179 research outputs found
Constant Rank Bimatrix Games are PPAD-hard
The rank of a bimatrix game (A,B) is defined as rank(A+B). Computing a Nash
equilibrium (NE) of a rank-, i.e., zero-sum game is equivalent to linear
programming (von Neumann'28, Dantzig'51). In 2005, Kannan and Theobald gave an
FPTAS for constant rank games, and asked if there exists a polynomial time
algorithm to compute an exact NE. Adsul et al. (2011) answered this question
affirmatively for rank- games, leaving rank-2 and beyond unresolved.
In this paper we show that NE computation in games with rank , is
PPAD-hard, settling a decade long open problem. Interestingly, this is the
first instance that a problem with an FPTAS turns out to be PPAD-hard. Our
reduction bypasses graphical games and game gadgets, and provides a simpler
proof of PPAD-hardness for NE computation in bimatrix games. In addition, we
get:
* An equivalence between 2D-Linear-FIXP and PPAD, improving a result by
Etessami and Yannakakis (2007) on equivalence between Linear-FIXP and PPAD.
* NE computation in a bimatrix game with convex set of Nash equilibria is as
hard as solving a simple stochastic game.
* Computing a symmetric NE of a symmetric bimatrix game with rank is
PPAD-hard.
* Computing a (1/poly(n))-approximate fixed-point of a (Linear-FIXP)
piecewise-linear function is PPAD-hard.
The status of rank- games remains unresolved
On the Complexity of Nash Equilibria of Action-Graph Games
We consider the problem of computing Nash Equilibria of action-graph games
(AGGs). AGGs, introduced by Bhat and Leyton-Brown, is a succinct representation
of games that encapsulates both "local" dependencies as in graphical games, and
partial indifference to other agents' identities as in anonymous games, which
occur in many natural settings. This is achieved by specifying a graph on the
set of actions, so that the payoff of an agent for selecting a strategy depends
only on the number of agents playing each of the neighboring strategies in the
action graph. We present a Polynomial Time Approximation Scheme for computing
mixed Nash equilibria of AGGs with constant treewidth and a constant number of
agent types (and an arbitrary number of strategies), together with hardness
results for the cases when either the treewidth or the number of agent types is
unconstrained. In particular, we show that even if the action graph is a tree,
but the number of agent-types is unconstrained, it is NP-complete to decide the
existence of a pure-strategy Nash equilibrium and PPAD-complete to compute a
mixed Nash equilibrium (even an approximate one); similarly for symmetric AGGs
(all agents belong to a single type), if we allow arbitrary treewidth. These
hardness results suggest that, in some sense, our PTAS is as strong of a
positive result as one can expect
On the Complexity of Nash Equilibria in Anonymous Games
We show that the problem of finding an {\epsilon}-approximate Nash
equilibrium in an anonymous game with seven pure strategies is complete in
PPAD, when the approximation parameter {\epsilon} is exponentially small in the
number of players.Comment: full versio
Settling Some Open Problems on 2-Player Symmetric Nash Equilibria
Over the years, researchers have studied the complexity of several decision
versions of Nash equilibrium in (symmetric) two-player games (bimatrix games).
To the best of our knowledge, the last remaining open problem of this sort is
the following; it was stated by Papadimitriou in 2007: find a non-symmetric
Nash equilibrium (NE) in a symmetric game. We show that this problem is
NP-complete and the problem of counting the number of non-symmetric NE in a
symmetric game is #P-complete.
In 2005, Kannan and Theobald defined the "rank of a bimatrix game"
represented by matrices (A, B) to be rank(A+B) and asked whether a NE can be
computed in rank 1 games in polynomial time. Observe that the rank 0 case is
precisely the zero sum case, for which a polynomial time algorithm follows from
von Neumann's reduction of such games to linear programming. In 2011, Adsul et.
al. obtained an algorithm for rank 1 games; however, it does not solve the case
of symmetric rank 1 games. We resolve this problem
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