31 research outputs found
Approximate well-supported Nash equilibria in symmetric bimatrix games
The -well-supported Nash equilibrium is a strong notion of
approximation of a Nash equilibrium, where no player has an incentive greater
than to deviate from any of the pure strategies that she uses in
her mixed strategy. The smallest constant currently known for
which there is a polynomial-time algorithm that computes an
-well-supported Nash equilibrium in bimatrix games is slightly
below . In this paper we study this problem for symmetric bimatrix games
and we provide a polynomial-time algorithm that gives a
-well-supported Nash equilibrium, for an arbitrarily small
positive constant
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games
While there have been a number of studies about the efficacy of methods to
find exact Nash equilibria in bimatrix games, there has been little empirical
work on finding approximate Nash equilibria. Here we provide such a study that
compares a number of approximation methods and exact methods. In particular, we
explore the trade-off between the quality of approximate equilibrium and the
required running time to find one. We found that the existing library GAMUT,
which has been the de facto standard that has been used to test exact methods,
is insufficient as a test bed for approximation methods since many of its games
have pure equilibria or other easy-to-find good approximate equilibria. We
extend the breadth and depth of our study by including new interesting families
of bimatrix games, and studying bimatrix games upto size .
Finally, we provide new close-to-worst-case examples for the best-performing
algorithms for finding approximate Nash equilibria
A Polynomial-Time Algorithm for 1/2-Well-Supported Nash Equilibria in Bimatrix Games
Since the seminal PPAD-completeness result for computing a Nash equilibrium
even in two-player games, an important line of research has focused on
relaxations achievable in polynomial time. In this paper, we consider the
notion of -well-supported Nash equilibrium, where corresponds to the approximation guarantee. Put simply, in an
-well-supported equilibrium, every player chooses with positive
probability actions that are within of the maximum achievable
payoff, against the other player's strategy. Ever since the initial
approximation guarantee of 2/3 for well-supported equilibria, which was
established more than a decade ago, the progress on this problem has been
extremely slow and incremental. Notably, the small improvements to 0.6608, and
finally to 0.6528, were achieved by algorithms of growing complexity. Our main
result is a simple and intuitive algorithm, that improves the approximation
guarantee to 1/2. Our algorithm is based on linear programming and in
particular on exploiting suitably defined zero-sum games that arise from the
payoff matrices of the two players. As a byproduct, we show how to achieve the
same approximation guarantee in a query-efficient way
A Polynomial-Time Algorithm for 1/3-Approximate Nash Equilibria in Bimatrix Games
Since the celebrated PPAD-completeness result for Nash equilibria in bimatrix games, a long line of research has focused on polynomial-time algorithms that compute ?-approximate Nash equilibria. Finding the best possible approximation guarantee that we can have in polynomial time has been a fundamental and non-trivial pursuit on settling the complexity of approximate equilibria. Despite a significant amount of effort, the algorithm of Tsaknakis and Spirakis [Tsaknakis and Spirakis, 2008], with an approximation guarantee of (0.3393+?), remains the state of the art over the last 15 years. In this paper, we propose a new refinement of the Tsaknakis-Spirakis algorithm, resulting in a polynomial-time algorithm that computes a (1/3+?)-Nash equilibrium, for any constant ? > 0. The main idea of our approach is to go beyond the use of convex combinations of primal and dual strategies, as defined in the optimization framework of [Tsaknakis and Spirakis, 2008], and enrich the pool of strategies from which we build the strategy profiles that we output in certain bottleneck cases of the algorithm
Discretized Multinomial Distributions and Nash Equilibria in Anonymous Games
We show that there is a polynomial-time approximation scheme for computing
Nash equilibria in anonymous games with any fixed number of strategies (a very
broad and important class of games), extending the two-strategy result of
Daskalakis and Papadimitriou 2007. The approximation guarantee follows from a
probabilistic result of more general interest: The distribution of the sum of n
independent unit vectors with values ranging over {e1, e2, ...,ek}, where ei is
the unit vector along dimension i of the k-dimensional Euclidean space, can be
approximated by the distribution of the sum of another set of independent unit
vectors whose probabilities of obtaining each value are multiples of 1/z for
some integer z, and so that the variational distance of the two distributions
is at most eps, where eps is bounded by an inverse polynomial in z and a
function of k, but with no dependence on n. Our probabilistic result specifies
the construction of a surprisingly sparse eps-cover -- under the total
variation distance -- of the set of distributions of sums of independent unit
vectors, which is of interest on its own right.Comment: In the 49th Annual IEEE Symposium on Foundations of Computer Science,
FOCS 200
On symmetric bimatrix games
Computation of Nash equilibria of bimatrix games is studied from the viewpoint of identifying polynomially solvable cases with special attention paid to symmetric random games. An experiment is conducted on a sample of 500 randomly generated symmetric games with matrix size 12 and 15. Distribution of support size and Nash equilibria are used to formulate a conjecture: for finding a symmetric NEP it is enough to check supports up to size 4 whereas for non-symmetric and all NEP's this number is 3 and 2, respectively. If true, this enables us to use a Las Vegas algorithm that finds a Nash equilibrium in polynomial time with high probability