# Indirect Dynamic Negotiation in the Nash Demand Game

arXiv: https://arxiv.org/abs/2409.06566 · HTML: https://mlsanity.com/paper/2409.06566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 66 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives](https://mlsanity.com/paper/1911.12303/) (2019)
- [Dynamics of targeted ransomware negotiation](https://mlsanity.com/paper/2110.00362/) (2021)
- [A Negotiating Strategy for a Hybrid Goal Function in Multilateral Negotiation](https://mlsanity.com/paper/2201.04126/) (2022)

## Abstract (arXiv)

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.