Hostname: page-component-7dd5485656-kp629 Total loading time: 0 Render date: 2025-10-30T17:22:24.606Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

1

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

References

Blackburn, J.M. (1936). “Acquisition of Skills: An Analysis of Learning Curves.” IHRB Report No. 73.Google Scholar
Chatterjee, K. and Samuelson, W. (1983). “Bargaining under Incomplete Information.” Operations Research. 31, 835–851.CrossRefGoogle Scholar
Cooper, D.J. and Feltovitch, N. (1996). “Selection of Learning Rules: Theory and Experimental Evidence.” University of Pittsburgh, Dept. of Economics, Working Paper No. 305.Google Scholar
Crawford, V.P. (1997). “Theory and Experiment in the Analysis of Strategic Interaction.” In Kreps, D. and Wallis, K. (eds.), Advances in Economics and Econometrics, Seventh World Congress: Theory and Applications. Cambridge: Cambridge University Press.Google Scholar
Daniel, T.E., Seale, D.A., and Rapoport, A. (1998). “Strategic Play and Adaptive Learning in the Sealed Bid Bargaining Mechanism.” Journal of Mathematical Psychology. 42, 133–166.CrossRefGoogle ScholarPubMed
Erev, I. and Roth, A.E. (1996). “On the Need for Low Rationality, Cognitive Game Theory: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria.” University of Pittsburgh, Dept. of Economics, unpublished manuscript.Google Scholar
Hamilton, L.A. (1992). Regression Analysis with Graphics. Belmont, CA: Wadsworth.Google Scholar
Harsanyi, J.G. (1967, 1968). “Games with Incomplete Information Played by Bayesian Players.” Management Science. 14, 159–182, 320-334, 486-502.CrossRefGoogle Scholar
Kahneman, D. and Tversky, A. (1979). “Prospect Theory: An Analysis of Decision under Risk.” Econometrica. 47, 263–291.CrossRefGoogle Scholar
Linhart, PB., Radner, R., and Satterthwaite, M.A. (eds.), (1992). Bargaining with Incomplete Information. San Diego: Academic Press.Google Scholar
Radner, R. and Schotter, A. (1989). “The Sealed-Bid Mechanism: An Experimental Study.” Journal of Economic Theory. 48, 179–220.CrossRefGoogle Scholar
Rapoport, A. and Fuller, M. (1995). “Bidding Strategies in a Bilateral Monopoly with Two-Sided Incomplete Information.” Journal of Mathematical Psychology. 39, 179–196.CrossRefGoogle Scholar
Roth, A.E. and Erev, I. (1995). “Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term.” Games and Economic Behavior. 8, 164–212.CrossRefGoogle Scholar
Satterthwaite, M.A. and Williams, S. (1989). “Bilateral Trade with the Sealed-Bid k-Double Auction: Existence and Efficiency.” Journal of Economic Theory. 48, 107–133.CrossRefGoogle Scholar
Schotter, A. (1990). “Bad and Good News about the Sealed-Bid Mechanism: Some Experimental Results.” American Economic Association Papers and Proceedings. 80, 220–226.Google Scholar
Selten, R. and Buchta, J. (1999). “Experimental Sealed Bid First Price Auctions with Directly Observed Bid Functions.” In Budescu, D., Erev, I., and Zwick, R. (eds.), Games and Human Behavior: Essays in Honor of Amnon Rapoport. Mahwah, NJ: L. Erlbaum Press.Google Scholar
Selten, R., Mitzkewitz, M., and Uhlich, G.R. (1997). “Duopoly Strategies Programmed by Experienced Players.” Econometrica. 63, 220–245.Google Scholar
Thorndike, E.L. (1898). “Animal Intelligence: An Experimental Study of the Associative Processes in Animals.” Psychological Monographs. 2.CrossRefGoogle Scholar
Wilson, R. (1985). “Incentive Efficiency of Double Auctions.” Econometrica. 53, 1101–1115.CrossRefGoogle Scholar