Fast Convergence in the Double Oral Auction

  • Sepehr Assadi
  • Sanjeev Khanna
  • Yang Li
  • Rakesh Vohra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9470)

Abstract

A classical trading experiment consists of a set of unit demand buyers and unit supply sellers with identical items. Each agent’s value or opportunity cost for the item is their private information and preferences are quasi-linear. Trade between agents employs a double oral auction (DOA) in which both buyers and sellers call out bids or offers which an auctioneer recognizes. Transactions resulting from accepted bids and offers are recorded. This continues until there are no more acceptable bids or offers. Remarkably, the experiment consistently terminates in a Walrasian price. The main result of this paper is a mechanism in the spirit of the DOA that converges to a Walrasian equilibrium in a polynomial number of steps, thus providing a theoretical basis for the above-described empirical phenomenon. It is well-known that computation of a Walrasian equilibrium for this market corresponds to solving a maximum weight bipartite matching problem. The uncoordinated but rational responses of agents thus solve in a distributed fashion a maximum weight bipartite matching problem that is encoded by their private valuations. We show, furthermore, that every Walrasian equilibrium is reachable by some sequence of responses. This is in contrast to the well known auction algorithms for this problem which only allow one side to make offers and thus essentially choose an equilibrium that maximizes the surplus for the side making offers. Our results extend to the setting where not every agent pair is allowed to trade with each other.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Sepehr Assadi
    • 1
  • Sanjeev Khanna
    • 1
  • Yang Li
    • 1
  • Rakesh Vohra
    • 1
  1. 1.University of PennsylvaniaPennsylvaniaUSA

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