Using Experimental Data to Model Bargaining Behavior in Ultimatum Games
AbstractSubgame perfect equilibrium predictions of ultimatum bargaining games correspond poorly to the data gathered from human subjects in laboratory environments. Attempts to reconcile this discrepancy have taken one or more of three routes: (1) expanding the agent foresight and scope of decisions, (2) explicit modeling of agents' initial beliefs and their dynamics, and (3) adding social arguments to agent preferences. We take the first two routes by including the probability of rejection by the responder in proposer's decision, and using experimental data to estimate a static model of agent beliefs. Data from previously reported experiments is compared to the predictions of the optimal decision rule to validate the proposer model. Models in which the probability of acceptance of a proposal declines with the amount offered to the responder are better able to organize the data about the behavior of both players. Explanation of responders' behavior remains weak.
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Bibliographic InfoPaper provided by Yale School of Management in its series Yale School of Management Working Papers with number ysm184.
Date of creation: 07 May 2001
Date of revision:
Ultimatum Game; Experimental Economics; Parametric Modeling; Estimation;
Other versions of this item:
- Shyam Sunder & Haijin Lin, 2003. "Using Experimental Data to Model Bargaining Behavior in Ultimatum Games," Yale School of Management Working Papers ysm330, Yale School of Management.
- C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
- C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
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- Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Society for Computational Economics, vol. 22(1), pages 39-63, August.
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