Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

TL;DR

This paper examines how to effectively explore strategies in empirical game-theoretic analysis, proposing a new evaluation scheme and a local search method for the minimum regret constrained profile that improves model construction.

Contribution

It introduces a robust evaluation method for strategy exploration in EGTA and proposes a local search approach for MRCP that outperforms existing methods.

Findings

The proposed evaluation scheme better reveals true learning performance.

The local search method for MRCP outperforms previous approaches.

Recent evaluation practices may lead to misleading conclusions.

Abstract

In empirical game-theoretic analysis (EGTA), game models are extended iteratively through a process of generating new strategies based on learning from experience with prior strategies. The strategy exploration problem in EGTA is how to direct this process so to construct effective models with minimal iteration. A variety of approaches have been proposed in the literature, including methods based on classic techniques and novel concepts. Comparing the performance of these alternatives can be surprisingly subtle, depending sensitively on criteria adopted and measures employed. We investigate some of the methodological considerations in evaluating strategy exploration, defining key distinctions and identifying a few general principles based on examples and experimental observations. In particular, we emphasize the fact that empirical games create a space of strategies that should be evaluated as a whole. Based on this fact, we suggest that the minimum regret constrained profile (MRCP) provides a particularly robust basis for evaluating a space of strategies, and propose a local search method for MRCP that outperforms previous approaches. However, the computation of MRCP is not always feasible especially in large games. In this scenario, we highlight consistency considerations for comparing across different approaches. Surprisingly, we find that recent works violate these considerations that are necessary for evaluation, which may result in misleading conclusions on the performance of different approaches. For proper evaluation, we propose a new evaluation scheme and demonstrate that our scheme can reveal the true learning performance of different approaches compared to previous evaluation methods.