SNEAD: Solving Nash Equilibria with Adversarial Deviation

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Abstract/Contents

Abstract: We present SNEAD, an extension of an algorithmic framework by McKelvey (1998) under which one can reduce the problem of finding Nash equilibria in finite games to a tractable unified optimization problem. Specifically, we adapt the method to tractably optimize continuous-strategy games and improve asymptotic running time over finite-strategy games. We further prove that convergence of our algorithm to zero loss guarantees a Nash equilibrium in quasiconcave games. Experimentally, our algorithm achieves state of the art performance on a variety of games, even in the face of high-dimensional, complex payoff functions. Finally, we provide supplementary theoretical frameworks which further adapt SNEAD to locate symmetric equilibria or subgame-perfect equilibria with novel algorithmic efficiency.

DOI	https://doi.org/10.25740/jh499vx6167
Location	https://purl.stanford.edu/jh499vx6167

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Preferred citation: Healy, C. (2022). SNEAD: Solving Nash Equilibria with Adversarial Deviation. Stanford Digital Repository. Available at https://purl.stanford.edu/jh499vx6167

Stanford University, Department of Economics, Honors Theses

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