A grey-box approach to automated mechanism design

J. NIU; K. CAI; S. PARSONS; M. FASLI; X. YAO

doi:10.1016/j.elerap.2011.06.006

A grey-box approach to automated mechanism design

J. NIU, K. CAI, S. PARSONS, M. FASLI, X. YAO

Research output: Journal Publications › Journal Article (refereed) › peer-review

9 Citations (Scopus)

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. © 2011 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	24-35
Number of pages	12
Journal	Electronic Commerce Research and Applications
Volume	11
Issue number	1
Early online date	26 Jun 2011
DOIs	https://doi.org/10.1016/j.elerap.2011.06.006
Publication status	Published - Jan 2012
Externally published	Yes

Bibliographical note

This work was started as part of the first author’s doctoral research at the CUNY Graduate Center and continued as part of the first author’s post-doctoral research at the University of Birmingham and the University of Essex.

This is a revised and substantially extended version of a paper presented at the Twelfth International Workshop on Agent-Mediated Electronic Commerce (Niu et al. 2010a), an extended abstract of which was also presented at the Ninth International Joint Conference on Autonomous Agent and Multi-Agent Systems (Niu et al. 2010b).

Funding

The authors acknowledge financial support from the us nsf under Grant IIS-0329037, Tools and Techniques for Automated Mechanism Design, and from the uk epsrc under Grants GR/T10657/01 and GR/T10671/01, Market Based Control of Complex Computational Systems, and resources from the computational facility at the CUNY Graduate Center. Our grateful thanks go to the entrants to the Market Design game for releasing binaries of their market agents and to the anonymous reviewers for their valuable comments.

Keywords

Agent-based computational economics
CAT game
Double auction
Mechanism design
Trading agent competition

Access to Document

10.1016/j.elerap.2011.06.006

Cite this

@article{d5c2c405bbe24a14b18894929d652b80,

title = "A grey-box approach to automated mechanism design",

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. {\textcopyright} 2011 Elsevier B.V. All rights reserved.",

keywords = "Agent-based computational economics, CAT game, Double auction, Mechanism design, Trading agent competition",

author = "J. NIU and K. CAI and S. PARSONS and M. FASLI and X. YAO",

note = "This work was started as part of the first author{\textquoteright}s doctoral research at the CUNY Graduate Center and continued as part of the first author{\textquoteright}s post-doctoral research at the University of Birmingham and the University of Essex. This is a revised and substantially extended version of a paper presented at the Twelfth International Workshop on Agent-Mediated Electronic Commerce (Niu et al. 2010a), an extended abstract of which was also presented at the Ninth International Joint Conference on Autonomous Agent and Multi-Agent Systems (Niu et al. 2010b).",

year = "2012",

month = jan,

doi = "10.1016/j.elerap.2011.06.006",

language = "English",

volume = "11",

pages = "24--35",

journal = "Electronic Commerce Research and Applications",

issn = "1567-4223",

publisher = "Elsevier B.V.",

number = "1",

}

TY - JOUR

T1 - A grey-box approach to automated mechanism design

AU - NIU, J.

AU - CAI, K.

AU - PARSONS, S.

AU - FASLI, M.

AU - YAO, X.

N1 - This work was started as part of the first author’s doctoral research at the CUNY Graduate Center and continued as part of the first author’s post-doctoral research at the University of Birmingham and the University of Essex. This is a revised and substantially extended version of a paper presented at the Twelfth International Workshop on Agent-Mediated Electronic Commerce (Niu et al. 2010a), an extended abstract of which was also presented at the Ninth International Joint Conference on Autonomous Agent and Multi-Agent Systems (Niu et al. 2010b).

PY - 2012/1

Y1 - 2012/1

N2 - 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. © 2011 Elsevier B.V. All rights reserved.

AB - 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. © 2011 Elsevier B.V. All rights reserved.

KW - Agent-based computational economics

KW - CAT game

KW - Double auction

KW - Mechanism design

KW - Trading agent competition

UR - http://www.scopus.com/inward/record.url?scp=84857052163&partnerID=8YFLogxK

U2 - 10.1016/j.elerap.2011.06.006

DO - 10.1016/j.elerap.2011.06.006

M3 - Journal Article (refereed)

SN - 1567-4223

VL - 11

SP - 24

EP - 35

JO - Electronic Commerce Research and Applications

JF - Electronic Commerce Research and Applications

IS - 1

ER -

A grey-box approach to automated mechanism design

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this