Indirect Dynamic Negotiation in the Nash Demand Game

0562376 - ÚTIA 2023 RIV US eng J - Journal Article
Guy, T. V. - Homolová, Jitka - Gaj, A.
Indirect Dynamic Negotiation in the Nash Demand Game.
IEEE Access. Roč. 10, č. 1 (2022), s. 105008-105021. ISSN 2169-3536. E-ISSN 2169-3536
R&D Projects: GA MŠMT(CZ) LTC18075
Institutional support: RVO:67985556
Keywords : Learning systems * Bayes methods * Markov processes * Biological system modeling * Uncertainty * Nash equilibrium * Resource management
OECD category: Automation and control systems
Impact factor: 3.9, year: 2022
Method of publishing: Open access
http://library.utia.cas.cz/separaty/2022/AS/homolova-0562376.pdf https://ieeexplore.ieee.org/document/9905577

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.
Permanent Link: https://hdl.handle.net/11104/0334712