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Resource Allocation Using Sequential Auctions

  • Craig Boutilier
  • Moisés Goldszmidt
  • Claire Monteleoni
  • Bikash Sabata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1788)

Abstract

Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and multiagent decision problems. When agents value resources in combination rather than in isolation, one generally relies on combinatorial auctions where agents bid for resource bundles, or simultaneous auctions for all resources. We develop a different model, where agents bid for required resources sequentially. This model has the advantage that it can be applied in settings where combinatorial and simultaneous models are infeasible (e.g., when resources are made available at different points in time by different parties), as well as certain benefits in settings where combinatorial models are applicable. We develop a dynamic programming model for agents to compute bidding policies based on estimated distributions over prices. We also describe how these distributions are updated to provide a learning model for bidding behavior.

Keywords

Optimal Allocation Markov Decision Process Prior Belief Bidding Strategy Combinatorial Auction 
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 2000

Authors and Affiliations

  • Craig Boutilier
    • 1
  • Moisés Goldszmidt
    • 2
  • Claire Monteleoni
    • 2
  • Bikash Sabata
    • 2
  1. 1.Dept. of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.Dept. of Computer ScienceStanford UniversityStanfordUSA

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