It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games
This work addresses the problem of modeling social interactions in online games for researchers and developers, but it is incremental as it builds on existing methods in a specific domain.
The study analyzed over 10,000 chat messages in the game Diplomacy to understand how negotiation strategies affect game outcomes, finding that linguistic modeling predicts strategies well but not short-term trust, while graph-aware reinforcement learning effectively predicts long-term success based on negotiation history.
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.