Abstract
We consider the problem of approximating the minmax value of a multi-player game in strategic form. We argue that in three-player games with 0-1 payoffs, approximating the minmax value within an additive constant smaller than ξ/2, where \(\xi = \frac{3-\sqrt5}{2} \approx 0.382\), is not possible by a polynomial time algorithm. This is based on assuming hardness of a version of the so-called planted clique problem in Erdős-Rényi random graphs, namely that of detecting a planted clique. Our results are stated as reductions from a promise graph problem to the problem of approximating the minmax value, and we use the detection problem for planted cliques to argue for its hardness. We present two reductions: a randomised many-one reduction and a deterministic Turing reduction. The latter, which may be seen as a derandomisation of the former, may be used to argue for hardness of approximating the minmax value based on a hardness assumption about deterministic algorithms. Our technique for derandomisation is general enough to also apply to related work about ε-Nash equilibria.
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Eickmeyer, K., Hansen, K.A., Verbin, E. (2012). Approximating the Minmax Value of Three-Player Games within a Constant is as Hard as Detecting Planted Cliques. In: Serna, M. (eds) Algorithmic Game Theory. SAGT 2012. Lecture Notes in Computer Science, vol 7615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33996-7_9
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