Chain: A Dynamic Double Auction Framework for Matching Patient Agents
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CitationBredin, Jonathan, David C. Parkes, and Quang Duong. 2007. Chain: A dynamic double auction framework for matching patient agents. Journal of Artificial Intelligence Research 30(1): 133-179.
AbstractIn 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:3967583
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