Abstract
This paper studies the computation of mixed Nash equilibria in weighted Boolean games. In weighted Boolean games, players aim to maximise the total expected weight of a set of formulas by selecting behavioural strategies, that is, randomisations over the truth assignments for each propositional variable under their unique control. Behavioural strategies thus present a compact representation of mixed strategies. Two results are algorithmically significant: (a) behavioural equilibria satisfy a specific independence property; and (b) they allow for exponentially fewer supports than mixed equilibria. These findings suggest two ways in which one can leverage existing algorithms and heuristics for computing mixed equilibria: a naive approach where we check mixed equilibria for the aforesaid independence property, and a more sophisticated approach based on support enumeration. In addition, we explore a direct numerical approach inspired by finding correlated equilibria using linear programming. In an extensive experimental study, we compare the performance of these three approaches.
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