What can we learn from applying machine learning to bargaining?

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Polanski, Arnold and Sikora, Jarosław (2025) What can we learn from applying machine learning to bargaining? Computational Economics. ISSN 0927-7099

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Abstract

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.

Item Type: Article
Additional Information: Funding: This work was supported by the Innovation Funding from the Faculty of Social Sciences at the University of East Anglia (SSF192002). No other funds, grants, or support were received during the preparation of this manuscript.
Faculty \ School: Faculty of Social Sciences > School of Economics
UEA Research Groups: Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance
Faculty of Social Sciences > Research Groups > Economic Theory
Faculty of Science > Research Groups > Statistics
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Depositing User: LivePure Connector
Date Deposited: 30 Jun 2025 11:30
Last Modified: 30 Jun 2025 19:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/99774
DOI: 10.1007/s10614-025-11014-y

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