Using Machine Learning to Understand Bargaining Experiments

Colin Camerer (), Hung-Ni Chen (), Po-Hsuan Lin (), Gideon Nave (), Alec Smith () and Joseph Wang
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Hung-Ni Chen: Ludwig Maximilians University of Munich
Po-Hsuan Lin: California Institute of Technology
Gideon Nave: University of Pennsylvania
Alec Smith: Virginia Tech

Chapter Chapter 19 in Bargaining, 2022, pp 407-431 from Springer

Abstract: Abstract 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 research by highly structured bargaining because they are hard to analyze. Machine learning comes to the rescue because the players’ unstructured bargaining behavior can be taken as “features” to predict outcomes. 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 the 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-76666-5_19

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DOI: 10.1007/978-3-030-76666-5_19

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