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Agents that buy and sell

Agents that buy and sell. Maes, Guttman, Moukas MIT Media Lab. Overview. Physical vs. digital negotiations General agents overview Current electronic purchases Software agents properties and types Agents as mediators in e-commerce Future directions. Physical vs. digital negotiations.

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Agents that buy and sell

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  1. Agents that buy and sell • Maes, Guttman, Moukas • MIT Media Lab

  2. Overview • Physical vs. digital negotiations • General agents overview • Current electronic purchases • Software agents properties and types • Agents as mediators in e-commerce • Future directions

  3. Physical vs. digital negotiations • “Real-world negotiations accrue transaction costs that may be too high for either consumers or merchants” • “At the speed of bits, agents will strategically form and reform coalitions to bid on contracts and leverage economies of scale”

  4. General agents overview • 1st generation agents • Filter information • Match people w/similar interests • Automate repetitive behavior • 2nd generation • E-commerce ==> revolutionize • business-to-business • business-to-consumer • consumer-to-consumer

  5. Current electronic purchases • Automate • Information • Products, vendors • Orders, payment • Merchandise buyer: manually • Seek merchants, products • Enter purchase & payment info

  6. Software agents properties and types • Personalized • Continuously running • Semi autonomous • ==> Optimize buying experience • E.g., monitoring agents …

  7. E.g., monitoring agents • Monitor • Quantity • Usage patterns • Invoke buying agent when low

  8. Buying agents • Collect info: vendors, products • Evaluate offerings • Make decision to investigate: merchants, products • Negotiate terms of transaction w/merchant • Place orders • Make auto payments

  9. Agents as mediators in e-commerce • Buying behavior models & theories • Six fundamental stages of buying process • Role of agents as mediators in e-commerce • Technologies / techniques in agents • Negotiations • Agent systems

  10. Buying behavior models & theories • CBB: consumer buying behavior • Other theories and models • Nicosia • Howard-Sheth • Gugel-Blackwell • Bettman info-processing • Andreasen

  11. Buying behavior models & theories (cont.) • Approximation & simplification • Define similar fundamental stages • Help identify where agent technology apply • Categorize existing systems • Buying process: Six fundamental stages

  12. Six fundamental stages of buying process • Need identification • Product brokering • Merchant brokering • Negotiation • Pruchase and delivery • Product service and evaluation

  13. Need identification • Buyer aware of unmet need • Can motivate through product information

  14. Product brokering • Retrieve info: help determine what to buy • Evaluate product alternatives • ==> Buyer-provided criteria • ==> “Consideration set” of products

  15. Merchant brokering • Combine consideration set w/merchant-specific info • Evaluate merchant alternatives subject to buyer-provided criteria • Price • Warranty • Availability • Delivery time • Reputation

  16. Negotiation • How to settle on terms of transaction • Fixed • Common in consumer products • Negotiable • Common in business-to-business • Duration and complexity

  17. Pruchase and delivery • End of negotiation • Other time (later?)

  18. Product service and evaluation • Post-purchase • Product service • Customer service • Satisfaction evaluation • Overall experience with decision

  19. Role of agents as mediators in e-commerce

  20. Need identification • Repetitive • E.g., supplies • Predictable • E.g., habits • How … monitors

  21. Monitors • Continuously running • Monitor sets of • Sensors • Data streams • Take actions • When prespecified conditions apply • Examples …

  22. Examples • Stock market • E-commerce • Amazon.com • Notification agent (“eyes”) • New book by • author • category

  23. Product evaluation • PersonaLogic ... • Tete-a-Tete (T@T) ... • Firefly ...

  24. PersonaLogic • Define product features • Filter unwanted products • Constraints on features • Constraint-satisfaction engine • Return list of products • Satisfy shopper’s “hard constraints” • Prioritize by soft constraints

  25. Tete-a-Tete (T@T) • Comparable techniques • Multiattribute utility theory • Also • Merchant brokering • Negotiation

  26. Firefly • Automated “word-of-mouth” • Collaborative filtering • Compare ratings w/others’ • Identify “nearest neighbors” • Users w/similar tastes • Recommend products rated highly by neighbors • Not yet rated by shopper

  27. Firefly (cont.) • ==> Serendipitous finds • Opinions of like-minded people • Music • Books • More difficult to characterize • Web pages • Resraturants

  28. Technologies / techniques for product evaluation • Constrained-based ... • Collaborative filtering ... • Rule-based ... • Data mining ...

  29. Constrained-based • PersonaLogic • Tete-a-Tete

  30. Collaborative filtering • Firefly • Other

  31. Rule-based • Broadvision, Inc. • Personalize product offerings for individual customers

  32. Data mining • Patterns in customer purchasing behavior • Help customers find products • E.g., Engage

  33. Merchant evaluation • BargainFinder (Andersen Consulting) ... • Jango …

  34. BargainFinder (Andersen Consulting) • Online price comparison • 9 merchant Web sites (at least) • 1/3 blocked • Don’t compete on price only • Also value-added services • Others asked to be included • Want to compete on prices

  35. Jango ... • “Advanced BargainFinder” • Solve merchant-blocking • Requests originate from requestor’s site • Not agent’s

  36. Technologies / techniques for merchant evaluation • Current: build comparison shopping agent • Largely manual, tedious • Virtual database • E.g., Junglee, Inc. • Learning • ==> Semi auto composing of “wrappers” for Web sites • Future ...

  37. Future ... • XML • Mobile agents • ==> Comparison-shopping agents • Flexible • Open ended • Easier to implement

  38. Negotiations • Settle on • Price • Other terms of transaction

  39. Negotiations: current • Business-to-business • Yes • Retail • Mostly fixed • Only last 100 years

  40. Dynamic negotiation of product price • Benefits ... • Impediments ...

  41. Dynamic negotiations: benefits • Don’t need to determine a-priori value of goods, services • Take to marketplace • ==> Limited resources allocated fairly • To those who value them most

  42. Dynamic negotiations: impediments • E.g., auctions • Geographical colocation at auction place • Complicated, frustrating • Extended period • Not fit for impatient / time-constrained • Generally cost too high for both

  43. Dynamic negotiations in digital world • Impediments gone • E.g., • OnSale • eBay’s AuctionWeb • No geographic colocation • Yes: manage own negotiation strategies • Agent tech. can help

  44. Agent systems that negotiate • Auction Bot (U Mich) ... • Kasbah (MIT MediaLab) ... • Tete-a-Tete (MIT MediaLab) ...

  45. Auction Bot (U Mich) • Internet auction server • New auctions • Auction type • Parameters • Clearing times • Method for resolving tie bids • Number of sellers permitted

  46. Auction Bot (cont.) • Buyers & sellers bid • Multilateral distributive negotiation protocol • Advantage: API • ==> Users create own SW agents • Autonomously compete in AB marketplace • Users encode own bidding strategies

  47. Kasbah (MIT MediaLab) • Online • Multiagent • Consumer-to-consumer • User (buy or sell) • Create agent • Give strategic directions • Send off to centralized marketplace

  48. Kasbah (cont.) • Proactive • Seek out buyers / sellers • Negotiate on behalf of owners • Goal • Complete acceptable deal on behalf of user • Subject to set of user constraints

  49. Kasbah (cont.): user constraints • Initial bidding (asking) price • Lowest (highest) acceptable price • Date to complete • Restrictions on parties to negotiate with • Price change over time

  50. Kasbah (cont.) • After match, only valid action • Buying agents offer bid • No restriction on time, price • Selling agents • Binding “yes” • “No”

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