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Bidding Heuristics for Simultaneous Auctions: Lessons from TAC Travel

  • Amy Greenwald
  • Victor Naroditskiy
  • Seong Jae Lee
Conference paper
  • 337 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 44)

Abstract

We undertake an experimental study of heuristics designed for the Travel division of the Trading Agent Competition. Our primary goal is to analyze the performance of the sample average approximation (SAA) heuristic, which is approximately optimal in the decision-theoretic (DT) setting, in this game-theoretic (GT) setting. To this end, we conduct experiments in four settings, three DT and one GT. The relevant distinction between the DT and the GT settings is: in the DT settings, agents’ strategies do not affect the distribution of prices. Because of this distinction, the DT experiments are easier to analyze than the GT experiments. Moreover, settings with normally distributed prices, and controlled noise, are easier to analyze than those with competitive equilibrium prices. In the studied domain, analysis of the DT settings with possibly noisy normally distributed prices informs our analysis of the richer DT and GT settings with competitive equilibrium prices. In future work, we plan to investigate whether this experimental methodology—namely, transferring knowledge gained in a DT setting with noisy signals to a GT setting—can be applied to analyze heuristics for playing other complex games.

Keywords

Multiagent System Marginal Utility Bidding Strategy Clearing Price Sample Average Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Amy Greenwald
    • 1
  • Victor Naroditskiy
    • 1
  • Seong Jae Lee
    • 2
  1. 1.Department of Computer ScienceBrown University 
  2. 2.Department of Computer ScienceUniversity of Washington 

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