Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Automated mechanism design: complexity results stemming from the single-agent setting
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Characterizing effective auction mechanisms: insights from the 2007 TAC market design competition
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
An Analysis of Entries in the First TAC Market Design Competition
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A grey-box approach to automated mechanism design
Electronic Commerce Research and Applications
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This paper presents an approach to automated mechanism design in the domain of double auctions. We describe a novel parameterized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach are able to win the Market Design Game against known, strong opponents.