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Bibliografie

Journal Article

Indirect Dynamic Negotiation in the Nash Demand Game

Guy T. V., Homolová Jitka, Gaj A.

: IEEE Access vol.10, 1 (2022), p. 105008-105021

: LTC18075, GA MŠk

: Learning systems, Bayes methods, Markov processes, Biological system modeling, Uncertainty, Nash equilibrium, Resource management

: 10.1109/ACCESS.2022.3210506

: http://library.utia.cas.cz/separaty/2022/AS/homolova-0562376.pdf

: https://ieeexplore.ieee.org/document/9905577

(eng): 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.

: BC

: 20205

07.01.2019 - 08:39