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Agent-mediated Electronic Commerce

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Tete-_at_-Tete (MIT Media Lab) Negotiates across multiple ... http://www.ebay.com/aw. http://auction.eecs.umich.edu. http://ecommerce.media.mit.edu/tete-a-tete ... – PowerPoint PPT presentation

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Title: Agent-mediated Electronic Commerce


1
Agent-mediatedElectronic Commerce
  • introduction

Luk Stoops programming laboratory VUB
2
Consumer Buying Behavior Model
  • Need Identification
  • Product Brokering
  • Merchant brokering
  • Negotiation
  • Purchase and Delivery
  • Product Service and Evaluation

3
Agent Systems
4
Need Identification
  • Becoming aware of unmet need
  • Stimulating trough product information
  • Problem Recognition
  • (Engel-Blackwell model)
  • Agents
  • alternate publicity
  • personalized publicity
  • ad busters

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Product Brokering
  • What to buy
  • Critical evaluation of retrieved product
    information
  • Agents
  • allow shoppers to specify constraints on a
    products features
  • feature filtering
  • recommend products via word of mouth
  • collaborative filtering

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Merchant brokering
  • Where to buy
  • BargainFinder
  • request price from 9 merchant Web sites
  • 1/3 blocked all of his requests
  • Jango
  • request originated from consumers browser
  • Kasbah
  • distributed trust and reputation mechanism

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Negotiation
  • Auctions on the web
  • eBay
  • On Sale
  • Yahoo
  • gt90 active online auctions
  • Business-to-business transactions
  • Fastparts (semiconductor)
  • FairMarket (computer)

19
Auctions
  • Hostile characteristics
  • first-price open-cry
  • winning bid gt market valuation
  • Short term benefit
  • long-term detriment

20
Auctions Disadvantages
  • Bids are non-retractable
  • Products are non-returnable
  • Long delay between
  • negotiation
  • Purchase and delivery
  • Only the highest bidder(s) can purchase
  • Shills !
  • Buyer coalitions !

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AuctionBot
  • General purpose Internet Auction server
  • University of Michigan
  • Start a new auction
  • Bid in an existing auction.
  • Facilities for
  • examining ongoing auctions
  • inspecting your own account activity
  • Free of charge.

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Kasbah Buying Agents
  • Product description
  • Minimum price
  • Maximum price
  • Best price so far
  • Time constraints
  • Report activities
  • Product condition
  • Locality
  • Minimum reputation
  • Horrible
  • Difficult
  • Average
  • Good
  • Great
  • Strategy

32
Buying Agents Strategies
33
Kasbah Selling Agents
  • Product description
  • Initial price
  • Lowest price
  • Time constraints
  • Report activities
  • Product condition
  • Locality
  • Minimum reputation
  • Horrible
  • Difficult
  • Average
  • Good
  • Great
  • Strategy

34
Kasbah Find Agents
  • Monitor market for specific products
  • timespan
  • price domain
  • Buying agents monitor
  • Selling agents monitor

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Generic - Comparative
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Purchase and Delivery
  • Security agents
  • Agents monitoring
  • Production
  • Delivery

45
Tete-_at_-Tete (MIT Media Lab)
  • Negotiates across multiple terms
  • warranty length and options
  • shipping time and cost
  • service contract
  • return policy
  • quantity
  • accessories
  • payment options
  • loan options

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Reputation systems
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Beginners Reputation
  • It is relatively easy to adopt a new
  • or change one's identity.
  • If 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.
  • Desirable 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
  • Even if a user starts receiving very low
    reputation ratings, he can improve his status
    later at almost the same rate as a beginner.
  • If 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
  • Two friends might decide to perform some dozens
    of fake transactions, rating each other with
    perfect scores so as to both increase their
    reputation value.
  • Even 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.

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Desiderata Reputation Systems
  • Ratings 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.
  • Reputation values of the users should not be
    allowed to increase at infinitum
  • eBay a seller may cheat 20 of the time but he
    can still maintain a monotonically increasing
    reputation value.

53
System Memory
  • The larger the number of ratings used in the
    evaluation of reputation values the highest the
    predictability of the mechanism it gets.
  • However, 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
  • New user minimum reputation
  • Reputation never under that minimum
  • Ratings after each transaction
  • Two users may rate each other only once
  • Users with high reputation experience much
    smaller rating changes

55
SporasReputation Evolution
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Trusting Friends of Friends(Histos)
57
- Value of other user- Weight received (older
version)
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- Value of other user- Weight received
59
- Value of the two users receiving an average
rating
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- Value of the user rated- Weight received
(Rater has 1500)
61
Product Service and Evaluation
  • Agent based
  • Distributed Reputation mechanism
  • Distributed trust mechanism
  • Collaborative rating among the consumers
  • Personalized evaluation of the various ratings
    assigned to each consumer or merchant

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Recommender Systems
  • Content-based filtering
  • keyword-based
  • extracting semantic information
  • Collaborative-based filtering
  • consumers ranking
  • Constraint-based filtering
  • constraint satisfaction problem (CSP)
  • scheduling - planning - configuration

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Me
65
Conclusions a New Game
  • More fair prices (in 87 lower, EY study)
  • Increased efficiency
  • First movers are long-term winners
  • Not playing losing
  • Brands less important
  • Knowing the customer owning him

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