Choosing Samples to Compute Heuristic-Strategy Nash

Auctions define games of incomplete information for which it is often too hard to compute the exact Bayesian-Nash equilibrium. Instead, the infinite strategy space is often populated with heuristic strategies, such as myopic bestresponse to prices. Given these heuristic strategies, it can be useful to evaluate the strategies and the auction design by computing a Nash equilibrium across the restricted strategy space. First, it is necessary to compute the expected payoff for each heuristic strategy profile. This step involves sampling the auction and averaging over multiple simulations, and its cost can dominate the cost of computing the equilibrium given a payoff matrix. In this paper, we propose two information theoretic approaches to determine the next sample through an interleaving of equilibrium calculations and payoff refinement. Initial experiments demonstrate that both methods reduce error in the computed Nash equilibrium as samples are performed at faster rates than naive uniform sampling. The second, faster method, has a lower metadeliberation cost and better scaling properties. We discuss how our sampling methodology could be used within experimental mechanism design