Dynamic unstructured bargaining with private information and deadlines: theory and experiment

We study dynamic unstructured bargaining with deadlines and one-sided private information, via theory and experiment. We predict the incidence of bargaining failures (“strikes”) and payoffs in each state by combining mechanism design and focal point approaches. Strikes are common in states with lower surpluses (“pies”) and strike incidence is decreasing in the pie size. Subjects reach equal splits when strikes are efficient, while payoffs are unbalanced in states where strikes are inefficient, with additional surplus accruing to the informed player. We employ a machine learning approach to explore the information content of bargaining process data. ∗Camerer: Humanities and Social Sciences and Computational and Neural Systems, California Institute of Technology, MC 228-77, Pasadena, CA 91125 (email: [email protected]); Nave: Humanities and Social Sciences and Computational and Neural Systems, California Institute of Technology, MC 228-77, Pasadena, CA 91125 (email: [email protected]); Smith: University of Arizona and Compass Lexecon, 4280 N. Campbell Ave, Suite 200, Tucson, AZ 85718 (email: [email protected]). We thank Eskil Forsell for research assistance and Joseph Wang for useful comments on this work.