Constrained automated mechanism design for infinite games of incomplete information

Authors:
Yevgeniy Vorobeychik;Daniel M. Reeves;Michael P. Wellman
Affiliations:
Sandia National Laboratories, Livermore, USA 94551-0969;Beeminder LLC, Peoria, USA 61614-3732;Computer Science and Engineering, University of Michigan, Ann Arbor, USA 48109-2121
Venue:
Autonomous Agents and Multi-Agent Systems
Year:
2012

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Abstract

We present a functional framework for automated Bayesian and worst-case mechanism design, based on a two-stage game model of strategic interaction between the designer and the mechanism participants. At the core of our framework is a black-box optimization algorithm which guides the process of evaluating candidate mechanisms. We apply the approach to several classes of two-player infinite games of incomplete information, producing optimal or nearly optimal mechanisms using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we provide evidence that ours is a promising approach to parametrized mechanism design for infinite Bayesian games.