Walverine: a Walrasian trading agent

  • Authors:

  • Shih-Fen Cheng;Evan Leung;Kevin M. Lochner;Kevin O'Malley;Daniel M. Reeves;Julian L. Schvartzman;Michael P. Wellman

  • Affiliations:

  • Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI;Artificial Intelligence Laboratory, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI

  • Venue:
  • Decision Support Systems - Special issue: Decision theory and game theory in agent design
  • Year:
  • 2005

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Abstract

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.