On Decentralized Implicit Negotiation in Modified Ultimatum Game

Homolová, Jitka; Zugarová, Eliška; Kárný, Miroslav; Guy, Tatiana V.

doi:10.1007/978-3-030-01713-2_25

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2022

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“…The paper also compares three types of prior knowledge reflecting typical cases and illustrate its use. The paper continues our previous work [27] that assumes complete knowledge of the opponent model, which is rarely achievable in real-world applications. Thus the present paper focuses on learning the opponent model as well as on intrinsic motivation to cooperate.…”

Section: Introductionmentioning

confidence: 74%

Indirect Dynamic Negotiation in the Nash Demand Game

2022

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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.INDEX TERMS Learning, Markov decision process, Nash demand game, Negotiation.

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Section: Introductionmentioning

confidence: 74%