Dynamic interventions with limited knowledge in network games
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910486313828352 |
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| author | Shakarami, Mehran Cherukuri, Ashish Monshizadeh, Nima |
| author_facet | Shakarami, Mehran Cherukuri, Ashish Monshizadeh, Nima |
| contents | This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their actions with the aim of maximizing their individual payoff functions, while a central regulator uses interventions to modify their marginal returns and maximize the social welfare function. This work builds on the key observation that the solution to the steering problem depends on the knowledge of the regulator on the players' parameters and the underlying network. We, therefore, consider different scenarios based on limited knowledge and propose suitable static, dynamic and adaptive intervention protocols. We formally prove convergence to the social optimum under the proposed mechanisms. We demonstrate our theoretical findings on a case study of Cournot competition with differentiated goods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_15673 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Dynamic interventions with limited knowledge in network games Shakarami, Mehran Cherukuri, Ashish Monshizadeh, Nima Optimization and Control Systems and Control This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their actions with the aim of maximizing their individual payoff functions, while a central regulator uses interventions to modify their marginal returns and maximize the social welfare function. This work builds on the key observation that the solution to the steering problem depends on the knowledge of the regulator on the players' parameters and the underlying network. We, therefore, consider different scenarios based on limited knowledge and propose suitable static, dynamic and adaptive intervention protocols. We formally prove convergence to the social optimum under the proposed mechanisms. We demonstrate our theoretical findings on a case study of Cournot competition with differentiated goods. |
| title | Dynamic interventions with limited knowledge in network games |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2205.15673 |