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<p>We collect a unique dataset from an online experiment on bilateral distributive bargaining. Using machine learning techniques, we construct a probabilistic model that predicts the share demands made by human participants. This model forms the basis for two bargaining algorithms: one that imitates human behavior, and another that optimally responds to the former. We then simulate additional bargaining games between these agents and estimate a range of models to analyze how structural features and past interactions influence agents’ decisions and bargaining outcomes. This approach yields new insights into the strategies employed by both human negotiators and artificial agents.</p>

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What Can We Learn from Applying Machine Learning to Bargaining?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月26日

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  • Arnold Polanski,
  • Jarosław Sikora

摘要

We collect a unique dataset from an online experiment on bilateral distributive bargaining. Using machine learning techniques, we construct a probabilistic model that predicts the share demands made by human participants. This model forms the basis for two bargaining algorithms: one that imitates human behavior, and another that optimally responds to the former. We then simulate additional bargaining games between these agents and estimate a range of models to analyze how structural features and past interactions influence agents’ decisions and bargaining outcomes. This approach yields new insights into the strategies employed by both human negotiators and artificial agents.