A grey-box approach to automated mechanism design

Authors:
J. Niu;K. Cai;S. Parsons;M. Fasli;X. Yao
Affiliations:
Department of Computer Science, Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA and CAISS, City College, CUNY, 137th Street and Convent Avenue, New York, NY 10031, USA;Department of Computer Science, Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA;Department of Computer Science, Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA and Department of Computer and Information Science, Brooklyn College, CUNY, 2900 Bedford Avenue, Br ...;School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK;School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Venue:
Electronic Commerce Research and Applications
Year:
2012

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Abstract

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 not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.