Bruno Codenotti, Mauro Leoncini, Giovanni Resta

It is known that finding a Nash equilibrium for win-lose bimatrix

games, i.e., two-player games where the players' payoffs are zero

and one, is complete for the class PPAD.

We describe a linear time algorithm which computes a Nash

equilibrium for win-lose bimatrix games where the number of winning

positions ...
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Christian Borgs, Jennifer Chayes, Nicole Immorlica, Adam Kalai, Vahab Mirrokni, Christos H. Papadimitriou

The folk theorem suggests that finding Nash Equilibria

in repeated games should be easier than in one-shot games. In

contrast, we show that the problem of finding any (epsilon) Nash

equilibrium for a three-player infinitely-repeated game is

computationally intractable (even when all payoffs are in

{-1,0,-1}), unless all of PPAD ...
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Yang Li

We initiate the study of the relationship between two complexity classes, BQP

(Bounded-Error Quantum Polynomial-Time) and PPAD (Polynomial Parity Argument,

Directed). We first give a conjecture that PPAD is contained in BQP, and show

a necessary and sufficient condition for the conjecture to hold. Then we prove

that the conjecture ...
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Nir Bitansky, Omer Paneth, Alon Rosen

We prove that finding a Nash equilibrium of a game is hard, assuming the existence of indistinguishability obfuscation and injective one-way functions with sub-exponential hardness. We do so by showing how these cryptographic primitives give rise to a hard computational problem that lies in the complexity class PPAD, for which ... more >>>

Tim Roughgarden

Computational complexity is the subfield of computer science that rigorously studies the intrinsic difficulty of computational problems. This survey explains how complexity theory defines “hard problems”; applies these concepts to several equilibrium computation problems; and discusses implications for computation, games, and behavior. We assume minimal prior background in computer science.

... more >>>Pavel Hubacek, Eylon Yogev

Local search proved to be an extremely useful tool when facing hard optimization problems (e.g. via the simplex algorithm, simulated annealing, or genetic algorithms). Although powerful, it has its limitations: there are functions for which exponentially many queries are needed to find a local optimum. In many contexts the optimization ... more >>>