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Multi-Agent Auction, Bidding and Contracting Support Systems

Multi-Agent Auction, Bidding and Contracting Support Systems. D.-J. Wu, Yanjun Sun FMEC May 11-12, 2000 Philadelphia. ABC in Human History. Marrying daughter in ancient China using bidding, Song Dynasty Auction women as wives in Babylonia, Fifth Century, B.C.

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Multi-Agent Auction, Bidding and Contracting Support Systems

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  1. Multi-Agent Auction, Bidding and Contracting Support Systems D.-J. Wu, Yanjun Sun FMEC May 11-12, 2000 Philadelphia

  2. ABC in Human History • Marrying daughter in ancient China using bidding, Song Dynasty • Auction women as wives in Babylonia, Fifth Century, B.C. • Farming contracting in ancient China, 452 B.C.

  3. Auction Literature • Tradition auction (single item) • English, Dutch • Sealed bid, open cry • First price winner and second price winner • Goods are storable • Combinatorial auction (multi item) • iBundle (AuctionBot) • Generalized Vickery auction

  4. ABC in Information Economy (Adapted from Vakrat and Seidmann, 1999)

  5. Roles and Examples of Agent Systems as Mediators in Electronic Commerce (Adapted from Guttman, Moukas, and Maes, 1999)

  6. eBAC Auction

  7. Research Questions: • Can artificial agents discover the equilibrium if it exists? • Can artificial agents learn reasonably good policies when facing automated markets? • What kind of mechanisms will induce coordination, cooperation and information sharing among agents?

  8. Blackboard Seller 1 Seller 2 Seller 3 Seller 1 Seller 2 Seller 3 Adaptive Learning Figure XXX: Myopic Bidding System Myopic Bidding System

  9. Agents Learning in Myopic System • Floating point representation. • Identical initial population. • Rule strength is current profit. • Memory size = 1. • Learning via genetic algorithms.

  10. Model for Myopic Bidding • Bidding Price • Bidding Capacity

  11. Technology and Capacity Parameters for the Three-Supplier Examples

  12. Price Bidding Model

  13. Nash Equilibria for Three-Supplier Normal Form Game

  14. Capacity Bidding Model

  15. Results: c = (10, 10, 18), K = (40, 40, 30)

  16. Dynamic Pure Strategy Price Bidding, Path Dependent (Ex. 1)

  17. Dynamic Pure Strategy Capacity Bidding, Path dependent (Ex. 1)

  18. Orthogonal Experiment * = 13, 13, 13 = 30, 30, 30 PD (16, 16, 16) (30, 30, 30) (21, 21, 21) PI (16, 16, 16) (30, 30, 30) (21, 21, 21) = 11, 21, 21 = 44, 23, 23 PD (21, 22, 22) (40, 31, 31) (No, No, No) PI (21, 22, 22) (40, 31, 31) (No, No, No) = 16, 16, 25 = 34, 34, 22 PD (No, No, 26) (31, 31, 31) (No, No, No) PI (No, No, 26) (31, 31, 31) (No, No, No) = 7, 12, 17 = 45, 26, 19 PD (No, No, 18) (39, 30, 30) (No, No, No) PI (No, No, 18) (38, 30, 30) (No, No, No) *=10,10,18 =40,40,30 PD (No, No, 19) (No, No, No) (18, 18, 19) PI (No, No, 19) (No, No, No) (18, 18, 19) * =10,12,14 =40,30,20 PD (14, 14, 15) (38, 29, 29) (No, No, No) PI (14, 14, 15) (38, 29, 29) (No, No, No) * Borrowed from WKZ.

  19. Summary of Myopic Price Bidding • No cooperation exists under any climate. • Bidding tends to have equilibrium under amenable climate. • No difference between path dependent and independent.

  20. Non-Myopic Bidding • No learning (Fixed strategy tournament) • One agent learning • All agents learning

  21. Strategy Profit 1 (R, R, R) (17089, 14500, 5982)  …   …   … 14 (N, N, N) (22091, 22091, 13623) …   …   … 26 (T, T, N) (22091, 22091, 13623) 27 (T, T, T) (22091, 22091, 13623) Tournament 1: Fixed Three-Strategy (Random, Nice, and Tit-for-Tat)

  22. 1 (R, R, R) (14718,13427,7705)   …  …   … 22 (N, N, N) (22091, 22091, 13623)   …   …   … 42 (T, T, N) (22091, 22091, 13623) 43 (T, T, T) (22091, 22091, 13623)   …   … … 64 (A, A, A) (10656, 10656, 934) Tournament 2: Fixed Four-Strategy (Random, Nice, Tit-for-Tat, and Nasty)

  23. One Agent Learning

  24. … …     … 65 (G, R, R) (26356, 9842, 4143) …   …   … 70 (G, N, N) (52800, 3857, 2379) … …   … 75 (G, T, T) (23115, 21483, 13248) …   …   … 80 (G, A, A) (17473, 5929, 9123) Tournament 3: One Agent Learning

  25. Tournament Strategy List 1 Strategy List 2 Strategy List 3 Seller 1 Seller 2 Seller 3 Strategy Discovery Figure XXX: Non-Myopic Bidding System Tournament 4: All Agents Learning

  26. Agent Learning in Non-Myopic System • Representation: Each rule specifies multi-period bidding strategy. • Randomly generated initial population. • Rule learning via genetic algorithms. • Rule strength is expected tournament profit.

  27. Model for Non-Myopic Bidding

  28. The Emergence of Trust • Learning to cooperate • Conditions for cooperation • Impact of climate

  29. Agent Learning in Dynamic Environment: Experiment 1

  30. Agent Learning in Dynamic Environment, Experiment 2 1 2 3 4 5 6 7 8 9 10 0% 54 54 54 54 54 54 54 54 54 54 25% 54 54 54 54 54 54 54 54 54 54 50% 54 34 48 55 53 55 53 34 55 53 75% 54 34 55 39 34 55 39 34 55 39 100% 54 34 55 39 55 39 55 39 55 39

  31. Profit Changing in Dynamic Environment, Experiment 2

  32. Ongoing Research • The ring of King Solomon • Agent communication • Computational principles of trust • Agent coalition and Bargaining • Role of Larmarcian learning

  33. Summary • Artificial agents are viable in automated marketplace. • Discover optimal bidding and contracting strategies in the equilibrium if exist. • Find better strategies in a complex dynamic environment where equilibrium do not exist. • The emergence of trust. • Depends on the auction mechanism: Capacity bidding induces cooperation. • Non-myopic bidding leads to cooperation while myopic bidding does not. • Climate has impact on agents cooperation.

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