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

被引：0
作者：
Jaidka K. [1 ]
Ahuja H. [2 ]
Ng L.H.X. [3 ]
机构：
[1] NUS Centre for Trusted Internet & Community, National University of Singapore, 11 Computing Drive, Singapore
[2] Indian Institute of Technology Ropar, Ropar
来源：
Proceedings of the ACM on Human-Computer Interaction | 2024年 / 8卷 / CSCW1期
关键词：
Diplomacy; discourse; machine learning; natural language processing; negotiation;
D O I：
10.1145/3637362
中图分类号：
学科分类号：
摘要：
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 dataset1 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. © 2024 Copyright held by the owner/author(s).
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