Reverse game: from Nash equilibrium to network structure, number and probability of occurrence
In this paper, we introduce a reverse game approach to network-modelled games to determine the network structure among players that can achieve a desired Nash equilibrium. We consider three types of network games: the majority game, the minority game and the best-shot public goods game. For any prop...
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| Language: | English |
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The Royal Society
2025-05-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241928 |
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| author | Ali Ebrahimi Mehdi Sadeghi |
| author_facet | Ali Ebrahimi Mehdi Sadeghi |
| author_sort | Ali Ebrahimi |
| collection | DOAJ |
| description | In this paper, we introduce a reverse game approach to network-modelled games to determine the network structure among players that can achieve a desired Nash equilibrium. We consider three types of network games: the majority game, the minority game and the best-shot public goods game. For any proposed Nash equilibrium, we identify the conditions and constraints of the network structure necessary to achieve that equilibrium in each game. Acceptable networks—i.e. networks that satisfy the assumed Nash equilibrium—are not unique, and their numbers grow exponentially based on the number of players and the combination of strategies. We provide mathematical relationships to calculate the exact number of networks that can create the specified Nash equilibrium in the best-shot public goods game. Additionally, in the majority and minority games, the relationships presented under special conditions specify the number of networks. We also investigate the distribution of acceptable networks as microsystems associated with the existing Nash equilibrium and their probability of occurrence. Our simulations indicate that the distribution of acceptable networks according to density follows a normal distribution, and their probability of occurrence increases. In other words, denser networks are more likely to lead to the desired Nash equilibrium. |
| format | Article |
| id | doaj-art-1a295f4dfe034d2e8ff7928ae8d0a6f9 |
| institution | OA Journals |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-1a295f4dfe034d2e8ff7928ae8d0a6f92025-08-20T02:32:53ZengThe Royal SocietyRoyal Society Open Science2054-57032025-05-0112510.1098/rsos.241928Reverse game: from Nash equilibrium to network structure, number and probability of occurrenceAli Ebrahimi0Mehdi Sadeghi1School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, IranNational Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, IranIn this paper, we introduce a reverse game approach to network-modelled games to determine the network structure among players that can achieve a desired Nash equilibrium. We consider three types of network games: the majority game, the minority game and the best-shot public goods game. For any proposed Nash equilibrium, we identify the conditions and constraints of the network structure necessary to achieve that equilibrium in each game. Acceptable networks—i.e. networks that satisfy the assumed Nash equilibrium—are not unique, and their numbers grow exponentially based on the number of players and the combination of strategies. We provide mathematical relationships to calculate the exact number of networks that can create the specified Nash equilibrium in the best-shot public goods game. Additionally, in the majority and minority games, the relationships presented under special conditions specify the number of networks. We also investigate the distribution of acceptable networks as microsystems associated with the existing Nash equilibrium and their probability of occurrence. Our simulations indicate that the distribution of acceptable networks according to density follows a normal distribution, and their probability of occurrence increases. In other words, denser networks are more likely to lead to the desired Nash equilibrium.https://royalsocietypublishing.org/doi/10.1098/rsos.241928game theorynetworkreverse gameNash equilibrium |
| spellingShingle | Ali Ebrahimi Mehdi Sadeghi Reverse game: from Nash equilibrium to network structure, number and probability of occurrence Royal Society Open Science game theory network reverse game Nash equilibrium |
| title | Reverse game: from Nash equilibrium to network structure, number and probability of occurrence |
| title_full | Reverse game: from Nash equilibrium to network structure, number and probability of occurrence |
| title_fullStr | Reverse game: from Nash equilibrium to network structure, number and probability of occurrence |
| title_full_unstemmed | Reverse game: from Nash equilibrium to network structure, number and probability of occurrence |
| title_short | Reverse game: from Nash equilibrium to network structure, number and probability of occurrence |
| title_sort | reverse game from nash equilibrium to network structure number and probability of occurrence |
| topic | game theory network reverse game Nash equilibrium |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241928 |
| work_keys_str_mv | AT aliebrahimi reversegamefromnashequilibriumtonetworkstructurenumberandprobabilityofoccurrence AT mehdisadeghi reversegamefromnashequilibriumtonetworkstructurenumberandprobabilityofoccurrence |