The benefit of law-making power

TL;DR

This paper explores a broader class of equilibria called political equilibria, which can be more optimal for a designated player than traditional Nash equilibria, especially in multi-player mean-payoff games.

Contribution

It introduces political equilibria as a new concept, proves their existence, compares them with Nash equilibria, and analyzes the computational complexity of constructing them.

Findings

Political equilibria always exist in multi-player mean-payoff games.

Constructing political and Nash equilibria is NP-complete.

For fixed number of players, solving two-player mean-payoff games is polynomial-time with an oracle.

Abstract

We study optimal equilibria in multi-player games. An equilibrium is optimal for a player, if her payoff is maximal. A tempting approach to solving this problem is to seek optimal Nash equilibria, the standard form of equilibria where no player has an incentive to deviate from her strategy. We argue that a player with the power to define an equilibrium is in a position, where she should not be interested in the symmetry of a Nash equilibrium, and ignore the question of whether or not her outcome can be improved if the other strategies are fixed. That is, she would only have to make sure that the other players have no incentive to deviate. This defines a greater class of equilibria, which may have better (and cannot have worse) optimal equilibria for the designated powerful player. We apply this strategy to concurrent bimatrix games and to turn based multi-player mean-payoff games. For the latter, we show that such political equilibria as well as Nash equilibria always exist, and provide simple examples where the political equilibrium is superior. We show that constructing political and Nash equilibria are NP-complete problems. We also show that, for a fixed number of players, the hardest part is to solve the underlying two-player mean-payoff games: using an MPG oracle, the problem is solvable in polynomial time. It is therefore in UP and CoUP, and can be solved in pseudo polynomial and expected subexponential time.