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Using Machine Learning to Understand Bargaining Experiments
Date created: 2020-07-15 10:18 PM | Last Updated: 2022-04-21 02:42 AM
Identifier: DOI 10.17605/OSF.IO/9J4CM
Category: Project
Description: We study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (the "pie size"). "Unstructured" means that players can make or withdraw any offers and demands they want at any time. Such paradigms, while lifelike have been displaced in experimental studies by highly structured bargaining because they are hard to analyze. Machine learning comes to the rescue because the players' wide range of choices in unstructured bargaining can be taken as "features" used to predict behavior. Machine learning approaches can accommodate a large number of features and guard against overfitting using test samples and methods such as penalized LASSO regression. In previous research we found that LASSO could add power to theoretical variables in predicting whether bargaining ended in disagreement. We replicate this work with higher stakes, subject experience, and special attention to gender differences, demonstrating the robustness of this approach.
This repository accompanies the publication:
Camerer CF, Chen H-N, Lin P-H, Nave G, Smith A, Wang J T-y (2022) Using Machine Learning to Understand Bargaining Experiments, in Bargaining: Current Research and Future Directions, edited by Emin Karagözoğlu and Kyle B. Hyndman, Palgrave MacMillan.
DOI: 10.1007/978-3-030-76666-5_19
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