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Agents that buy and sell

Agents that buy and sell

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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”