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Reinforcement-Based Adaptive Learning in Asymmetric Two-Person Bargaining with Incomplete Information

Published online by Cambridge University Press:  14 March 2025

Amnon Rapoport,
Terry E. Daniel and
Darryl A. Seale
Amnon Rapoport*
Affiliation:
University of Arizona, Department of Management and Policy, Tucson, AZ 85721
Terry E. Daniel*
Affiliation:
University of Alberta, Department of Finance and Administrative Science, Faculty of Business, Edmonton, Alberta T6G 2R6, Canada
Darryl A. Seale*
Affiliation:
Kent State University, Department of Administrative Sciences, Kent, OH 44242
*
email: amnon@aruba.ccit.arizona.edu
email: tdaniel@maildrop.srv.ualberta.ca
email: dseale@bsa3.kent.edu
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Abstract

The sealed bid k-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.

Keywords

bilateral bargaininggames with incomplete informationadaptive learning

Information

Type
Research Article
Information
Experimental Economics , Volume 1 , Issue 3 , December 1998, pp. 221 - 253
Copyright
Copyright © 1998 Economic Science Association

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Footnotes

1

Present address: Department of Marketing, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.

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