Indirect Dynamic Negotiation in the Nash Demand Game

TL;DR

This paper introduces a Bayesian learning and Markov decision process-based model for indirect negotiation in the Nash demand game, improving coordination, success rate, and individual profit in sequential bargaining with incomplete information.

Contribution

It presents a novel decision model combining Bayesian learning and MDPs for indirect negotiation, specifically applied to the Nash demand game.

Findings

Negotiation leads to better coordination between players.

The model increases the success rate of bargaining.

Players achieve higher individual profits.

Abstract

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.