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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2409.06566 |
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| _version_ | 1866917772407078912 |
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| author | Guy, Tatiana V. Homolová, Jitka Gaj, Aleksej |
| author_facet | Guy, Tatiana V. Homolová, Jitka Gaj, Aleksej |
| contents | The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Indirect Dynamic Negotiation in the Nash Demand Game Guy, Tatiana V. Homolová, Jitka Gaj, Aleksej Computer Science and Game Theory Artificial Intelligence Optimization and Control The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players. |
| title | Indirect Dynamic Negotiation in the Nash Demand Game |
| topic | Computer Science and Game Theory Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2409.06566 |