Chain: A Dynamic Double Auction Framework for Matching Patient Agents
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.
Cite
@article{arxiv.1111.0046,
title = {Chain: A Dynamic Double Auction Framework for Matching Patient Agents},
author = {J. L. Bredin and Q. Duong and D. C. Parkes},
journal= {arXiv preprint arXiv:1111.0046},
year = {2011}
}