A Game-Theoretic Framework for Network Formation in Large Populations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915429965889536 |
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| author | Dayanikli, Gokce Lauriere, Mathieu |
| author_facet | Dayanikli, Gokce Lauriere, Mathieu |
| contents | In this paper, we study a model of network formation in large populations. Each agent can choose the strength of interaction (i.e. connection) with other agents to find a Nash equilibrium. Different from the recently-developed theory of graphon games, here each agent's control depends not only on her own index but also on the index of other agents. After defining the general model of the game, we focus on a special case with piecewise constant graphs and we provide optimality conditions through a system of forward-backward stochastic differential equations. Furthermore, we show the uniqueness and existence results. Finally, we provide numerical experiments to discuss the effects of different model settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03847 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Game-Theoretic Framework for Network Formation in Large Populations Dayanikli, Gokce Lauriere, Mathieu Optimization and Control Computer Science and Game Theory Social and Information Networks In this paper, we study a model of network formation in large populations. Each agent can choose the strength of interaction (i.e. connection) with other agents to find a Nash equilibrium. Different from the recently-developed theory of graphon games, here each agent's control depends not only on her own index but also on the index of other agents. After defining the general model of the game, we focus on a special case with piecewise constant graphs and we provide optimality conditions through a system of forward-backward stochastic differential equations. Furthermore, we show the uniqueness and existence results. Finally, we provide numerical experiments to discuss the effects of different model settings. |
| title | A Game-Theoretic Framework for Network Formation in Large Populations |
| topic | Optimization and Control Computer Science and Game Theory Social and Information Networks |
| url | https://arxiv.org/abs/2508.03847 |