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A Distributed Algorithm for Optimising over Pure Strategy Nash Equilibria

Record type: Conference or Workshop Item (Other)

We develop an efficient algorithm for computing pure strategy Nash equilibria that satisfy various criteria (such as the utilitarian or Nash--Bernoulli social welfare functions) in games with sparse interaction structure. Our algorithm, called Valued Nash Propagation (VNP), integrates the optimisation problem of maximising a criterion with the constraint satisfaction problem of finding a game's equilibria to construct a criterion that defines a c-semiring. Given a suitably compact game structure, this criterion can be efficiently optimised using message-passing. To this end, we first show that VNP is complete in games whose interaction structure forms a hypertree. Then, we go on to provide theoretic and empirical results justifying its use on games with arbitrary structure; in particular, we show that it computes the optimum >82% of the time and otherwise selects an equilibrium that is always within 2% of the optimum on average.

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Citation

Chapman, Archie, Farinelli, Alessandro, Munoz De Cote Flores Luna, Jose Enrique, Rogers, Alex and Jennings, Nicholas R. (2010) A Distributed Algorithm for Optimising over Pure Strategy Nash Equilibria At Twenty-Fourth AAAI Conference on Artificial Intelligence, Georgia. 11 - 15 Jul 2010. , pp. 749-755.

More information

Published date: July 2010
Additional Information: Event Dates: 11 - 15 July, 2010
Venue - Dates: Twenty-Fourth AAAI Conference on Artificial Intelligence, Georgia, 2010-07-11 - 2010-07-15
Keywords: Game theory, distributed optimisation
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 270818
URI: http://eprints.soton.ac.uk/id/eprint/270818
PURE UUID: 5bd31b00-e026-4172-8c80-451277d328fe

Catalogue record

Date deposited: 09 Apr 2010 14:15
Last modified: 18 Jul 2017 06:50

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Contributors

Author: Archie Chapman
Author: Alessandro Farinelli
Author: Jose Enrique Munoz De Cote Flores Luna
Author: Alex Rogers
Author: Nicholas R. Jennings

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