ASocialMechanismofReputationManagement inelectroniccommunities 446EGRC,1010MainCampusDrive BinYuandMunindarP.Singh? DepartmentofComputerScience NorthCarolinaStateUniversity fbyu,mpsinghg@eos.ncsu.edu Raleigh,NC27695-7534,USA Abstract.Trustisimportantwhereveragentsmustinteract.Weconsidertheimportantcaseofinteractionsinelectroniccommunities,whernesses.Weproposeasocialmechanismofreputationmanagement,which theagentsassistandrepresentprincipalentities,suchaspeopleandbusinismscomplementhardsecuritytechniques(suchaspasswordsanddigitalcerticates),whichonlyguaranteethatapartyisauthenticatedand aimsatavoidinginteractionwithundesirableparticipants.socialmecha- whentrustedthirdpartiesarenotavailable.ourspecicapproachto thatisdesirabletoothers.socialmechanismsareevenmoreimportant authorized,butdonotensurethatitexercisesitsauthorizationinaway 1Introduction helpeachotherweedoutundesirableplayers. reputationmanagementleadstoadecentralizedsocietyinwhichagents communityprovideservicesaswellasreferralsforservicestoeachother.our Theworldwideexpansionofnetworkaccessisdrivinganincreaseininteractions munityasasetofinteractingparties(peopleorbusinesses).themembersofa notionofservicesisgeneralinthattheyneednotbebusinessservicesprovided amongpeopleandbetweenpeopleandbusinesses.wedeneanelectroniccom- sense,e.g.,justcompanionshiporlivelydiscussion. forafee,butmaybevolunteerservices,ornoteven\services"inthetraditional participants'reputationsbothforexpertise(providinggoodservice)andhelpfulness(providinggoodreferrals).thesocialnetworkismaintainedbypersonal decidingwhetherorhowtorespondtoarequest.theagentsassisttheirusers agentsassistingdierentusers.agentsandtheirusershavefullautonomyin Wemodelanelectroniccommunityasasocialnetwork,whichsupportsthe inevaluatingtheservicesandreferralsprovidedbyothers,maintainingcontact lists,anddecidingwhomtocontact.inthismanner,theagentsassisttheirusers?thisresearchwassupportedbythenationalsciencefoundationundergrantiis- 9624425(CareerAward).Weareindebtedtotheanonymousreviewersforhelpful comments.

inndingthemosthelpfulandreliablepartiestodealwith.therecommendationsbythepersonalagentsarebasedonarepresentationofhowmuchtheother partiescanbetrusted.theagentsbuildandmanagetheserepresentationsof Hardsecurityapproacheshelpestablishthatthepartyyouaredealingwithis trust.todoso,theagentsnotonlytakeintoaccountthepreviousexperiencesof authenticatedandauthorizedtotakevariousactions.theydon'tensurethat theirusers,butalsocommunicatewithotheragents(belongingtootherusers). thatpartyisdoingwhatyouexpectanddeliveringgoodservice.inotherwords, thehardsecurityapproachessimplyplacealowhurdleoflegalitythatsomeone Thenotionoftrustcomplementshardsecurity,e.g.,throughcryptography. accountableevenforthelegalactionsthattheyperform. reputationmanagement.section3presentssomenecessarybackgroundonhow mustcrossinordertoparticipate,whereastrustmanagementmakespeople toestablishanelectroniccommunity.section4introducesourapproach,giving thekeydenitionsanddiscussingsomeinformalpropertiesoftrust.section5 presentsourexperimentalmodelandsomebasicresultsthatwehaveobtained. Thispaperisorganizedasfollows.Section2presentssomerelatedworkin forfutureresearch. Section6concludesourpaperwithadiscussionofthemainresultsanddirections 2RelatedWork OnSaleExchangeandeBayareimportantpracticalexamplesofreputationmanagement.OnSaleallowsitsuserstorateandsubmittextualcommentsabout sellers.theoverallreputationofaselleristheaverageoftheratingsobtained someoneratesthem,whileonebaytheystartwithzerofeedbackpoints.both approachesrequireuserstoexplicitlymakeandrevealtheirratingsofothers.as fromhiscustomers.inebay,sellersreceivefeedback(+1,0,?1)fortheirreliabilityineachauctionandtheirreputationiscalculatedasthesumofthoseratings overthelastsixmonths.inonsale,thenewcomershavenoreputationuntil aresult,theuserslosecontroltothecentralauthority. andkasbah[2,12]requirethatusersgivearatingforthemselvesandeitherhave usestheseratingstocomputeaperson'soverallreputationorreputationwith Acentralsystemkeepstrackoftheusers'explicitratingsofeachother,and acentralagency(directratings)orothertrustedusers(collaborativeratings). Someprototypeapproachesarerelevant.Yenta[3],weavingaweboftrust[4], suchrelationshipsandhowtheratingspropagatethroughthiscommunity. inelectronicmarketplaces.however,ttpismostappropriateforclosedmarketplaces.inlooselyfederated,opensystemsattpmayeithernotbeavailable TrustedThirdParties(TTP)[7]actasabridgebetweenbuyersandsellers amongtheusersoftheirelectroniccommunity.itisnotclearhowtoestablish respecttoaspecicuser.thesesystemsrequirepreexistingsocialrelationships orhavelimitedpowertoenforcegoodbehavior. controlthroughreputation[6].insoftsecurity,theagentspolicethemselves Rasmusson&Jansonproposedthenotionofsoftsecuritybasedonsocial

withoutreadyrecoursetoacentralauthority.softsecurityisespeciallyattractive inopensettings,andmotivatesourapproach. Work(SIF)[8].InSIF,anagentevaluatesthereputationofanotheragentbased considersonlyanagent'sownexperiencesanddoesn'tinvolveanysocialmechanisms.hence,agroupofagentscannotcollectivelybuildupareputationfor others.amorerelevantcomputationalmethodisfromsocialinteractionframe- Marshpresentsaformalizationoftheconceptoftrust[5].Hisformalization describehowtondsuchwitnesses,whereasintheelectroniccommunities,deals arebrokeredamongpeoplewhoprobablyhavenevermeteachother. ondirectobservationsaswellthroughotherwitnesses.however,sifdoesnot approachforreputationmanagement:howto(1)givefullcontroltotheusersin termsofwhentorevealtheirratings;(2)helpanagentndtrustworthyagents (veritablestrangers)evenwithoutpriorrelationships;and,(3)speedupthe propagationofinformationthroughthesocialnetwork.oursocialmechanism Challenges.Thefollowingaresomeimportantchallengesforanyagent-based amongagents,evenacrosssub-communities.therefore,undesirableagentscan seekstoaddresstheabovechallenges.inparticular,ratingsareconveyedquickly quicklyberuledout. 3ElectronicCommunities adeal.thepayosinthegamearesuchthatbothagentswouldbenetifboth withtwoagents.theagentshavetodecidewhethertocooperateordefectfrom cooperate.however,ifoneagentweretotrytocooperatewhentheotherdefects, Tobetterunderstandthenotionoftrustincommunities,let'sdiscussthefamous prisoners'dilemma[1].theprisoner'sdilemmaarisesinanon-cooperativegame foreachagenttodefect,therebyleadingtoaworsepayoforbothagentsthan theonehand,iftheplayerstrusteachother,theycanbothcooperateandavert ifbothweretocooperate. thecooperatorwouldsuerconsiderably.thismakesthelocallyrationalchoice buildupinasettingwheretheplayershavetorepeatlyinteractwitheachother. amutualdefectionwherebothsuer.ontheotherhand,suchtrustcanonly Ourobservationisthatareputationmechanismsustainsrationalcooperation, Theprisoner'sdilemmaisintimatelyrelatedtotheevolutionoftrust.On becausethegoodplayersarerewardedbysocietywhereasthebadplayersare penalized.boththerewardsandpenaltiesfromasocietyaregreaterthanfrom socialnetworkforinformationgathering[10,11].inourarchitecture,eachuseris anindividual. associatedwithapersonalagent.usersposequeriestotheiragents.thequeries whomtosendthequery.afterconsultationwiththeuser,theagentsendsthe bytheuserarerstseenbyhisagentwhodecidesthepotentialcontactsto Theproposedapproachbuildson(andappliesin)ourworkonconstructinga querytotheagentsforotherlikelypeople.theagentwhoreceivesaquerycan decideifitsuitsitsuserandlettheuserseethatquery.inadditiontoorinstead

ofjustforwardingthequerytoitsuser,theagentmayrespondwithreferralsto otherusers. alimitonthenumberofreferralsrequested.aresponsemayincludeananswer incomingquery.areferraldependsonthequeryandonthereferringagent's orareferral,orboth,orneither(inwhichcasenoresponseisneeded).an agentanswersonlyifitisreasonablycondentthatitsexpertisematchesthe Aqueryincludesthequestionaswellastherequester'sIDandaddressand modelofotheragents;areferralisgivenonlyifthereferringagentplacessome itsmodeloftheexpertiseoftheansweringagent,anditsmodelsofanyagent evaluatingtheexpertiseoftheagentwhogavetheanswer.thisevaluationaects itup.whentheagentreceivesananswer,itusestheanswerasabasisfor trustintheagentbeingreferred. whomayhavegivenareferraltothisansweringagent.ingeneral,theoriginating Whentheoriginatingagentreceivesareferral,itdecideswhethertofollow agentmaykeeptrackofmorepeersthanhisneighbors.periodicallyhedecide 4ReputationRatingandPropagation whichpeerstokeepasneighbors,i.e.,whichareworthremembering. Inourapproach,agentAassignsaratingtoagentBbasedon(1)itsdirect A'sratingofthoseneighbors.Thesecondaspectmakesourapproachasocialone observationsofbaswellas(2)theratingsofbasgivenbyb'sneighbors,and andenablesinformationaboutreputationstopropagatethroughthenetwork. sources.however,suchapproachesdonotconsiderthereputationsofthewitnessesthemselves.clearly,theweightassignedtoaratingshoulddependon thereputationoftherater.moreover,reputationratingscannotbeallowedto asimplisticapproachthatdirectlycombinestheratingsassignedbydierent Traditionalapproacheseitherignorethesocialaspectsaltogetheroremploy Denition1.Ti(j)tisthetrustratingassignedbyagentitoagentjattime increaseadinnitum.toachievetheabove,werstdeneanagent'sratingof t.werequirethat?1<ti(j)t<1andti(j)0=0. anotheragent.initially,theratingiszero. Cooperationbytheotheragentgeneratesapositiveevidenceanddefection anegativeevidence.thus0and0.toprotectthosewhointeract reputations,meaningthatreputationsshouldbehardtobuildup,buteasyto withanagentwhocheatssomeofthetime,wetakeaconservativestancetoward Eachagentwilladaptitsratingofanotheragentbasedonitsobservation. teardown.thiscontrastswithmarsh[5],whereanagentmaycheatasizable fraction(20%)ofthetimebutstillmaintainamonotonicallyincreasingreputation.wecanachievethedesiredeectbyrequiringthatjj<jj.weusea simpleapproachtocombineinevidencefromrecentinteractions. bythefollowingtableanddependsontheprevioustrustrating. Denition2.Afteraninteraction,theupdatedtrustratingTi(j)t+1isgiven

Ti(j)tCooperationbyj >0Ti(j)t+(1?Ti(j)t) <0(Ti(j)t+)=(1?minfjTi(j)tj;jjg)Ti(j)t+(1+Ti(j)t) Defectionbyj =0 (Ti(j)t+)=(1?minfjTi(j)tj;jj)g fortrust. Denition3.Foragenti:?1!i1and?1i1,where!ii. Ti(j)!iindicatesthatitrustsjandwillcooperatewithj;Ti(j)iindicates FollowingMarsh[5],wedeneforeachagentanupperandalowerthreshold thatimistrustsjandwilldefectagainstj;i<ti(j)<!imeansthatimust decideonsomeothergrounds. 4.1PropagationofReputationRating Eachagenthasasetofpotentiallychangingneighborswithwhomitmaydirectly interact.howanagentevaluatesthereputationsofotherswilldependinpart onthetestimoniesofitsneighbors.thisnaturallyleadstotheideaofareferral chain. Denition4.=hA0;:::;Aniisa(possible)referralchainfromagentA0to agentan,whereai+1isaneighborofai. Denition5.xy=if(x0^y0)thenxyelse?jxyj A0willuseareferralchaintoAntocomputeitsratingT0(n)towardsAn. chainisnegative.below,let=ha0;:::;anibeareferralchainfromagenta0 Wedeneatrustpropagationoperator,. toagentanattimet.wenowdenetrustpropagationoverareferralchain. Denition6.Foranyk,0kn,T0(k)t=T0(1)t:::Tk?1(k)t Inotherwords,theleveloftrustpropagatedoveranegativelinkinareferral agentsonthechain.forthisreason,wetermthepenultimateagentthewitness. denedase0(k)t=t0(k)ttk(k+1)t.herekisthewitnessofthistestimony. Denition7.Atestimonyforagent0fromagentkrelativetoachainis Thepenultimateagentonareferralchainhasdirectevidenceofthelast onlyifagentkistrusted,i.e.,t siderwitnessesreliableaslongastheyhaveapositivetrustrating. reliable.soastoallowtestimonyfromweakagentstobecombinedin,wecon- Denition8.Foragentiattimet,atestimonyfromagentkisreliableifand Testimonyfromawitnessisusedwhenthewitnessisconsideredsuciently case,wechooseareferralchainthatyieldsthehighesttrustratingfork. Denition9.Foragenti,atestimonyfromagentkwithrespecttoreferral Tworeferralchains1and2maypassthroughthesameagentk.Inthis i(k)t>0. chain1ismorereliablethanwithrespecttoreferralchain2ifandonlyif1 yieldsahighertrustratingforagentk,i.e.,t1 i(k)t2 i(k).

4.2IncorporatingTestimoniesfromDierentWitnesses theratingbyagivenagent.first,toeliminatedoublecountingofwitnesses,we Wenowshowhowtestimoniesfromdierentagentscanbeincorporatedinto distinctifandonlyifthewitnessesofalltestimoniesinearedistinct,i.e., jfe1w;:::;elwgj=l. denedistinctsetsoftestimonies.(ewreferstothewitnessoftestimonye). Denition10.AsetoftestimoniesE=fE1;:::;ELgtowardsagentnis fromthatwitness.noticethattheindividualwitnessesdonothavetobetrusted allthetrustabletestimonies,andforanywitness,itcontainsthebesttestimony greaterthan!ifortheirtestimonytobeused. Themaximallyreliabledistinct(MRD)subsetofasetoftestimoniescontains distinct,ve,and(8e:(e2e^te Denition11.VisaMRDsubsetofasetoftestimoniesEifandonlyifVis wecomputetheaverageoftestimoniesfromv:e=1=lpjvj Ew^TV GivenasetoftestimoniesEaboutAn,werstnditsMRDsubsetV.Next i(vw)te i(ew))). i(ew)>0))(9v:v2v^vw= agenta0willupdateitstrustratingofagentanasfollows(allratingsareat timetexceptwherespecied). when thent0(n)t+1= i=1vi.therefore, T0(n)andEarepositive oneoft0(n)andeisnegativet0(n)+e=(1?minfjt0(n)j;jejg) T0(n)andEarenegative T0(n)+E(1?T0(n)) 4.3Gossip T0(n)+E(1+T0(n)) IfanagentAencountersabadpartnerBduringsomeexchange,Awillpenalize BbydecreasingitsratingofBbyandinformingitsneighbors.Anagentwho receivesthisinformationcancombineitintoitstrustmodelofb. Denition12.SupposeagentireceivesamessageTk(n)(fromagentkabout isprocessedincrementally. propagatearumorwithouthavingbeenexplicitlyqueried.forthisreason,gossip Gossipisdierentfromtheusualreferralprocess,becauseanagentcan agentn).ifti(k)isnegative,theniignoresthemessage.ifti(k)ispositive, thenagentiupdatesitstrustratingofagentnasfollows. whenti(n)andtk(n)thenti(n)t+1= arebothpositive arebothnegative haveoppositesigns Ti(n)=Ti(n)+Ti(k)Tk(n)(1?Ti(n)) Ti(n)+Ti(k)Tk(n)(1+Ti(n)) (Ti(n)+Ti(k)Tk(n))=(1?minfjTi(n)j;jTi(k)Tk(n)jg)

Wenowdescribeandformalizesomeimportantpropertiesoftrust. 4.4PropertiesofTrust 1.Symmetry Ingeneral,symmetrywillnothold,becauseanagentmaytrustanothermore thanitistrustedback.however,whentheagentsaretrustworthy,through 2.Transitivity haveforanytwoagentsaxanday,tx(y)tty(x)twhent!1. ifoneoftheagentsdoesn'tactinatrustworthymanner,theotheragent willbeforcedtopenalizeit,leadingtolowmutualtrust.forthisreason,we repeatedinteractions,theywillconvergetohighmutualtrust.conversely, 3.Self-reinforcement Trustisnottransitive,butthefollowingwillholdifxisarationalagent: Trustisself-reinforcing,becauseagentsactpositivelywiththosewhomthey (Tx(y)t>Tx(z)t)^(Tx(z)t>Tx(w)t))(Tx(y)t>Tx(w)t) trust.theconverseistrue,asbelowacertaintrust,individualstendto conrmtheirsuspicionsofothers[9].therstpartofthefollowingruleis basedontheideathatiftrustbetweentwoagentsisinitiallyabove!,then Between!and,anythingcanhappen[5]. willtendnottocooperatewitheachotherwhateverthesituation,thus thetrustbetweenthosetwoagentswillnotdecreasebelowthatthreshold. reinforcingtheother'sopinionaboutthemasnon-cooperativeandunhelpful. Theconverseistrue,sinceifbothagentstrusteachotherbelow,they {If(Tx(y)t>!x)^(Ty(x)t>!y)then 4.Propagation {If(Tx(y)t<x)^(Ty(x)t<y)then (Tx(y)t+1Tx(y)t)^(Ty(x)t+1Ty(x)t) notknowz.howmuchxtrustszshoulddependonhowmuchxtrustsy, Considerthreeagentsx,y,andz.Ifxknowsyandyknowsz,butxdoes (Tx(y)t+1Tx(y)t)^(Ty(x)t+1Ty(x)t) andhowmuchytrustsz.thefollowingrulewillholdifxisrational. Asimpleformulafordeterminingtrustthatsatisestheaboveconstraint,is (Tx(z)t+1Tx(y)t)^(Tx(z)t+1Ty(z)t) 5ExperimentsandResults Tx(z)t+1=Tx(y)tTy(z)t manyagentsknowthegivenagent,howmanyagentsitknows,whichcommunity Inoursimulatedsetup,eachagenthasaninterestvector,anexpertisevector, itbelongsto,andsoon.inourcase,theneighbormodelskeptbyanagentare andmodelsofseveralneighbors.ingeneral,theneighbormodelsdependonhow thegivenagent'srepresentationoftheotheragents'expertiseandreputation.

generatedasvectorsbyperturbingtheinterestvectorofthegivenagent.the dependingonitsinterests. motivationforthisistocapturetheintuitionthatanagentwillproducequeries Anagent'squeriesaregeneratedbasedonitsinterestvector.Thequeriesare vector,orrefertootheragentsitknows.theoriginatingagentcollectsallpossible referrals,andcontinuestheprocessbycontactingsomeofthesuggestedreferrals. Atthesametime,itchangesitsmodelsforotheragents. Whenanagentreceivesaquery,itwilltrytoansweritbasedonitsexpertise vectorsofdimension5.theagentssendqueries,referrals,andresponsestoone another,allthewhilelearningabouteachothers'interestandexpertisevectors. Theagentsarelimitedinthenumberofneighborstheymayhave inourcase thelimitis4. Ourexperimentsinvolvebetween20and60agentswithinterestandexpertise 5.1Metrics Wenowdenesomeusefulmetricsinwhichtointuitivelycapturetheresultsof ourexperiments. wherenisthenumberofagentswhoknowagentai.wesaythatagentak agentsisgivenbyr(ai):r(ai)=1=npnj=1tj(ai) Denition13.TheaveragereputationofanagentAifromthepointofother wherenisthetotalnumberofagents. Denition14.Theaveragereputationofallagentsis:R=1=NPNi=1r(Ai), knowsagentaiifandonlyifaiisaneighborofak. 5.2SelectionofRewardsandPenalties Figure1illustratesthechangeoftrustratingsdependingondierentvaluesof Thisaverageisametricfordeterminingthestabilizationofacommunity. and.partaappliestoanewagentwhoinitiallyhasatrustof0,butbuildsup whoisalreadywell-trusted;partcappliestoanuntrustedagentwhothrough theratingthroughpositiveinteractions;partbappliestoacooperativeagent repeatedpositiveinteractionsbecomestrusted;partdappliestoanewagent whoseratingfallsbecauseofnegativeinteractions;partedescribesatrusted untrustedagentwhobecomesfurtheruntrustedbecauseofdefections. agentwhobecomesuntrustedbecauseofdefections;and,partfappliestoan betheratiobetweenthenumberofcooperationsanddefections.byappropriatelyselectingtheratingsofand,wecanlet!1.assumetheinitial trustratingofagentaiis0:6.let=5;10;20.figure2displaysthechangeof trustrating.noticethattrustbuiltupthroughseveralpositiveinteractionsis Consideranagentwhocooperatesanddefectsondierentinteractions.Let lostthroughevenasingledefection.

Part A. T 0 =0,α=0.05,0.1,0.2 1 Part B. T 0 =0.6,α=0.05,0.1,0.2 1 Part C. T 0 = 0.6,α=0.05,0.1,0.2 0.8 0.8 0.6 0.4 0.2 0.9 0.8 0.7 0.6 0.4 0.2 0 0.2 0.4 0 0 5 10 0.6 0 5 10 0.6 0 5 10 Part D. T 0 =0,β= 0.1, 0.2, 0.3 0 Part E. T 0 =0.6,β= 0.1, 0.2, 0.3 1 Part F. T 0 = 0.6,β= 0.1, 0.2, 0.3 0.6 0.2 0.5 0.7 Fig.1.Selectionofand,where=0:05(0?0),0:1(0?:0),0:2(0??0)and= 0.4?0:1(0x0),?0:2(0+0),?0:3(00) 0 0.8 0.6 0.5 0.9 0.8 5.3AvoidingUndesirableAgents 1 1 1 0 5 10 0 5 10 0 5 10 Ourmechanismquicklylowersthereputationsofselshagents.Considerthe agentsax,ay,andazknowhim.theirinitialratingstowardsaware0:4,0:5, followingexample.assumeagentawisanon-cooperativeagent,andonlythree and0:6,respectively. agentaxwilldisseminateitsobservationofagentawthroughoutthesocialnetwork.eventuallytheaveragereputationofagentawmaydecreasetoalowlevel. Thisisthepowerofreferrals.Figure3experimentallyconrmsourhypothesis. 1,agentAwdefectsagainstagentAx.Let=0:05and=?0:3.Accordingto theformulaforupdatingtrust,tx(w)=(0:4+(?0:3))=(1?minj0:4j;j?0:3j)= 0:1=0:7=0:1429.Thenewreputationoftheagentisr(Aw)=0:413.Moreover, SotheaveragereputationofagentAwattime0is0:5.However,sayattime continuallyintroduceandremovethemselvesfromthenetwork.toevaluatehow 5.4IntroducingNewAgents ourapproachaccommodateschangesofthisvariety,webeginwithastable Clearly,asocialnetworkwillnotremainstableforlong,becauseagentswill thenewagentwouldhavetokeepcooperatingreliablyorelsebeostracizedearly. networkandintroduceanewagentrandomlyintoit.thenewagentisgiven randomneighbors,andalloftheirtrustratingstowardsthisnewagentarezero. AssumeR=0:637attimet.Inordertobeembeddedintothesocialnetwork,

1 0.8 0.6 0.4 Fig.2.Changeoftrustfor=5(0?0),10(0?:0),20(0?+0)when=0:05and=?0:3 0.2 0 0.2 0.4 0 2 4 6 8 12 14 16 18 20 Times interactions 0.5 0.45 Average reputation 0.4 0.35 Fig.3.AveragereputationofagentAwforN=30,=0:05and=?0:3 0.3 0.25 0.2 agents,thenewagentcanhaveitsaveragereputationincreasesteadily.figure4 0.15 0 20 40 60 80 100 120 140 160 180 200 conrmsthishypothesis. Itsinitialthresholdforcooperatingislow.Byfrequentlycooperatingwithother Numbers of messages 6Discussion Althoughwepresentourresultsinthecontextofelectroniccommunities,our oftheotheragents.theabilitytodealwithselsh,antisocial,orunreliable mentcanhelptheagentsnessetheirinteractionsdependingonthereputations agentsaretrustworthyandreliable.approachesforexplicitreputationmanage- approachappliestomultiagentsystemsingeneral.mostcurrentmultiagentsystemsassumebenevolence,meaningthattheagentsimplicitlyassumethatother agentscanleadtomorerobustmultiagentsystems.

0.35 0.3 Average reputation 0.25 0.2 Fig.4.AveragereputationofnewagentAnewforN=30,=0:05and=?0:3 0.15 0.1 0.05 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 bymaliciousagents.itreliesonlyontherebeingalargenumberofagentswho withothers.however,itdoesnotfullyprotectagainstspuriousratingsgenerated Ourpresentapproachadjuststheratingsofagentsbasedontheirinteractions Numbers of messages agents.thisisnotideal,butnotanyworsethandemocraticruleinhuman oerhonestratingstooverridetheeectoftheratingsprovidedbythemalicious societies.democraticsocietiescannotguaranteethatamaliciousrulerwon'tbe ofthepopulationintheratingprocess. elected,buttheyreducethechanceofsuchaneventbyengagingalargefraction aswellasofcommunityformation.wealsowanttostudytheevolutionary situationswheregroupsofagentsconsiderratingschemesforotheragents.the purposeisnotonlytostudyalternativeapproachesforachievingmoreecient communities,butalsototestifourmechanismisrobustagainstinvasionand, Infuturework,weplantostudythespecialproblemsoflyingandrumors hence,morestable. References 3.LennyFoner.Yenta:Amulti-agent,referral-basedmatchmakingsystem.InProceedingsofthe1stInternationalConferenceonAutonomousAgents,pages301{ sellinggoods.inproceedingsofthe1stinternationalconferenceonthepractical 1.RobertAxelrod.TheEvolutionofCooperation.BasicBooks,NewYork,1984. 2.AnthonyChavezandPattieMaes.Kasbah:Anagentmarketplaceforbuyingand ApplicationofIntelligentAgentsandMultiagentTechnology(PAAM'96),1996. 4.RohitKhareandAdamRifkin.Weavingaweboftrust.WorldWideWeb,2(3):77{ 5.P.StevenMarsh.FormalisingTrustasaComputationalConcept.PhDthesis, 307,1997. 6.LarsRasmussonandSverkerJanson.SimulatedsocialcontrolforsecureInternet 112,1997. DepartmentofComputingScienceandMathematics,UniversityofStirling,April commerce.inproceedingsoftheworkshoponnewsecurityparadigms,1996. 1994.

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