Equilibria, Fixed Points, and Complexity Classes

Mihalis Yannakakis (1)

1 Department of Computer Science [New York]

Many models from a variety of areas involve the computation of an equilibrium or fixed point of some kind. Examples include Nash equilibria in games; market equilibria; computing optimal strategies and the values of competitive games (stochastic and other games); stable configurations of neural networks; analysing basic stochastic models for evolution like branching processes and for language like stochastic context-free grammars; and models that incorporate the basic primitives of probability and recursion like recursive Markov chains. It is not known whether these problems can be solved in polynomial time. There are certain common computational principles underlying different types of equilibria, which are captured by the complexity classes PLS, PPAD, and FIXP. Representative complete problems for these classes are respectively, pure Nash equilibria in games where they are guaranteed to exist, (mixed) Nash equilibria in 2-player normal form games, and (mixed) Nash equilibria in normal form games with 3 (or more) players. This paper reviews the underlying computational principles and the corresponding classes.

Mots clés: Computational Complexity; Equilibria; Fixed points; Computational Complexity.; Game Theory

Domaines: Complexité [cs.CC]; Informatique et théorie des jeux [cs.GT]

Mihalis Yannakakis. Equilibria, Fixed Points, and Complexity Classes. STACS 2008, Feb 2008, Bordeaux, France. pp.19-38. ⟨hal-00208088⟩