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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. Data are available at https://doi.org/10.1287/mnsc.2017.2965 . This paper was accepted by Uri Gneezy, behavioral economics.

A critical component of both economic and perceptual decision making under uncertainty is the belief-formation process. However, most research has studied belief formation in economic and perceptual decision making in isolation. One reason for this separate treatment may be the assumption that there are distinct psychological mechanisms that underlie belief formation in economic and perceptual decisions. An alternative theory is that there exists a common mechanism that governs belief formation in both domains. Here, we test this alternative theory by combining a novel computational modeling technique with two well-known experimental paradigms. We estimate a drift-diffusion model (DDM) and provide an analytical method to decode prior beliefs from DDM parameters. Subjects in our experiment exhibit strong extrapolative beliefs in both paradigms. In line with the common mechanism hypothesis, we find that a single computational model explains belief formation in both tasks and that individual differences in belief formation are correlated across tasks. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2016.2453 . This paper was accepted by Yuval Rottenstreich, judgment and decision making.