . . 49
2.4 Discussion. . . 50
2.5 Conclusions . . . 51
3 ModelingComplexMulti-IssueNegotiationsUsingUtilityGraphs 53 3.1 Introduction . . . 53
3.2 Thenegotiationsetting . . . 57
3.2.1 NetUtilityfunctionsofBuyerandSeller . . . 57
3.2.2 Usinggainsfromtradeasefciencycriteria . . . 58
3.2.3 Outlineofthenegotiationsettingandprotocol. . . 58
3.2.4 Assumptionsaboutbuyerknowledge . . . 60
3.2.5 Top-levelnegotiationalgorithmusedbytheseller . . . 60
3.3 Decomposableutilityfunctionsandtheirgraphicalrepresentation . . . 61
3.3.1 Thek-additiveutilityform . . . 62
3.3.2 Usinggraphstomodelcomplexutilityfunctions . . . 63
3.4 Negotiationheuristicsbasedonutilitygraphs . . . 64
3.4.1 Selectingthebestcounteroffer. . . 65
3.4.2 UpdatingSub-utilityFunctions. . . 69
3.5 Constructingthestructureofutilitygraphsusingaggregatenegotiationdata 72 3.5.1 Collaborativeltering:briefintroduction . . . 72
3.5.2 Overviewofourlteringandnegotiationapproach . . . 73
3.5.3 Minimalsuper-graphforaclassofbuyers . . . 74
3.5.4 Extractinginformationfromconcludednegotiationdata . . . 75
3.5.5 Computingthesimilaritymatrices . . . 76
3.5.6 Buildingthesuper-graphofbuyerutilities . . . 77
3.5.7 MinimizationofexpectedlossinGainsfromTradeascut-offcriteria 78 3.6 ExperimentalAnalysis . . . 79
3.6.3 Set-upandresultsfordifferentgraphstructures . . . 83
3.6.4 Experimentalset-upandanalysisofthecollaborativelteringmodel 87 3.6.5 Resultsfromretrievalexperimentsusingcosine-basedvs. correla-tionbasedsimilarity . . . 89
3.6.6 Experimentalresultsforselectinggraphcut-offnumberofedgesin themaximalgraph . . . 91
3.7 Discussion. . . 94
3.7.1 Comparisontootherautomatednegotiation(bargaining)approaches 94 3.7.2 Relationtographicalutilitymodelsandpreferenceelicitation . . . 97
3.8 Conclusionsandfuturework . . . 98
II Preferencesunderuncertaintyandstrategicreasoningin sequen-tialauctions 103 4 Designingbiddingstrategiesinsequentialauctionsforriskaverseagents 105 4.1 Introduction . . . 105
4.1.1 Goalsandorganisationofthischapter . . . 107
4.2 ModelingUtilityFunctionsUnderRisk . . . 107
4.2.1 Theimportanceofriskaversionindecisionmaking:anexample . . 110
4.3 Biddinginsequentialauctionswithcomplementarities . . . 111
4.3.1 Optimalbiddingpolicyforsequential2ndprice(Vickrey)auctions. 113 4.3.2 Optimalbiddingpolicyforsequential1stpriceauctions: numerical solutions . . . 115
4.3.3 Biddingstrategyformultiplecopyauctionsequences . . . 117
4.4 Experimentalanalysis. . . 118
4.4.1 Experimentalhypotheses . . . 119
4.4.2 Experimentalsetup . . . 119
4.4.3 Experimentalresultsforone-typeitemauctions . . . 120
4.4.4 Resultsforoneitemanddifferentauctionlengths . . . 121
4.4.5 Settingwithdifferentitemtypesandmorecomplexpreferences . . 123
4.4.6 Multipleitemsetting:hypotheses . . . 125
4.4.7 Resultsfortwo-itemcase. . . 127
4.5 Conclusionsandfurtherwork. . . 127
5 UsingPricedOptionstoSolvetheExposureProbleminSequentialAuctions 129 5.1 Introduction . . . 129
5.1.1 Options:basicdenition . . . 130
5.1.2 Relatedwork . . . 131
5.1.3 Outlineandcontributionofourapproach . . . 132
5.2 Expectedprotforasequenceofnauctionsand1synergybuyer . . . 133
5.2.1 Protwithnuniquegoodswithoutoptions . . . 133
5.2.2 Protwithnuniquegoodswithoptions . . . 135
5.3.1 Whenagentsarebetteroffwithoptions . . . 137
5.3.2 Synergybuyer'sprot-maximizingbid . . . 145
5.4 Simulationofamarketwithasinglesynergybuyer . . . 151
5.4.1 Synergybuyer'sbidstrategy . . . 152
5.4.2 Experimentalresults:marketentryeffectforonesynergybuyer . . 155
5.5 Settingswithlongersequencesofauctionsandeffectofauctionorder . . . 158
5.6 Multiplesynergybuyers . . . 160
5.6.1 Twosynergybuyersinteractingindirectlythroughtheexerciseprice level. . . 161
5.6.2 Directsynergybuyercompetitioninthesamemarket . . . 162
5.6.3 Largersimulationwithrandomsynergybuyers'marketentry. . . . 163
5.7 Discussionandfurtherwork . . . 164
III Emergenceofcollaborationandsocialpreferences inweb com-munitiesandonlinemarkets 167 6 EmergenceofConsensusandSharedVocabulariesinCollaborativeTagging 169 6.1 Introduction . . . 169
6.1.1 TaggingversusTaxonomiesontheWeb . . . 170
6.1.2 OverviewofRelatedWork . . . 172
6.1.3 TheTripartiteStructureofTagging . . . 175
6.1.4 Organizationofthechapter. . . 177
6.2 DetectingPowerLawsinTags . . . 177
6.2.1 PowerLawDistributions:Denition. . . 177
6.2.2 EmpiricalResultsforPowerLawRegressionforIndividualSites. . 178
6.2.3 EmpiricalResults forPowerLaw Regression UsingRelative Fre-quencies. . . 180
6.3 TheDynamicsofTagDistributions. . . 181
6.3.1 Kullback-LeiblerDivergence:Denition . . . 181
6.3.2 ApplicationtoTagDynamics . . . 182
6.3.3 EmpiricalResultsforTagDynamics . . . 182
6.3.4 Examiningthedynamicsoftheentiretagdistribution . . . 184
6.4 ConstructingTagCorrelationGraphs . . . 185
6.4.1 Methodology . . . 185
6.4.2 Constructingthetagcorrelation(folksonomy)graphs . . . 186
6.5 Identifyingtagvocabulariesinfolksonomiesusingcommunitydetection al-gorithms . . . 189
6.5.1 Usingcommunitydetectionalgorithmstopartitiontaggraphs . . . 190
6.5.2 Edgelteringstep . . . 191
6.5.3 Normalizedvs.non-normalizededgeweights . . . 191
6.5.4 Thegraphpartitioningalgorithm. . . 192
6.5.5 Graphpartitioning:experimentalresults . . . 194
fromsearchenginequerydata . . . 196
6.6.1 Datasetandmethodologyemployed. . . 198
6.6.2 Discussionoftheresultsfromthequerylogdataandcomparison . 198 6.7 ConclusionsandFutureWork. . . 200
6.8 Acknowledgments . . . 201
7 TheComplexDynamicsofSponsoredSearchMarkets:AnEmpiricalStudy 203 7.1 Introduction . . . 203
7.1.1 Thedataset . . . 204
7.2 Complexsystemsanalysisappliedtothewebandeconomics . . . 204
7.2.1 Powerlaws:denition . . . 205
7.3 Inuenceofdisplayrankonclickingbehaviour . . . 206
7.3.1 Resultsondisplaypositionbiasandinterpretation . . . 207
7.4 Marketstructureattheadvertiserlevel . . . 209
7.4.1 Distributionofimpressionsvs. distributionofclicksforthetop ad-vertisers . . . 209
7.4.2 Distributionofmarketshareperdisplayrankposition. . . 210
7.5 Usingclickdatatoderivesearchtermrecommendations . . . 211
7.5.1 Derivingdistancesfromco-occurrenceinsponsoredclicklogs . . . 212
7.5.2 Constructingkeywordcorrelationgraphs . . . 212
7.5.3 Graphcorrelationgraphs:results. . . 213
7.5.4 Automaticidenticationofsetsofkeywords . . . 214
7.5.5 Discussionofgraphpartitioningresults . . . 215
7.6 Discussion. . . 216
7.6.1 Contributionofthechapter&relatedwork . . . 216
7.6.2 Futurework. . . 217
7.6.3 Acknowledgements. . . 217
IV Conclusions 219 8 Conclusionsandfurtherwork 221 8.1 Overviewoftheresearchcontributionsperchapter . . . 221
8.2 Furtherwork . . . 224
V Anindustrialapplicationcase 229
A APlatformforAuction-BasedAllocationofLoadsinTransportationLogistics 231
A.1 Introduction . . . 231
A.1.1 Themulti-partylogisticsdomain . . . 232
A.1.2 Companyprole . . . 232
A.1.3 Automatingmulti-partylogisticsusingagents . . . 233
A.1.4 Goalsofthiswork . . . 233
A.2 Overviewofthebusinesscaseandourplatform . . . 235
A.2.1 Generatingtransportationorders . . . 235
A.2.2 Computingpricesandcosts . . . 237
A.3 Auctionprotocolanddesignoftheauctioneeragent . . . 237
A.3.1 Auctionset-up . . . 238
A.3.2 Auctionsforloadswithashortleadtime . . . 238
A.3.3 Auctionsfororderswithalongertimehorizon . . . 239
A.3.4 Totalcapacityofloadstobegeneratedperday . . . 239
A.3.5 Auctioneeruserinterface . . . 240
A.4 Automatedbidders:descriptionanduserinterface . . . 241
A.5 Thecarrieragents:descriptionanduserinterfaces . . . 242
A.5.1 Transportationmodelandcarriercosts. . . 243
A.5.2 Penaltyforlatedeliveries. . . 244
A.5.3 Informationsuppliedaboutothercarriersduringthecompetition . . 244
A.5.4 Planningandbiddingdecisionsupportinterface. . . 245
A.6 Outlineofpreliminaryhumanbiddingresults . . . 246
A.7 Discussion. . . 247
Introduction
1.1 Agentsand electronicmarkets
Multi-agentsystemsare oneofthemost promisingnewtechnologiestoemergeinrecent
decades, atthecrossroadsbetweenseveraleldssuchas articialintelligence,distributed
systems,economicsandevensociology. Someauthors[16,231]haveoutlinedavision,in
whichmanyofthetasksperformedtodaybyhumansaredelegatedtointelligent,autonomous
andproactiveprograms,genericallycalledsoftwareagents. Asystemcomposedofseveral
suchagentsiscalledamulti-agentsystem(MAS).
Electronicmarketsrepresentkeycoordinationmechanismsinmulti-agentsystems.They
allowpartiestoefcientlyallocateresources,tasksandcapabilitiesinlargedistributed
sys-tems,composedofself-interestedagents. Therapidriseinelectroniccommerceand
mar-keting,logistics,distributednetworks(amongmanyothers)havemadethedevelopmentof
agenttechnologiescapableofautomatingsuchprocessesincreasinglyimportant.For
exam-ple,electroniccommercehaswitnessedanexponentialincreaseinthevalueofthegoodsand
servicessoldonlinejustinthepastfewyears. Itisnotjustthesaleofphysicalgoodsthat
hasgreatlyincreased,butalsothesaleofvirtualservices,suchasscreenattentionspace
fordisplayingadvertisingine-commerce,orkeywordhitsbysurfersusingsearchengines.
Suchsalesrequirefrequent,repeatedinteractions,whicharethetypeofprocessesthatare
likelytobenetmostfromautomationusingsoftwareagents.
Therearemanychallengesthat designersofagents actinginelectronicmarketsmust
face. Perhapsthemosteasilyrecognizedchallengeindesigningandusingsuchasystem,
isthelackofcentralizedcontrol. Agentsareautonomousactors,thattaketheirown
deci-sions,ratherthansimplyexecutingoperationsassignedtothembyanoutsideprocess(such
asobjectsorwebservicesdo).Furthermore,perhapsmoreimportantly,theyareoften
self-interestedactors,whosegoalsandobjectivesmaynotmatch. Forexample,inoptimizing
alloca-tionofloadsfromtheperspectiveofeachcompanymaybeverydifferentthantheoptimal
allocationfortheentiresystem. Inotherapplicationscenarios,suchasonlineadvertising,
agentsrepresentingdifferentcompaniesactivelycompeteforvirtualcommodities,suchas
consumerattentionspace.
AnimportantchallengeinMASisthepresenceofuncertainty,i.e. incompleteor
im-perfectinformation,bothregardingthemarketenvironment,thepreferences,strategiesand
behaviourofthe otheragents and,sometimes, evenuncertaintyinspecifyingtheagent's
own preferences. Furthermore, unlikeassumptions commonlymadein gametheory, the
agentsareboundedrationalactorsandoftenhavetomakedecisionsinlimitedtime,under
riskaversionorbasedonotherconstraintsimposedbytheirownersorthemarket
environ-ment.Moreover,theopposingagentsparticipatinginthesamemarketmayalsobebounded
rationalandevenact `irrationally, whichmakes modelingtheagent'sown optimalor
rationalbehaviourinsuchasettingevenharder.
Anotherimportantapproachinthestudyofagent-mediatedelectronicmarketsare the
so-calledcomplexsystemstechniques.Theaimofsuchapproachesistoexaminehoworder
and structurecanemerge ina large systemcomposed ofmanyautonomousentities(i.e.
agents),actingindependently,withoutanycentralcontrollertoprovidecoordination. The
recentsurgeofinterestinsystemssuchaswebcommunitiesandonlineelectronicmarkets,
wherestructureemergesoutofindividualagentdecisions,makessuchquestionsincreasingly
important.
1.2 Negotiation(bargaining)vs. auctionprotocols
Negotiation,verybroadlydened,istheprocessbywhichagroupofagentscommunicate
withoneanothertotrytoreachagreementonsomematterofcommoninterestc.f.[111,
189].Automatednegotiationhasbeenattheforefrontofresearchinterestsinthemulti-agent
researchcommunityeversincethebeginningoftheeld[129,189].
Oneofthemaindistinctionlinesbeingdrawninexistingliteratureisbetweenautomated
negotiation(bargaining)protocolsandauctionprotocols[111,147]. Bargainingisalways
adecentralizedprocessandistypically(thoughnotnecessarily)based onanalternating
offers-typeofprotocol. Someauthors[111,115,175](amongothers)arguethat
bargain-ingdoeshavesomeadvantagesoverauctions,especiallyinmultipleissuecases, inwhich
thereisincompleteinformationabouttheopponentpreferences(orevenuncertaintyabout
the agent'sownpreferences)andthespace ofpossibledealstobeexploredisverylarge.
Bargainingalsoallowsmoreexibilityinhowthenegotiationismodeled,aswellasa
de-greeofself-interestonthepartoftheagents. Somesources[87,115,175,179]evenargue
that,inelectroniccommerce,multi-issuenegotiationshouldbemodeled,atleastpartially,
asacooperativeprocess,becausesellershaveaninterestinmaintainingagoodrelationship
andthelong-termsatisfactionoftheirbuyers.
Auctions,ontheotherhand,followprotocolswithxedrules,thattypicallyrelyona
managementscience[190],butalsoarticialintelligenceandtheoreticalcomputerscience
[27,55,58,59,81,142,174,194].Theyhavebeenthemethodofchoiceforautomating
elec-tronicmarketplaces.
Inourwork,wehavelookedatbothmechanisms,fordifferentsettings.Ourinitialwork
inthetopicstartedondesigningefcientbilateralnegotiationmechanisms,rstforlinear
utilityfunctions(Chapter2ofthisthesis),thenforcomplex,interdependentutilityfunctions
(Chapter3). Wehavealso lookedatdesigningbiddingstrategiesforsequentialauctions,
insettingsnotpreviouslyconsideredinexistingliterature,suchasthecasewhensomeof
theagents areriskaverse(Chapter4), orwhenoptionsareauctionedinstead oftheitems
themselves(Chapter5).
Thisthesistakesanengineeringapproach,meaningthatweaimtoidentifyopen
prob-lems,andthenengineerandvalidatesolutionsforthem. Wedostudytowhatdegreethese
problemscanbeaddressedusingananalytical,mathematicalapproachinsofaraspossible.
However,manynegotiationandauctioningprocessesaretoocomplextobesolvedusinga
purelyanalyticalapproach,asisnormallythecaseforreal-worldproblems. Insuchcases,
experimentalvalidationisapromisingalternative,whichwasusedextensivelyinthisthesis.
Acommonthreadrunningthroughthe researchpresentedinthisthesisisthatwetake
theheuristicapproachtothedesignofbiddingagents. Thatis,we focusourattentionon
designingthestrategiesthatbiddingagentsusetobidornegotiateinagivenmarket
envi-ronment(usuallyonewidelyencounteredinpractice),notthemarketprotocolitself.Thisis
animportantdistinction,asexplainedinthenextsection.
1.3 Designingforstrategicbehaviour: marketmechanisms
vs. individual agent strategies
With the growth of interest in electronic markets, several research lines have emerged,
proposingdifferentapproachestomodelingstrategic,self-interestedbehaviourwhen
allo-catingresourcesortasksamongasetofagents.Oneofthemostpromisingsuchapproaches
iscomputationalmechanismdesign-ortobemoreprecise,thatpartofmechanismdesign
theorythatconcernsdesignofelectronicmarkets.
Mechanismdesigninitiallydevelopedasabranchofalgorithmicgametheory[168].
Ba-sicallydened,mechanismdesignisconcernedwithdeningtherulesofthegame(i.e.the
marketmechanism),suchthattheoutcome(i.e. nalallocationoftheitems,togetherwith
the correspondingpayments)guaranteescertaindesiderata (i.e. properties).
Commonly-citeddesideratainclude,forexample:Pareto-optimality,efciency,budgetbalanceor
indi-vidualrationality[58].
Besidesfromthesegame-theoreticdesiderata,computationalrequirements(i.e.the
com-putationtimeormemoryneededtondsuchamechanism)oftenplayanimportantrole.The
biddingagents,asthestructureofthemarketprovidesbidderswithanequilibriumbidding
strategy.Differentequilibriumconceptexist,varyingintheirstrength,e.g.dominant
strate-gies,ex-postefcient,Bayesian.Themostdesirablemarketmechanismsarestrategy-proof
mechanisms,i.e.thosemechanismsinwhichtruthfulbiddingisthedominantstrategy.
Themechanismdesignapproachhasproventobeverysuccessfulinmanyapplications.
However,thereexistsawiderangeofpracticalsettingsforwhichitisunrealistictoassume
thatonecandesignacompletelynewmarketmechanismfromscratch. Furthermore,many
mechanismsproposedbythislineofresearchofteninvolveallocationandbiddingrulesthat
carefullydesignedandmathematicallysound,butmaybecounter-intuitiveforhumanusers
ofthesystem.
Moreover,inmanyreal-lifeallocationproblems,thereismorethanonemarketanagent
can/shouldparticipatein,andthestrategicbehaviouracrossmarketbordersbecomesthe
crucialissue.Evenifanagenthasanoptimal(e.g.dominant)biddingstrategyineachofthe
marketsheparticipatesin,whencoordinatingthebiddingindifferentmarkets,hisoptimal
strategymaybeverydifferentfromthedominantstrategyforeachmarkettakeninisolation.
One suchexampleis bidding ina sequenceof secondprice(i.e. Vickrey)auctions, for
agents whichhavecomplementaryutilities overtheitems beingoffered[27,89,187,217]
1
. Althoughtheagents insuchasequentialauctionhaveadominantbidding strategyin
eachindividualVickreyauctiontakeninisolation,biddingoptimallyinasequenceofsuch
auctionsisacomplexdecisionproblem,andthebidsplacedintheoptimalsequentialbidding
policymaydifferconsiderablythanthedominantbidsintheindividualauctions
2 .
Similarly,arelateddecisionproblemisfacedbyagentsbiddinginasetofsimultaneously
ascendingascendingEnglishauctions,whenagentshavecomplementaryutilityfunctions[1,
184].Whereasasimple,dominantbiddingstrategyexistsforeachEnglishauctiontakenin
isolation,determiningtheoptimalbiddingstrategyfortheentiresetisachallengingproblem,
forwhichnodominantstrategyresultsareknown. Intuitivelyexplained,itisdifcultfor
abiddingagenttodistributethe additionalcomplementarityvalueacross asequenceora
setofsimultaneousauctions, becausean agentcanonlyknowifhe canbenetfrom the
complementarityonceallitemsinthedesiredbundlehavebeenacquired(i.e. onceallthe
auctionsclose).
Whilethecomputationalmechanismdesigncommunityhasbegantoaddresssomeof
thesechallenges,throughsuchtechniquesasonlinemechanismdesignoradaptive
mecha-nismdesign[33,74,92],theseapproachesstillimposeseveralrestrictionsonthestructureof
theproblem,andformanymarketsettingwidelyusedinpractice,nodominantequilibrium
strategiesare[yet]knowntoexist.
Theresearchperformedforthisthesismostlyfollowstheothermaindirectionofresearch
1
Whilethesenotionswillbeformallydenedlater,intuitively,acomplementaryvaluationimpliesthatanagent assignsacombinationofitemsasuper-additiveutility(i.e.autilityhigherthanthesumoftheitemstaken individ-ually),whilesubstitutabilityimpliesasub-additiveutility(seeChapter3)
2
Inasequenceofauctions,thisistruewhenevereithercomplementarityorsubstitutabilityeffectsexistbetween items,orthereareotherpreferenceconstraintstoaccountfor,suchasbudgetconstraintsoraversiontorisk(c.f. [27,89,187]andChapters4and5.
designtheoptimalagentstrategiesforbiddinginsuchmarkets?
Workondesigning biddingagents' strategiesalso hasarelativelylonghistoryin the
MAS community[1,27,81,83,89,184,188,223](amongmayothers). Severalplatforms
havebeenproposedtoenablecomparisonofdifferentauctiontradingstrategies(aswellas
learningandadaptationheuristics).Themostwell-knownistheTradingAgentCompetition
(TAC)platform,withits differentversions: TACclassic, TACsupply-chainmanagement
etc.[96,192,227,230]. ThemarketstructurefortheTACcompetitionsisbuilttoresemble
tradingscenariosthatwouldbeencounteredinpractice. Reasoningrequiredforthe
trad-ingagentsintheseplatformscombineselementsofbothefcientbiddinginsequentialand
simultaneousauctions,aswellaslearning,anticipationoffutureorders,inventory
manage-mentetc. Anotherdirectionofwork(mentionedhereforcompleteness)examinesbidding
heuristicsfordoubleauctionsettings[226],whichischaracteristictonancialmarkets[124].
Asalreadydiscussed,inthisthesiswealsotaketheheuristicapproach,andwearemostly
concernedaboutthedesignofagentstrategies,ratherthanthethemarketmechanismitself.
Inparticular,weareconcernedwithoneaspectoftheproblem,whichishowtomodeland
efcientlyusepreferenceinformationoftheagentstakingpartinsuchmarkets.
1.4 Modeling preferences and utilities in agent-mediated
marketsettings
Inbuildingefcient electronicmarkets, the methodofmodelingandreasoningabout the
preferences ofparticipatingagents is akeymodelingchoice. Some sourcescall
model-ingpreferencesofbuyersandprovidersremainstheAchilles'heelintheapplicationof
multi-agentresourceallocationtoindustrialprocurementsettings[46].Thereare,however,
considerabledifferencesastotheirmeaningofthetermspreferenceandutilityin
dif-ferentsources intheeconomicsandmulti-agentsystemliterature. Inthe broadestsense,
preferencesexpresstherelativeorabsolutesatisfactionofanindividualwhenfacedwitha
choicebetweendifferentalternatives[46]. Inthisthesis,webroadlydistinguishbetween
twobroadclassesofconceptsofpreferenceorutility:preferencesincombinatorialsettings
(i.e. toreasonregardingmultiplecriteria ormultipleitems)andpreferencesunder
uncer-tainty.
Mostexistingliteratureonmulti-agentresourceallocationandmarketmechanisms
con-siderscombinatorialpreferencesandutilities. Combinatorialpreferencesareeither
multi-item(i.e.involveexpressingpreferencesovercombinationsofitems)ormulti-issueor
multi-attribute(e.g. involvecombinationsofattributesforthesameitem, e.g. colour,priceand
mileageforausedcar,totaketheexampleusedinChapter2). Moreover,inmany(ifnot
most)realisticapplications,itisreasonabletoexpectthattherearecomplexdependencies
betweenattributesoritems,andthechoiceinonemayaffectthechoicemadeforasubsetof
non-linear,combinatorialpreferencesinmarketsituationsisacomplexproblem,whichwe
discussinSect.1.7below.
Anotherperspectiveondeningpreferencesconsidersthecomplexdecisionsanagent
faces,notsomuchwithrespecttospecifyingdesiredcombinationsovermultipleitemsor
issues,butwithrespecttouncertaintyaboutthefuture.Thisappearstobeastandard
under-standingofpreferenceinsomeelds,suchaseconometrics.Forinstance,ina2006MIT
textbookoneconometricanalysisofauctiondata[171],thechapterregardingpreferences
dealsexclusivelywithpreferencestowardsrisk. Inthisthesis,we considerboth
perspec-tivesonpreference,andbothtypesofmarket-basedinteractiondiscussedabove: bilateral
negotiationandauctions,althoughfordifferentproblems.
1.5 Emergenceofcollaborationandstructureinmulti-agent
systems
Asnoted inSection1.1,another importantproblemarisinginmulti-agentsystems is the
lackofcentralizedcontrol.Nevertheless,manysystemsoccurringinreal-lifethatonewould
intuitively recognizeas multi-agent, exhibit aremarkabledegreeofstructure, although
theylackanyrecognizablecentralauthorityorcontroller.Instead,orderseemstoemerge
fromthedecentralizedactionsofmanyautonomousagents,actingindependentlytosatisfy
theirowninterest.Examplesofsuchsystemsinclude:theformationofequilibriaandpricing
structureinmarkets(aphenomenarstreferredtobyAdam'sSmithastheguidinghand),
emergenceofstablevocabulariesinhumanlanguages(butalsointaggingsystems)[40,93,
206],formationofstablegroupsinonlinesocialnetworks[131]etc.Thisraisesquestionsnot
onlyregardingtheexistenceandpropertiesofsuchstablestructures,butalsothedynamicsof
theprocess,i.e.howdotheyform,especiallyinanenvironmentwithnocentralinformation
sourceand/orself-interestedparties.
Oneoftherecentlyemergingeldsthataimstostudysuchphenomenaiscomplex
sys-temstheory[11,160,228]. TheseminalworkofRobertAxelroodontheevolutionof
co-operation[7]markedaturningpoint,sinceitshowed,throughcomputersimulations,how
cooperationcanemergeinamulti-agentsystem,evenintheabsenceofacentralauthority.
Arelated discipline that aims to examinecomplex-systems typephenomena through
large-scalesimulationsisagent-basedcomputationaleconomics[218].Therehasbeenmuch
workrecentlyinthisarea.Forexample,researchershavesimulatedthedynamicsofarticial
agentsocieties[7],stockmarkets[6],andevenentireeconomies[37,68].Thedevelopment
ofthewebhasgivenanewstimulustothiswork,andresearchershavebuiltcomplex
sim-ulationsoftheemergenceofsocialnetworks[131],onlinemarketsystems[54]orarticial
languagesandsemioticdynamics[42,210].
But,perhapsmoreimportant,theemergenceofthesocialwebprovides,fortherst
time,theopportunitytotestthesehypothesesempirically,onreal-worlddata.Infact,while
re-datasetsgeneratedbytheactionsofverymany(thousands,orinsomecasesevenmillions)
ofwebusers.
Interestingly,manyofthe effects foundresembled closely what was hypothesised in
complexsystemstheoryfromthebeginning. Inparticular,itappearsthatthereare
impor-tantnetworkeffectswhenmanyuserscollaborateonlineandmakedecisionsinan
on-linecommunityormarketplace. Whatthismeans,basically,isthattheactionsandchoices
madebyprevioususersmayconsiderablyinuencethechoicesmadebyfutureusers.This
typeofself-reinforcingfeedbackloopoftengivesrisetotheso-calledpowerlaw
distri-butions[11,41],whicharecharacteristicoflarge-scalesystemsthatcanbecharacterisedas
complex.
Thisthesismakestwoimportantcontributionstounderstandingtheemergenceofsocial
structureinsuchlarge-scale,decentralizedsystems. Oneiscollaborativetagging(results
presentedinChapter6)andsponsoredsearchmarkets(Chapter7).
1.6 Positioningof thecontributions ofthis thesis
The previousdiscussion identiedsomeimportantopenchallenges inunderstandingand
designingmulti-agentsystems:
Complexityofrepresenting(andreasoningwith)complexpreferences.Theseinclude
bothcombinatorialpreferencesandpreferencestowardsriskanduncertainty.
Strategicreasoningofagentsbasedonthesecomplexpreferences,especiallyforcases
whenagentsareself-interested.
Lackofcentralcontrol,andespecially,theemergenceofcooperationintheabsence
ofacentralauthority.
Inthisthesis,weaimtomakeseveralcontributionstothestateoftheartinunderstanding,
modelingandsolvingthesechallenges,asfollows.
PartIofthethesisismostlyconcernedwiththeissueofmodelingcombinatorial
prefer-ences(multi-issueormulti-item)inbilateralnegotiations. Chapter2considershow
prefer-enceinformationcanbeefcientlyusedinanegotiationmodelinwhichuserspreferences
areexpressedoverseveraldiscreteattributesandonecontinuousattribute(price).Chapter3
considershowcomplex,multi-issuenegotiationsovermanybinaryitemsorbundlesofitems
canbemodeledusingutilitygraphs. PartIalsodealswithsomeissuesrelatedtostrategic
reasoning,since,althoughbilateralnegotiationisoftenapartiallycooperativeprocess,there
isanimportantdegreeofself-interestinvolvedonthepartofthebargainingagents.
PartIIofthe thesiscanbeseenas mostlydealingwithpreferencesinuncertain
envi-ronmentsandstrategicreasoning,inparticularthestrategicreasoningofagentswith
pricedoptionsmechanismcanhelpsolvetheexposureproblembiddersfaceina
sequen-tial auctionssetting(andimplicitly, thestrategicreasoningitinvolvesduringthe bidding
process). Buttheissueofpreferenceisalsoimportantinthispartaswell,althoughinthe
formofagentpreferencestowardsrisk,whenfacedwithanuncertainfuture.Inthiscontext,
Chapter4examineshowanagent'spreferencestowardsriskaffectshis/heroptimalbidding
policyandresultingmarketallocation.
Finally,PartIIIofthethesisstudiestheissueoflackofcentralisedcontrol,more
particu-lartheemergenceofcollaborationandstructureinalargemulti-agentsystem,intheabsence
ofacentralcontroller.Forthispart,weuselargescale,empiricaldatafromtwoimportant
socialwebapplications:collaborativetaggingandsponsoredsearchmarkets.Whileinin
collaborativetagging,forinstance,theissueofstrategic/game-theoreticreasoningdoesnot
playadirectrole(sincethereisnocompetingallocationofsomescarceresource),stillthe
issueofhowagentstakedecisionsisacrucialonetomodel. Arguably,thereisalsoa
con-nectiontotheissueofpreference,sincethroughtheirchoiceoftagsandlinkstoclickagents
expressanimplicitopinion(whichmayormaynotbeinuencedbythatofotherusers).
Therestofthisintroductorychapterisorganisedasfollows. Inthefollowingsections
(Sect.1.7-1.10),wegivemoredetaileddescriptionsoftheproblemsinthisspacewhichwe
aimtoaddressinthisthesis,aswellasbriefabstractsofourresultsforeach.InSection1.11
wegivetheoverviewofthestructureof[therestof]thethesis.Section1.11alsosummarizes
thestructureofthethesisthroughadiagram,suchastomoreintuitivelyhighlightandexplain
therelationsthatexistbetweenthedifferentchapters.Theintroductionconcludeswithalist
ofresultingrefereedpublicationsrelatedtoeachchapter.
1.7 Modeling of combinatorial preferences (multi-issue or
multi-item)inbilateralnegotiations
Therearemanywaystoexpressachoicebetweenmultipleoutcomesdenedinmulti-agent,
economicsandAIliterature. Ataxonomyofpreferencesusedinthemulti-agentliterature
wouldinclude:
Qualitativepreferences: Nonumericalutilityvaluesareassignedtooutcomes,only
valuelabelssuchasgood,verygood,unsatisfactoryetc.
Quantitativepreferences: Preferenceoveroutcomesare expressedinthe formofa
utilityfunction(tobedenedbelow).Notethatsometimesqualitativeandquantitative
preferencestakentogetherarecalledcardinalpreferences.
Ordinalpreferences:onlyanordercanbespeciedbetweenranking(i.e. throughan
asymmetricandtransitivepreferencerelationbetweenalternatives).
ausercanassignanumericalvalue(eitherutilityormonetaryvalue)topossibleoutcomes
(orcombinationofitems),andthediscussioninthefollowingsectionsofthisintroduction
referstothequantitativecase.
However,notethat,ifonedenespreferenceasanychoicebetweenseveraloutcomesor
alternatives,theconceptofpreferencecanbeconstructedasbroader. Forexample,actions
suchas choosingatag that otherusershavealso selectedinthe past(see Chapter6), or
clickingonthelinkatthetopofalist,inordertosavereadingtime(seeChapter7)may
beseenasexpressinganimplicitpreference. WereturntothisideainSection1.10,when
describingthe chaptersinPartIII ofthethesis -the discussion inthefollowingsections
referringtothecaseofquantitative,economicpreferences.
Thebasisofquantitativepreferencemodelingisutilitytheory. Followingtheworkof
KenneyandRaiffa[179],manymulti-issuenegotiationandresourceallocationmodelsuse
autilityfunction,whichmapstheoutcomespaceoverasetofissues(attributes,criteria)to
autilityvalue,whichisfrequently-thoughnotnecessarily-scaledbetween0and1. The
crucialthingtonoteisthatRaiffa'smodelsandmuchoftheinitialresearchonmulti-issue
negotiationconsiderslinearlyadditiveutilityfunctions,i.e.eachissue/attributeunder
nego-tiationisassignedaweight,andtheutilityofeachpossibleoutcome/contractiscomputedas
aweightedsumovertheissuesundernegotiation.
Aspecialsubclassofquantitativepreferencefunctions,whichisimplicitlyusedinmost
ofexistingauctionliterature, are the so-calledquasi-linear preferences. Thisbasically
meansthattheutilityoftheagentsisexpressedinmonetaryterms(asanamountofmoney),
as opposedtoutils(i.e. conventionalunits,usuallyscaledbetween0and1). Thiscanbe
viewedasarestrictionforsomesettings,asrealutilityfunctionsovermonetaryendowments
areknowntobeconcave,i.e. humansareknowntohaveadecreasingmarginalutilityfor
money(see,e.g.[171]).
1.7.1 Pareto-optimaloutcomesinmulti-issuenegotiation
As shown in[179],multi-issueandmulti-attribute negotiation models arefundamentally
differentfromsingle-issuenegotiation (suchas bargainingoveraprice). Multi-issue
ne-gotiationsrepresent non-zerosumgames, inthesense thatitis possibletond mutually
benecialtrade-offsbetweentheissuesundernegotiationsuchas toincreasethegainsfor
bothparties. Raiffaalsoshowsthatthemoreasymmetricpreferencesbetweenthe
negotia-torsare,thehigherthepotentialformutuallybenecialtrade-offsbetweentheissues.
Themaincriteriatomeasurehowefcientanagreement(orcontract)isthe socalled
Paretoefciency. AnoutcomeissaidtobeParetooptimalifitisnotstrictlydominatedby
anyotheroutcomeinthepreferencesofboth(orall)sides(agents)inanegotiation. That
means,therearenotrade-offspossiblethatwouldincreasetheutilityofoneagent,without
making anotheragentworse off. The setofall Pareto-optimalpointsform theso called
thatoutcome/contractis.
Animportantconceptindesigningmulti-issuenegotiationmodelsistheuncertainty
re-gardingopponentpreferences,denedhereastheamountofinformationregardingthe
op-ponentpreferencesavailablewhenmakingnegotiationoffers.
Directvs.indirectrevelationmechanisms
Theliteratureonagent-mediatedelectronicmarketsidentiestwomainapproachesbywhich
agentscansharetheirprivatepreferenceinformation:
Directrevelationmechanisms.Directrevelationmechanismsarebasedonthe
revela-tionprinciple[58]. Basicallyexplained,therevelationprinciplestatesthatany
allo-cationmechanismwithacertainequilibriumcanbetransformedintoanother
mecha-nism,inwhichatrustedcenteraskstheagentstotruthfullyrevealtheirpreferencesand
implementstheoriginalequilibrium(andallocation)ontheirbehalf.Thismeansthat
typically(thoughnot exclusively),directrevelationleadstoacentralizedallocation
mechanism,suchasacombinatorialauction.
Indirectrevelationmechanisms:Inthistypeofmechanism,theagentsarenotassumed
todirectlyrevealtheirpreferencestotheotheragents,butcommunicatetheir
prefer-encesthroughouttheircounter-offers(ortheirbids,foranauction). Forinstance,in
abilateral,multi-issuenegotiationoverthe saleofacar(see [115,116]and
Chap-ter2ofthisthesis), theagents donotdirectlyreveal toeachotherhowmuchthey
arewillingtopaytogettheirfavouritecolourortheirfavouriteaccessories(e.g. CD
player,airconditioning)installed,butinpractice,thiscanbededucedindirectlyfrom
theoffers/counter-offerstheymake. Similarly,anagentrepresentingacustomerona
largeelectroniccommercewebsite(see[185,186,220]andChapter3)doesnothave
torevealallhispreferencestothemerchant,butthemerchant(whomayormaynot
alsoactastheauctioneer)canlearnpartoftheirpreferencesfromprevious
counter-offers.
Basically,inthisthesiswetaketheindirectrevelationapproach,aswearguethisismore
realisticinmanyreal-lifeapplications,inwhichonlyalimiteddegreeoftrustexistsbetween
partiesinsharinginformationandnofullytrustedthirdpartycanbeestablished. The
rea-sonsforthismaybeendogenoustothenegotiationmechanism(e.g. thereisnooptimal
incentivecompatiblemechanismandtheopponentmayuseanyinformationsuppliedtoget
abetterdealforhimself)orexogenous(e.g.itmaybeundesirabletohavetospecify
prefer-encesoverthewholesetofalternatives,duetoprivacyconcernsorfuturebusinessinterests).
Furthermore,forcomplexnon-linearpreferences,therearealsopreferenceformulation
andcommunicationcosts. Aswe showinChapter3, becauseofboundedrationalityand
communicationability,itisoftendifcultforanagentherselftoformulateandcommunicate
searchintomodelingpreferencesinagent-mediatedmarkets,followedbybriefdescriptions
ofthecontributionsmadeinthisthesistoopenproblemsintheeld. Therefore,some
sec-tionsoftheintroductionpresentimportantconceptsfromageneralpointofview,whileother
sectionsdescribehowthesegeneralconceptswereextendedbyourownresearch,described
inthechaptersofthisthesis. Thegoalistoallowthereadertogetabetterunderstandthe
positioningandcontributionofourworkwithrespecttothestateoftheartintheeld.
1.7.2 Modelingmulti-attributenegotiationwithincompletepreference
information
Animportantdirectionofworkintheliteratureonmulti-issuenegotiationishowtodesign
efcientbargainingstrategiesinsettingswhenagentsdonothaveanyinformationaboutthe
opponent's(i.e. negotiation partner's)preferences. Theymayhave, however,someprior
knowledgeaboutthedomaintheyarenegotiatingabout. Thispriordomainknowledgecan
be, forexample,fuzzylogicdistancesbetweenattributes,suchas theperceptualdistance
betweendifferentcolours(suchasin[71,163]),oranorderingbetweenasetofqualitative
attributelabels(suchasgood,standard,meageretc),inourresearch(seeChapter2).
TheworkpresentedinChapter2and[115,116]considerssuchanincomplete
informa-tionnegotiationmodel.Theaimofthismodelistoinvestigatetherolethatpartiallyrevealing
preferenceinformationcanimprovetheoutcomeofamulti-attributenegotiation.Asa
prac-ticaldomaincase,we consideredabilateralnegotiationbetweenabuyer(customer)anda
seller(cardealer)overthesaleofacar.Thenegotiationisnotexclusivelyonprice,butalso
on thequalityoftheaccessories whichthe dealerhastoinstallinthecar togetthe deal
done(suchasaCDplayer,extraspeakers,airconditioningandtowhedge). Inthissetting,
weshowthatitispossibleforbothpartiestoreachclosetoPareto-efcientagreements,by
revealingonlypartial(i.e.incomplete)informationabouttheirpreferencesofthenegotiation
partner. Furthermore,weproposedanovelguessingheuristic,bywhichanagentusesthe
historyofopponent'sbidstopredicthis/herpreferencesinordertoproposebetterdeals.
1.7.3 Non-linearandcombinatorialpreferencesinnegotiation
Acrucialprobleminapplyingmulti-issueormulti-itemnegotiationmodelsinmanyrealistic
settingsisthefactthattheremaybecomplexinter-dependenciesbetweendifferentissues,
leadingtonon-linearpreferencesorutilityfunctions[108,126,138,186].Theproblem
ap-pearsbothwhenconsideringintegrative, multi-issuenegotiations,as wellas negotiations
overbundlesofitems [46,186]. Inboththesecases, it isimportanttoallowforconcise
representationsoftheutilitiesoverpossibleoutcomes
3 .
3
Theproblemisinfact,two-fold.Firstthereisthecomplexityrelatedtopreferenceformulationofcombinatorial preferences,aswellasoneofpreferencecommunicationcomplexity.
Theeasiestwaytorepresentpreferencesistoenumerateallpossibleoutcomes(or
com-binationsofgoods),togetherwiththeirutilityvalueforthosegoods(monetaryorotherwise).
Thisiscalledtheexplicitformofpreferencerepresentation(orbundleform).Theexplicit
formisfullyexpressive, inthesense thatanyutilityfunctionmaybedescribedbylisting
allpossiblecombinationsandtheirvalues. Itis,however,impracticalformostnon-trivial
settings,asthenumberofdescriptionswouldbeexponentialinthenumberofresources(e.g.
foronly50binaryissuesoritems,2
50 >10
15
valueswouldneedtobeassigned-see
Chap-ter3).Thishaspromptedanotherimportantdirectionofresearchinelectronicmarkets,that
ofdesigningmoreconciseutilityrepresentation(orpreference)languages.Thereareseveral
classesofsuchpreferencelanguages:
Biddinglanguages,whicharetypicallyusedincombinatorialauctionstoallowagents
toformulatetheirbids(and,implicitly,communicatetheirpreferencestothe
auction-eer).Somespecicbiddinglanguagesinclude:
The ORlanguage: The agentcan specify an array ofvaluations over
differ-entsubsetsofitems inagivenbundleofitems. Thevalueofanycombination
canthenbecomputedasthemaximalvaluethatcanbeobtainedasasumover
disjointsubsets specied[46]. Forexample,inthebid: < fI
1 g;3 > OR < fI 2 g;3> OR <fI 3 g;3 > OR < fI 1 ;I 2
g;8 >expressesthatthebidderis
willingtopay3foreitherI 1
;I 2
;I 3
or11forall3items(inthiscase,itisbetter
totakethevalueofthesubset<fI 1
;I 2
g;8>thanthevaluesofeachindividual
itemseparately).BecausetheORdependencyisnotexclusive,theORlanguage
cannotexpresssubstitutabilitydependencies,i.e. itcannotexpressthefactthat
gettingacombinationofitemshaslesserutilitythanthesumofindividualitems.
Intheaboveexample,itisnotpossiblefortheagenttoexpressthatheisonly
willing topay4ifhegetsbothI
1 ;I 2 . Ifthebid< fI 1 ;I 2 g;4 >wereadded
tothesetofbidsplaced,thentheauctioneerwouldsimplymatchthebidsover
the individualitems < fI 1
g;3 >and< fI 2
g;3 >(as anyterms ofthe OR
dependencymaybechosen).
ExclusiveOR(i.e. XOR)biddinglanguage[194]-isanalternativetoOR,in
whichallcombinationbidsareassumedtobemutuallyexclusive.Forexample,
intheaboveexample,abidsuchas:<fI 1 g;3>XOR<fI 2 g;3>XOR< fI 3 g;3>XOR < fI 1 ;I 2
g;4>meansthattheagent(bidder)caneitheruse
onlyoneitemfromI
1 ;I
2 ;I
3
withautilityof3,orthecombinationoffI
1 ;I
2 g
witha utilityof4, butnoother combination(so, e.g. gettingbothI
1
andI
3
wouldstillonlyhavetheutilityof3).XORisfullyexpressive,inthesensethat
itcanrepresentanymonotonicutilityfunction.However,XORmayhaveahigh
communication/elicitationcost,evenforsimplesettings.Anexampleisthe
util-ityfunctionthat,foranysetRofitemsI 1
;:::I n
2R ,simplycountsthenumber
ofitemstheagentowns-i.e. u(R ) = jR j. Suchafunctionwouldrequirean
exponentialnumberofbidsintheXORlanguage,butonlyalinearnumberin
ORlanguage.Thisisbecause,usingXOR,allcombinationsspeciedare
consideredmorenaturalwaytorepresentpreferences,thereexistsalineofwork
thataimstoextenditsexpressiveness,withoutrequiringanexhaustivelistingof
XORbids[169].
Weightedpropositionalformulasandstraightlineprogramsareotheralternativesto
representingcomplexpreferences,whichmakeuseoflogicalformalisms. For
exam-ple,weightedpropositionalformulasarederivedfromaqualitativeformofpreference
representation,inwhichthepreferencesoftheagentareexpressedasgoals. Inthe
weightedcase(unlikeinpurelyformallogicapproaches),goalscanbeassigneda
util-ityweightifsatised. Wedonotdealwiththiskindofpreferencelanguagesinthis
paper,butthereadercanconsult[139]forthefulldetailsofthisapproach.
Thek-additiveform[46,55,186](alsocalledthepolynomialform[133])isanother
naturalandconcisemethodtorepresentcombinatorialpreferences. K-additive
func-tionscanencodesynergy(complementarityorsubstitutability)effectsbetweensubsets
ofuptokitems. Forinstance,ifwedenotebyx
1 ;:::x
n
theinstantiationofthesetof
nitems,theexpressionfora3-additiveutilityform(i.e.takingamaximumk=s)is:
U(x 1 ;:::;x n )= X 1in i x i + X 1i;jn i;j x i x j + X 1i;j;k n i;j;k x i x j x k (1.1) Wherex 1 ;::;x n
representsavectorof1and0,denotingwhetheranitemis(orisnot)
consideredinthecombinationbeingevaluated,thereals
1 :::
n;n;n;n
arethe
param-etersofthefunction,whilek(samekasink-additivity)isthemaximumrankofthe
polynomial,i.e. allthepolynomialtermshavingarankabovek
max
havethe
coef-cients = 0. Linearlyadditivefunctionsformasubclassofthek-additiveclass,as
denedabove,fork
max
=1. Thek-additiveformisfullyexpressive,forunbounded
k.Thismeansthat,ifkissufcientlylarge,itcanbeusedtoexpressanyutility
func-tionovera given,nite, binarysetofitems. Inpractice,although(as discussedin
Chapter3thisthesis),inorderforthisrepresentationtobecomputationallyuseful,the
maximumrankofthepolynomialkisgenerallyassumedtobeboundedtoalimited
value(e.g.2-4,asdiscussedinChapter3).
1.7.4 Modelingmulti-itemnegotiationsoverk-additiveutilityfunctions
usingutilitygraphs
InChapter3ofthethesis,weconsiderthecaseofmodelingcomplexbilateralnegotiations
overasetofmultiple,binaryissues(whichcanalsorepresentabundleofitems). Fromthe
conciserepresentationformsdiscussedinthetaxonomyfromSect. 1.7.3,theoneweused,
as wefounditmostnaturalinthe contextofthemulti-issuenegotiation,isthek-additive
form.Thisrepresentationisanaturalextensionoflinearutilitymodelsalreadyusedinmuch
nonlinearityinpreferencemakesthebargainingproblemconsiderablyharder.Forexample,
inEq.1.1above,thecaseofk
max
=2isalreadymuchharderthank
max
=1.
Multi-issuenegotiationwithnon-linearutilityfunctionsisknowntobeacomplex
prob-lem, even forthe caseof binaryissues[108,126,207]. The state ofthe art inthiseld
proposescomplexsolutionsthatinvolveamediator,aswellastechniquessuchassimulated
annealing[126]oreconometricmethods[207]thatareeithercomputationallyexpensiveor
do notscalewellforsettingswithmanyissues. In[186](correspondingtothe rstpart
inChapter3)weintroduceanovelutilitygraphformalismformodelingnonlinear(i.e.
k-additive)preferences,andweshowhowsuchgraphscanbeusedtomodelandlearn
oppo-nentpreferencesincomplex,multi-issuenegotiations.Utilitygraphsareoriginallyinspired
fromprobabilisticgraphicalmodels,buttheyencodeutilities,ratherthanprobabilities.The
mainideabehindourapproachistousethestructureofthegraphstorestricttheopponent
modelingandsearchtothemostpromisingregionoftheutilityspace. Aselleragentcan
startanegotiationwithanapproximationoftheutilityfunctionofatypicalrandombuyerin
theformofamaximalutilitygraph,andthenrenethismodelbasedonthecounter-offers
he observesduringthe negotiation. Inourcase,the initialutilitygraph reectsthe prior
informationthatthesellerhasabouthowtheutilityfunctionofarandombuyerisstructured,
inordertohelpinthesearch.
Animportantquestionis,ofcourse,howdoestheselleracquirethisinitialbuyerutility
graph approximation. Onesolution istoassume somepriordomain knowledge, suchas
plausibleconstraintsonthe shapeautilityfunctioncouldtake(whichmaybereasonable
forsomesettings). Fore-commercedomains,wehaveproposedanotheralternative:using
collaborativelteringonprevioussalesdata,thatwillbepresentedafterthediscussionin
thenextsubsection.
1.7.5 Individualpreferencesandsocialinuence
In the previousdiscussion oncombinatorial preferences, preferences are dened from a
single-agentperspective,meaningthattheutilityofanyagentisassumedtobeprivateand
independentofwhatotheragentsmaydesire. Otherwiseput,ifasellerencountersabuyer
andnegotiateswithhim thecongurationofaproductorthecompositionofabundleof
items,hewillassumethatthepreferredcombinationsofthisparticularbuyerarecompletely
independentofwhatotherbuyersencounteredwantedinthepast.Thisis,infact,astandard
assumptioninmuchofnegotiationandauctiontheory.
However,existingpracticeinelectroniccommercesuggested,fortheChapter3ofthis
thesis,analternativeapproach.Thesuccessofsocialsearchinprovidingonlinebuying
rec-ommendationsprovidesconsiderableevidencethatpreferencesarenotstrictlyindependent,
butareinsomewayclustered. Considerforexample,the caseofAmazon.com,whohas
severalmillionbooktitlesinitscollection. Eliciting,foreachindividualcustomer,his/her
preferencesoverthesebookstoproposeacceptablebundlesforthebuyerswouldbeanearly
impossibletask.However,Amazonimplicitlyassumesthatifalargenumbersofcustomers
aswell. Forinstance,ifacustomerbuysabookontravellingtoPortugal,theAmazon
en-gineassumeshemayalsobeinterestedinabookontraveltoSpain,sincemanycustomers
encounteredinthepastshowedinterestedinboth.Thereforeproposingadeal(e.g.postage
reduction,orasmalldiscount)maybeagoodwaytoincentivisethecustomertobuyboth
booksfromthesite.
Notethatthisdoesnotalwayshavetobeacorrectprediction: infact manycustomers
maynotbeinterestedintheexactcombinationproposed.However,itdoesprovideagood
approximationinsearchingthespaceofacustomer'spreferences,evenifthecustomerwas
neverencounteredbefore.
Traditionally,researchinmulti-issuenegotiationdoesnot explicitlymodelthissocial
dimensionofcustomerpreferences,orconsidertherolethatsocialinuenceplaysonthe
structureofutilityfunctions.Weshowthathavinganexplicitrepresentationthatrelatesthe
twoelds(inourcaseintheformofutilitygraphs)allowsustoconsiderablyimprovesearch
inanonlinenegotiationsetting. Furthermore,theinteractionbetweentheseeldsdoesnot
have tobe one-way: negotiation also hasa lotto addto web-based recommendationin
electroniccommerce. Throughaniterativenegotiationprocess,theinitialproposals(based
onanonymous,aggregatepreferences)canbecustomizedtothepreferencesofaparticular
customer,basedontheindirectrevelationmadethroughhis/hercounter-offersinnegotiation.
1.7.6 Learningthestructure ofutilitygraphsusedinmulti-item
nego-tiationthroughcollaborativeltering
Ourapproachtomodelingopponentpreferencesinnegotiationmakesuseoftheabove
in-tuition. Chapter3ofthisthesisproposesanovelcollaborativelteringmethodbywhich
previouslyconcludednegotiationdatacanbeusedtoconstructtheinitialapproximationof
theutilitygraphofarandombuyerthatthesellercanuseinlaternegotiations. Theseller
willthenadjust(learn)thevaluesinthegraph,foreachspecicnegotiation,basedonthe
counter-offersthebuyermakes,untilanagreementisreachedoverthebundlecombination.
Therefore,wetakewhatcanbedescribedasatwo-stepcustomizationapproach: initially,
anapproximationofthemaximalstructureofautilitygraphforarandombuyerisobtained
usingcollaborativelteringonallconcludednegotiation data(whichdoesnothavetobe
buyer-specic).Then,thisdealisrenedthroughoffersandcounteroffersduringthe
nego-tiationwithaspeciccustomer.
Weshowthat thecombined approachcanenablebuyersandsellers toreachefcient
agreementsevenincomplexnon-linearsettings,involvingonlyindirectrevelation(although
therearesomeassumptionsregardingthemaximalcomplexityoftheutilitygraphsthata
buyercanhave).Oneofthecontributionsofthisapproachtothestateoftheartinautomated
negotiationisthatitprovidesalinkbetweenthecustomizationtechniquesusedinmulti-issue
ormulti-itemnegotiationandthoseusedincollaborativelteringandsocialcomputing.In
andshorternegotiationsforcomplex,non-linearutilitysettingsthanwasreportedinother
research[126].
1.8 Preferences under uncertainty and bidding in
sequen-tialauctions
Inthepreviousdiscussion,wehavemainlydiscussedtheconceptofpreference(orutility)
inthecontextofintegrativenegotiation,inwhichtheallocationsforallitems(orissues)is
agreedatthesametime. Thus,whenanagentspeciesapreferencebyassigninga
mone-taryvaluetoacombinationofitems, heisbiddingforanentirecombination,andthereis
nouncertaintythathewillnotgetsomeofitemsintheagreedconguration,iftheseller
ac-ceptstheoffer.Thisisareasonableassumptionforintegrativenegotiationandcombinatorial
auctions(wheretheallocationforallitemsisnegotiatedsimultaneously).However,itdoes
not holdforotherwidelyusedallocationmechanisms, suchas sequential/simultaneously
ascendingauctions[27,89,184,217]orone-by-oneissuenegotiations[72].
Inthissection(correspondingtoChapters4and5ofthethesis),weconsiderthecase
whenagentshavetobidsequentiallyitemssoldindifferentauctions,withoutknowingwith
certaintythattheywillgettheentirecombinationofitemstheydesire. Insuchcases,
eco-nomictheory identiesanother important class ofpreferences, preferences towards risk.
Riskaversionisaveryimportantpartofeconomictheory-infact,a2006MITtextbookon
theeconometricsofauctiondata[171],thechapteronpreferencesisbasicallyconcerned
withpreferencestowardsrisk.
Thewayeconometrictheorymodels riskaversionisthroughtheso-called
Neumann-Morgensternpreferencefunctions,inwhichthe utilityderivedbyanagentfrom acertain
amountofmoneyisnotalinearfunction,butaconcaveone.Otherwisestated,utility
func-tionsarenotquasi-linear,inthesensethattheutilitythateachagentderivesfromanamount
ofmoneyisnotdirectlyproportionaltotheamountpaid/received.
Inthefollowing,webrieydenetheexposureprobleminsequentialauctions,therole
thatriskaversionplaysinthebiddingdecision,aswellasanoverviewofthecontributions
inChapters4,5andAppendixA.
1.8.1 Sequentialauctionsandtheexposureproblem
AsshowninSect.1.3above,therearetwomaindirectionsofresearchintheapplicationof
agentsystemstoauctionmarkets.Oneconcernsthedesignoftheauctionmechanismitself,
suchthatparticipantagentshaveadominantbiddingstrategy(usually,todeclaretruthfully
theirvalues),aswellascertainproperties,suchasefciency,individualrationalityorbudget
balance. However,formanymarketdesignsthatare necessarilyencounteredinpractice,
tions.
Asshownin[27,89,187]andChapters4and5ofthisthesis,themainproblemthata
bidderfacesinasequentialauctionistheexposureproblem. Informallydened,the
expo-sureproblemmeansthatanagenthastocommittobuyinganitem,beforehe/shecanbesure
thathewillabletosecureotheritemsinhisusefulsetorbundle(denedasthesetofitems
thatgiveshimapositiveutility). Ifshefailstoacquirethisbundle,thenhemakesaloss.
Hence,wesaythattheagentisexposedtotheriskofaloss.
Mostofthe modelsthatstudybiddingauctionbiddingstartfrom theassumptionthat
agents havequasi-linearutilityfunctions. Basicallydened,quasi-linearityassumeseach
agenthasasetofpayoffsthathe/sheassignstoanycombinationsofitems. Thesepayoffs
are,formanyofthemodelsstudied,private: theyarenotknowntotheotherparties. The
utility thatan agentget from participating inthe auction is assumedproportionalto the
differencebetweenhis/herprivate payoffandtheamount hepaystoacquire theitems in
question,inotherwords,itisdenedstrictlyinmonetaryprot/lossterms.
Thisquasi-linearityofpreferences assumption,whilewidelyusedandvalidformany
business models andsettings, doesnot universallyhold. Inmanyreal-lifesettings,even
assumingitistruethatagentshaveprivatevaluesfordifferentsubsetsofitemsunder
nego-tiation,protandlossarenotjudgedinthesameterms. Makingalossfromaninteraction
(i.e. payingmorethanhis/herprivatepayoffvalue)isnotproportionalasgainingthesame
amountasprot.Inotherwords,agentsarerisk-aversetomakingaloss,evenifthepotential
forgainisconsiderablylarger.
1.8.2 Designingsequentialauctionstrategiesforrisk-aversebidders
Prior to the publicationofour research, there hadbeen quite alot ofpreviouswork on
designingefcientbiddingstrategiesforagentsparticipatinginsequential[27,89,217]and
simultaneouslyascending[1,184]auctions.
Whilethisworkreportedsomepositiveresults,animportantlimitationofexisting
litera-tureweexaminedwasthatitdoesnotexplicitlymodeltherisk-takingattitudeofthebidding
agents. Byexplicitlymodel wemeanbuildingaproleofthe agent'sriskpreferences
towardsuncertain,futureoutcomes(suchasthenalallocationofasequentialauction).In
standardeconomictheory,sincetheseminalworkofK.ArrowandJ.Pratt,preferences
to-wardsriskhavebeenconsideredessentialinunderstandingandmodelingdecisionmaking
underuncertainty[5,88,153,171]. Auctionliteraturefromstandardeconomics[158,171]
considersriskaversionan importantprobleminmodelingrealbidderpreferences.
How-ever,theeconomicliteraturethatweareawareofdoesnotconsidersequentialauctionswith
complementarybiddervaluations,exceptperhapsinthesimplestofsettings(becausesuch
auctionsdonothavewell-denedequilibria). Morespecically,unliketheAIcommunity,
researchersineconomicsarenotconcernedwithdesigningautomatedbiddingheuristicsfor
ThemaincontributionofChapter4ofthisthesisismakingalinkbetweenrisk-aversion
models,andthestrategiesthatrisk-aversebidderscanuseinsequentialauctions. First,we
introducetheArrow-Prattriskmodelsfrom economicstotheproblemofmodelingagent
biddingstrategies. Wethenstudythewayinwhichtheperceivedoptimalbiddingstrategy
computedbyariskaverseagent,givenherprobabilisticmodelofthefuture,differsfrom
theoptimalstrategyofariskneutralagent.Wendthatagentsmoreaversetoriskbidmore
aggressively,inordertocovertheirsunkcostsfortheinitialitemsinthesequence.However,
ifthefuturesequenceofauctionsisinitiallyperceivedastoorisky(giventheagent'sinitial
estimationoffutureclosingprices),thebeststrategyavailabletoariskaverseagentissimply
nottoparticipateatall.
Ourexperimentalresultsshowthatariskaversebidderhas,asexpected,alowerchance
toendupwithanincompletebundleofgoods,thusmakealoss.However,whenconsidering
long-termandrepeatedinteractions,suchagentsmake,onaverage,alowerexpectedprot,
becausetheyparticipateinless auctions. Forsomemarketsettings, thisalsoaffects,ina
negativeway,theauctioneerrevenuesfromtheauctions.
Inthefollowingsection,welookatadifferentsideoftheproblemofexposuretoriskof
lossinsequentialauctions,namelywhatcanbedonetoreduceit.
1.8.3 Optionsmechanismsinsequentialoptions
Asdiscussedabove,sequentialauctionsdonotguaranteeadominantbiddingstrategyforthe
agents(unlikethecombinatorialcase). However,theproblemremains,asmanyallocation
problemsoccurringinpracticeareinherentlydecentralizedandsequential.Differentsellers
mayprefer,foravarietyofreasons,toselltheiritemsseparately-oreventhroughdifferent
markets,as thenumberofelectronicauctionsites onlineindicates. Furthermore,inmany
applicationsettings,notallresourcesthataretobeallocatedareknowninadvance,butthey
appeardynamicallyovertime. InChapter5ofthisthesis,westudyanalternativetothis
verydifcultproblemthat,althoughitcannotcompletelyeliminatebidder'sexposure,itcan
signicantlyreduceit:theuseofpricedoptions.
Intuitivelydened, anoptionisacontractbetweenthebuyerandthe sellerofaitem,
wherethebuyerhastheright tochooseinthefuturewhetherornothewillpurchase the
itemagainstthepre-agreedexerciseprice. Theselleristhenboundtosellthe itematthe
demandofthebuyer.Sincethebuyergainsaright,hehastopaytheoptionpriceregardless
ofwhetherhewillexercisetheoptionornot.
Optionsreducetheexposureproblemasynergybuyerfaces.Hestillhastopaytheoption
price,butifhefailstocompletehisdesiredbundle,thenhedoesnotpaytheexerciseprice
as well,andthushelimitshisloss. Theriskofnotwinningsubsequentauctionsispartly
transferredtotheseller,whomaymiss outonthe exerciseprice. However,thesellercan
benetindirectlyfrom theparticipationinthismarketofadditionalcomplementary-value
buyers(alsocalledsynergybuyers),whowouldhaveotherwisestayedout.
