It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games

TL;DR

This paper analyzes online player negotiations in Diplomacy, showing how chat strategies influence long-term success and highlighting the importance of negotiation modeling in reinforcement learning.

Contribution

It introduces a dataset of negotiation chat messages and demonstrates their predictive power for long-term game outcomes using linguistic and graph-aware models.

Findings

Negotiation strategies can be predicted from chat messages.

Chat strategies are crucial for long-term success prediction.

Short-term trustworthiness is harder to predict.

Abstract

Online games are dynamic environments where players interact with each other, which offers a rich setting for understanding how players negotiate their way through the game to an ultimate victory. This work studies online player interactions during the turn-based strategy game, Diplomacy. We annotated a dataset of over 10,000 chat messages for different negotiation strategies and empirically examined their importance in predicting long- and short-term game outcomes. Although negotiation strategies can be predicted reasonably accurately through the linguistic modeling of the chat messages, more is needed for predicting short-term outcomes such as trustworthiness. On the other hand, they are essential in graph-aware reinforcement learning approaches to predict long-term outcomes, such as a player's success, based on their prior negotiation history. We close with a discussion of the implications and impact of our work. The dataset is available at https://github.com/kj2013/claff-diplomacy.