Dynamic unstructured bargaining with private information: Theory, experiment, and outcome prediction via machine learning

• Main Authors: Camerer, C.F (Author), Nave, G. (Author), Smith, A. (Author)
• Format: Article
• Language: English
• Published: INFORMS Inst.for Operations Res.and the Management Sciences 2019
• Subjects: Bargaining; Dynamic game; Dynamic games; Dynamics; Economic and social effects; Efficiency; Forecasting; Learning systems; Machine design; Machine learning; Machine learning approaches; Mechanism design; Mechanism design theories; Outcome prediction; Private information; Trade off
• Online Access: View Fulltext in Publisher

 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. © 2018 The Author(s).