1. Agent-mediated Electronic Commerce introduction Luk Stoops programming laboratory VUB

2. Consumer Buying Behavior Model zNeed Identification zProduct Brokering zMerchant brokering zNegotiation zPurchase and Delivery zProduct Service and Evaluation

3. Agent Systems

4. Need Identification zBecoming aware of unmet need zStimulating trough product information zProblem Recognition y (Engel-Blackwell model) zAgents yalternate publicity ypersonalized publicity yad busters

8. Product Brokering zWhat to buy zCritical evaluation of retrieved product information zAgents yallow shoppers to specify constraints on a product’s features xfeature filtering yrecommend products via “word of mouth” xcollaborative filtering

14. Merchant brokering zWhere to buy zBargainFinder yrequest price from 9 merchant Web sites y1/3 blocked all of his requests zJango yrequest originated from consumer’s browser zKasbah ydistributed trust and reputation mechanism

18. Negotiation zAuctions on the web yeBay yOn Sale zYahoo y>90 active online auctions zBusiness-to-business transactions yFastparts (semiconductor) yFairMarket (computer)

19. Auctions zHostile characteristics zfirst-price open-cry ywinning bid > market valuation zShort term benefit zlong-term detriment

20. Auctions Disadvantages zBids are non-retractable zProducts are non-returnable zLong delay between ynegotiation yPurchase and delivery zOnly the highest bidder(s) can purchase zShills ! zBuyer coalitions !

29. AuctionBot zGeneral purpose Internet Auction server zUniversity of Michigan zStart a new auction zBid in an existing auction. zFacilities for yexamining ongoing auctions yinspecting your own account activity zFree of charge.

31. Kasbah Buying Agents zProduct description zMinimum price zMaximum price zBest price so far zTime constraints zReport activities zProduct condition zLocality z Minimum reputation yHorrible yDifficult yAverage yGood yGreat z Strategy

32. Buying Agents Strategies

33. Kasbah Selling Agents zProduct description zInitial price zLowest price zTime constraints zReport activities zProduct condition zLocality z Minimum reputation yHorrible yDifficult yAverage yGood yGreat z Strategy

34. Kasbah Find Agents zMonitor market for specific products ytimespan yprice domain zBuying agents monitor zSelling agents monitor

35. Generic - Comparative

44. Purchase and Delivery zSecurity agents zAgents monitoring yProduction yDelivery

45. Tete-@-Tete (MIT Media Lab) zNegotiates across multiple terms ywarranty length and options yshipping time and cost yservice contract yreturn policy yquantity yaccessories ypayment options yloan options

48. Reputation systems

49. Beginners Reputation zIt is relatively easy to adopt a new or change one's identity. zIf a user ends up having a reputation value lower than the reputation of a beginner, he would have an incentive to discard his initial identity and start from the beginning. zDesirable that while a user's reputation value may decrease after a transaction, it will never fall below a beginner's value.

50. Reputation Improving Rate zEven if a user starts receiving very low reputation ratings, he can improve his status later at almost the same rate as a beginner. zIf reputation = the arithmetic average of the ratings received since the user joined the system: users who perform relatively poorly in the beginning adopt a new identity to get rid of their bad reputation history.

51. Fake Transactions zTwo friends might decide to perform some dozens of fake transactions, rating each other with perfect scores so as to both increase their reputation value. zEven if we allow each user to rate another only once, another way to falsely increase one's reputation would be to create fake identities and have each one of those rate the user's real identity with perfect scores.

52. Desiderata Reputation Systems zRatings given by users with an established high reputation in the system should be weighted more than the ratings given by beginners or users with low reputations. zReputation values of the users should not be allowed to increase at infinitum yeBay: a seller may cheat 20% of the time but he can still maintain a monotonically increasing reputation value.

53. System Memory zThe larger the number of ratings used in the evaluation of reputation values the highest the predictability of the mechanism it gets. zHowever, since the reputation values are associated with human individuals and humans change their behavior over time it is desirable to disregard very old ratings.

54. Sporas zNew user: minimum reputation zReputation never under that minimum zRatings after each transaction zTwo users may rate each other only once zUsers with high reputation experience much smaller rating changes

55. Sporas Reputation Evolution

56. Trusting Friends of Friends (Histos)

57. - Value of other user - Weight received (older version)

58. - Value of other user - Weight received

59. - Value of the two users receiving an average rating

60. - Value of the user rated - Weight received (Rater has 1500)

61. Product Service and Evaluation zAgent based yDistributed Reputation mechanism yDistributed trust mechanism zCollaborative rating among the consumers zPersonalized evaluation of the various ratings assigned to each consumer or merchant

63. Recommender Systems zContent-based filtering ykeyword-based yextracting semantic information zCollaborative-based filtering yconsumers ranking zConstraint-based filtering yconstraint satisfaction problem (CSP) yscheduling - planning - configuration

65. Conclusions: a New Game zMore fair prices (in 87% lower, E&Y study) zIncreased efficiency zFirst movers are long-term winners zNot playing = losing zBrands less important zKnowing the customer = owning him

66. Literature zAgent-mediated Electronic Commerce: A Survey Robert H. Guttmann, Alexandros G. Moukas, Pattie Maes zCollaborative Reputation Mechanisms in Electronic Marketplaces Giorgos Zacharia, Alexandros Moukas, Pattie Maes zhttp://ecommerce.media.mit.edu zhttp://www.personalogic.com zhttp://www.firefly.com zhttp://www.jango.com zhttp://kasbah.media.mit.edu zhttp://www.ebay.com/aw zhttp://auction.eecs.umich.edu zhttp://ecommerce.media.mit.edu/tete-a-tete