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

Camerer, Colin F. and Nave, Gideon and Smith, Alec (2019) Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning. Management Science, 65 (4). pp. 1867-1890. ISSN 0025-1909. https://resolver.caltech.edu/CaltechAUTHORS:20190503-153709056

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1287/mnsc.2017.2965DOIArticle
https://pubsonline.informs.org/doi/suppl/10.1287/mnsc.2017.2965PublisherSupplemental Material
ORCID:
AuthorORCID
Camerer, Colin F.0000-0003-4049-1871
Nave, Gideon0000-0001-6251-5630
Smith, Alec0000-0002-1938-4660
Additional Information:© 2018 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, distribute, transmit and adapt this work, but you must attribute this work as “Management Science. Copyright © 2018 The Author(s). https://doi.org/10.1287/mnsc.2017.2965, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/.” Received: June 15, 2016; Accepted: September 08, 2017; Published Online: May 08, 2018. Accepted by Uri Gneezy, behavioral economics. Generous support was provided by the National Science Foundation [SES-0850840] and the Behavioral and Neuroeconomics Discovery Fund at Caltech. Open access was sponsored by C. Camerer.
Funders:
Funding AgencyGrant Number
NSFSES-0850840
Behavioral and Neuroeconomics Discovery Fund, CaltechUNSPECIFIED
Subject Keywords:bargaining; dynamic games; private information; mechanism design; machine learning
Issue or Number:4
Record Number:CaltechAUTHORS:20190503-153709056
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190503-153709056
Official Citation:Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning. Colin F. Camerer, Gideon Nave, and Alec Smith. Management Science 2019 65:4, 1867-1890; doi: 10.1287/mnsc.2017.2965
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95222
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 May 2019 22:48
Last Modified:03 Oct 2019 21:11

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