Abstract
Coalition formation games form an important subclass of mixed-motive strategic situations, in which players must negotiate competitively to secure contracts. This paper compares the performance of two learning mechanisms, reinforcement learning and counterfactual reasoning, for modeling play in such games. Previous work [CK04] found that while the former type of agent converged to theoretical solutions, they did so much more slowly than human subjects. The present work addresses this issue by allowing agents to update extensively based on counterfactual reasoning.
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Chavez, A.K. (2005). Adaptive Agents in Coalition Formation Games. In: Kimbrough, S.O., Wu, D. (eds) Formal Modelling in Electronic Commerce. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26989-4_16
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DOI: https://doi.org/10.1007/3-540-26989-4_16
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