MiCRO for Multilateral Negotiations

TL;DR

This paper extends the simple MiCRO negotiation strategy to multilateral settings, demonstrating its superior performance over state-of-the-art agents and establishing it as an empirical Nash equilibrium.

Contribution

It introduces a multilateral version of MiCRO, a simple negotiation strategy, and empirically shows its effectiveness and equilibrium properties in complex negotiations.

Findings

Outperforms ANAC winners in multilateral negotiations

Forms an empirical Nash equilibrium

Effective without opponent modeling or machine learning

Abstract

Recently, a very simple new bilateral negotiation strategy called MiCRO was introduced that does not make use of any kind of opponent modeling or machine learning techniques and that does not require fine-tuning of any parameters. Despite its simplicity, it was shown that MiCRO performs similar to -- or even better than -- most state-of-the-art negotiation strategies. This lead its authors to argue that the benchmark domains on which negotiation algorithms are typically tested may be too simplistic. However, one question that was left open, was how MiCRO could be generalized to multilateral negotiations. In this paper we fill this gap by introducing a multilateral variant of MiCRO. We compare it with the winners of the Automated Negotiating Agents Competitions (ANAC) of 2015, 2017 and 2018 and show that it outperforms them. Furthermore, we perform an empirical game-theoretical analysis to show that our new version of MiCRO forms an empirical Nash equilibrium.