A survey on algorithms for Nash equilibria in finite normal-form games
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews variou...
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| Published in: | Computer science review Vol. 51; p. 100613 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2024
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| ISSN: | 1574-0137, 1876-7745 |
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| Abstract | Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives. For the theoretical part, we classify algorithms in the literature and present basic ideas on algorithm design and analysis. For the empirical part, we present a comprehensive comparison on the algorithms in the literature over different kinds of games. Based on these results, we provide practical suggestions on implementations and uses of these algorithms. Finally, we present a series of open problems from both theoretical and practical considerations.
•A classification on Nash equilibrium algorithms in the literature with basic ideas on design and analysis presented.•A comprehensive comparison on Nash equilibrium algorithms in the literature over different kinds of games.•Practical suggestions on implementations and uses of Nash equilibrium algorithms. |
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| AbstractList | Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives. For the theoretical part, we classify algorithms in the literature and present basic ideas on algorithm design and analysis. For the empirical part, we present a comprehensive comparison on the algorithms in the literature over different kinds of games. Based on these results, we provide practical suggestions on implementations and uses of these algorithms. Finally, we present a series of open problems from both theoretical and practical considerations.
•A classification on Nash equilibrium algorithms in the literature with basic ideas on design and analysis presented.•A comprehensive comparison on Nash equilibrium algorithms in the literature over different kinds of games.•Practical suggestions on implementations and uses of Nash equilibrium algorithms. |
| ArticleNumber | 100613 |
| Author | Shao, Kun Mguni, David Henry Deng, Xiaotie Huang, Wenhan Duan, Zhijian Wang, Jun Li, Hanyu |
| Author_xml | – sequence: 1 givenname: Hanyu orcidid: 0000-0002-5013-3333 surname: Li fullname: Li, Hanyu email: lhydave@pku.edu.cn organization: CFCS, School of Computer Science, Peking University, No. 5 Yiheyuan Road, Haidian District, 100871, Beijing, China – sequence: 2 givenname: Wenhan surname: Huang fullname: Huang, Wenhan email: wenhanhuang1993@gmail.com organization: Independent Researcher, Shenzhen, China – sequence: 3 givenname: Zhijian surname: Duan fullname: Duan, Zhijian email: zjduan@pku.edu.cn organization: CFCS, School of Computer Science, Peking University, No. 5 Yiheyuan Road, Haidian District, 100871, Beijing, China – sequence: 4 givenname: David Henry surname: Mguni fullname: Mguni, David Henry email: d.mguni@qmul.ac.uk organization: Huawei Noah’s Ark Lab, London, United Kingdom – sequence: 5 givenname: Kun surname: Shao fullname: Shao, Kun email: shaokun2@huawei.com organization: Huawei Noah’s Ark Lab, London, United Kingdom – sequence: 6 givenname: Jun surname: Wang fullname: Wang, Jun email: jun.wang@cs.ucl.ac.uk organization: Department of Computer Science, University College London, London, United Kingdom – sequence: 7 givenname: Xiaotie orcidid: 0000-0002-5282-6467 surname: Deng fullname: Deng, Xiaotie email: xiaotie@pku.edu.cn organization: CFCS, School of Computer Science, Peking University, No. 5 Yiheyuan Road, Haidian District, 100871, Beijing, China |
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