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Computing pure nash equilibria in graphical games via markov random fields
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Source Electronic Commerce archive
Proceedings of the 7th ACM conference on Electronic commerce table of contents
Ann Arbor, Michigan, USA
Pages: 91 - 99  
Year of Publication: 2006
ISBN:1-59593-236-4
Authors
Constantinos Daskalakis  University of California, Berkeley
Christos H. Papadimitriou  University of California, Berkeley
Sponsors
ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce
Publisher
ACM  New York, NY, USA
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ABSTRACT

We present a reduction from graphical games to Markov random fields so that pure Nash equilibria in the former can be found by statistical inference on the latter. Our result, when combined with the junction tree algorithm for statistical inference, yields a unified proof of all previously known tractable cases of the NP-complete problem of finding pure Nash equilibria in graphical games, but also implies efficient algorithms for new classes, such as the games with O(log n) treewidth. Furthermore, this important problem becomes susceptible to a wealth of sophisticated and empirically successful techniques from Machine Learning.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Constantinos Daskalakis: colleagues
Christos H. Papadimitriou: colleagues