Exposing market mechanism design trade-offs via multi-objective evolutionary search


Market mechanisms are a means by which resources in contention can be allocated between contending parties, both in human economies and those populated by software agents. Designing such mechanisms has traditionally been carried out by hand, and more recently by automation. Assessing these mechanisms typically involves them being evaluated with respect to multiple conflicting objectives, which can often be nonlinear, noisy, and expensive to compute. For typical performance objectives, it is known that designed mechanisms often fall short on being optimal across all objectives simultaneously. However, in all previous automated approaches, either only a single objective is considered, or else the multiple performance objectives are combined into a single objective. In this paper we do not aggregate objectives, instead considering a direct, novel application of multi-objective evolutionary algorithms (MOEAs) to the problem of automated mechanism design. This allows the automatic discovery of trade-offs that such objectives impose on mechanisms. We pose the problem of mechanism design, specifically for the class of linear redistribution mechanisms, as a naturally existing multi-objective optimisation problem. We apply a modified version of NSGA-II in order to design mechanisms within this class, given economically relevant objectives such as welfare and fairness. This application of NSGA-II exposes tradeoffs between objectives, revealing relationships between them that were otherwise unknown for this mechanism class. The understanding of the trade-off gained from the application of MOEAs can thus help practitioners with an insightful application of discovered mechanisms in their respective real/artificial markets.

Publication DOI: https://doi.org/10.1109/CEC.2013.6557742
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
Additional Information: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2013 IEEE Congress on Evolutionary Computation
Event Type: Other
Event Dates: 2013-06-20 - 2013-06-23
Uncontrolled Keywords: automated mechanism design,fairness,market based interaction,redistribution,resource allocation,welfare,Computational Theory and Mathematics,Theoretical Computer Science
ISBN: 978-1-4799-0453-2, 978-1-4799-0452-5
Full Text Link: http://ieeexplo ... rnumber=6557742
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2013
Authors: Chandra, Arjun
Allmendinger, Richard
Lewis, Peter R. (ORCID Profile 0000-0003-4271-8611)
Yao, Xin
Torresen, Jim



Version: Accepted Version

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