Reinforcement-Based Adaptive Learning in Asymmetric Two-Person Bargaining with Incomplete Information |
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Authors: | Amnon Rapoport Terry E. Daniel Darryl A. Seale |
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Affiliation: | (1) Department of Management and Policy, University of Arizona, 85721 Tucson, AZ, USA;(2) Department of Finance and Administrative Science, Faculty of Business, University of Alberta, T6G 2R6 Edmonton, Alberta, Canada;(3) Department of Administrative Sciences, Kent State University, 44242 Kent, OH, USA;(4) Present address: Department of Marketing, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong |
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Abstract: | The sealed bidk-double auction is a mechanism used to structure bilateral bargaining under two-sided incomplete information. This mechanism is tested in two experiments in which subjects are asked to bargain repeatedly for 50 rounds with the same partner under conditions of information disparity favoring either the buyer (Condition BA) or seller (Condition SA). Qualitatively, the observed bid and offer functions are in agreement with the Bayesian linear equilibrium solution (LES) constructed by Chatterjee and Samuelson (1983). A trader favored by the information disparity, whether buyer or seller, receives a larger share of the realized gain from trade than the other trader. Comparison with previous results reported by Daniel, Seale, and Rapoport (1998), who used randomly matched rather than fixed pairs, shows that when reputation effects are present this advantage is significantly enhanced. A reinforcement-based learning model captures the major features of the offer and bid functions, accounting for most of the variability in the round-to-round individual decisions. |
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Keywords: | bilateral bargaining games with incomplete information adaptive learning |
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