Chain: a dynamic double auction framework for matching patient agents

  • Authors:

  • Jonathan Bredin;David C. Parkes;Quang Duong

  • Affiliations:

  • Dept. of Mathematics and Computer Science, Colorado College, Colorado Springs, CO;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2007

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Abstract

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, CHAIN, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the CHAIN construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of CHAIN when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with "zero-intelligence plus"-style learning agents. CHAIN-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.