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Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning

Colin F. Camerer (), Gideon Nave () and Alec Smith ()
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Colin F. Camerer: Humanities and Social Sciences and Computational and Neural Systems, California Institute of Technology, Pasadena, California 91125
Gideon Nave: Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Alec Smith: Department of Economics, Virginia Tech, Blacksburg, Virginia 24061

Management Science, 2019, vol. 65, issue 4, 1867-1890

Abstract: We study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (the “pie size”). Using mechanism design theory, we show that given the players’ incentives, the equilibrium incidence of bargaining failures (“strikes”) should increase with the pie size, and we derive a condition under which strikes are efficient. In our setting, no equilibrium satisfies both equality and efficiency in all pie sizes. We derive two equilibria that resolve the trade-off between equality and efficiency by favoring either equality or efficiency. Using a novel experimental paradigm, we confirm that strike incidence is decreasing in the pie size. Subjects reach equal splits in small pie games (in which strikes are efficient), while most payoffs are close to either the efficient or the equal equilibrium prediction, when the pie is large. We employ a machine learning approach to show that bargaining process features recorded early in the game improve out-of-sample prediction of disagreements at the deadline. The process feature predictions are as accurate as predictions from pie sizes only, and adding process and pie data together improves predictions even more.

Keywords: bargaining; dynamic games; private information; mechanism design; machine learning (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:inm:ormnsc:v:65:y:2019:i:4:p:1867-1890