Indirect Dynamic Negotiation in the Nash Demand Game
<|reference_start|>Indirect Dynamic Negotiation in the Nash Demand Game: 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.<|reference_end|>
@article{guy2024indirect,
    title={Indirect Dynamic Negotiation in the Nash Demand Game},
    author={Tatiana V. Guy and Jitka Homolov'a and Aleksej Gaj},
    journal={arXiv preprint arXiv:2409.06566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06566},
    primaryClass={cs.GT cs.AI math.OC}
}