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This paper provides theoretical bounds for empirical game theoreticalanalysis of complex multi-agent interactions. We provide insights in theempirical meta game showing that a Nash equilibrium of the meta-game is anapproximate Nash equilibrium of the true underlying game. We investigate andshow how many data samples are required to obtain a close enough approximationof the underlying game. Additionally, we extend the meta-game analysismethodology to asymmetric games. The state-of-the-art has only consideredempirical games in which agents have access to the same strategy sets and thepayoff structure is symmetric, implying that agents are interchangeable.Finally, we carry out an empirical illustration of the generalised method inseveral domains, illustrating the theory and evolutionary dynamics of severalversions of the AlphaGo algorithm (symmetric), the dynamics of the ColonelBlotto game played by human players on Facebook (symmetric), and an example ofa meta-game in Leduc Poker (asymmetric), generated by the PSRO multi-agentlearning algorithm.

A Generalised Method for Empirical Game Theoretic Analysis