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Learning in a Laboratory Market with Random Supply and Demand

Published online by Cambridge University Press:  14 March 2025

Timothy N. Cason and
Daniel Friedman
Timothy N. Cason*
Affiliation:
Department of Economics, Krannert School of Management, Purdue University, West Lafayette, IN 47907-1310
Daniel Friedman
Affiliation:
Department of Economics, University of California at Santa Cruz, Santa Cruz, CA 95064
*
email: cason@mgmt.purdue.edu
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Abstract

We propose a simple adaptive learning model to study behavior in the call market. The laboratory environment features buyers and sellers who receive a new random value or cost in each period, so they must learn a strategy that maps these random draws into bids or asks. We focus on buyers’ adjustment of the “mark-down” ratio of bids relative to private value and sellers’ adjustment of the corresponding “mark-up” ratio of asks relative to private cost. The learning model involves partial adjustment of these ratios towards the ex post optimum each period. The model explains a substantial proportion of the variation in traders’ strategies. Parameter estimates indicate strong recency effects and negligible autonomous trend, but strongly asymmetric response to different kinds of ex post error. The asymmetry is only slightly attenuated in “observational learning” from other traders’ ex post errors. Simulations show that the model can account for the main systematic deviations from equilibrium predictions observed in this market institution and environment.

Keywords

experimentcall marketauctionbidding

JEL classification

D44: AuctionsC92: Laboratory, Group BehaviorD83: Search • Learning • Information and Knowledge • Communication • Belief • Unawareness

Information

Type
Research Article
Information
Experimental Economics , Volume 2 , Issue 1 , August 1999, pp. 77 - 98
Copyright
Copyright © 1999 Economic Science Association

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References

Bush, R. and Mosteller, F. (1955). Stochastic Models of Learning. New York: John Wiley and Sons.CrossRefGoogle Scholar
Camerer, C. (1999). Behavioral Game Theory. Book Manuscript, California Institute of Technology.Google Scholar
Camerer, C. and Ho, T.-H. (1999). “Experience-Weighted Attraction Learning in Normal-Form Games.” Econometrica. 67, forthcoming.CrossRefGoogle Scholar
Capra, C.M., Goeree, J., Gomez, R., and Holt, C. (1999). “Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition.” Manuscript, University of Virginia.Google Scholar
Cason, T. (1993). “Call Market Efficiency with Simple Adaptive Learning.” Economics Letters. 40, 27–32.CrossRefGoogle Scholar
Cason, T. and Friedman, D. (1995). “Learning in Laboratory Markets with Random Supply and Demand.” Manuscript, University of Southern California.Google Scholar
Cason, T. and Friedman, D. (1997). “Price Formation in Single Call Markets.” Econometrica. 65, 311–345.CrossRefGoogle Scholar
Cheung, Y.-W. and Friedman, D. (1997). “Individual Learning in Games: Some Laboratory Results.” Games and Economic Behavior 19, 46–76.CrossRefGoogle Scholar
Daniel, T., Seale, D., and Rapoport, A. (1998). “Strategic Play and Adaptive Learning in the Sealed Bid Bargaining Mechanism.” Journal of Mathematical Psychology. 42, 133–166.CrossRefGoogle ScholarPubMed
Friedman, D. and Ostroy, J. (1995). “Competitivity in Auction Markets: An Experimental and Theoretical Investigation.” Economic Journal. 105, 22–53.CrossRefGoogle Scholar
Friedman, D., Massaro, D., Kitsis, S., and Cohen, M. (1995). “A Comparison of Learning Models.” Journal of Mathematical Psychology. 39, 164–178.CrossRefGoogle Scholar
Friedman, D. and Massaro, D. (1996). “Probability Matching and Underconfidence: An Exploration of the Decision Process.” Manuscript, University of California at Santa Cruz.Google Scholar
Fudenberg, D. and Levine, D. (1998). Theory of Learning in Games. Cambridge, MA: MIT Press.Google Scholar
Garvin, S. and Kagel, J. (1994). “Learning in Common Value Auctions: Some Initial Observations.” Journal of Economic Behavior and Organization. 25, 351–372.CrossRefGoogle Scholar
Gode, D. and Sunder, S. (1993). “Allocative Efficiency of Markets with Zero Intelligence (ZI) Traders: Market as a Partial Substitute for Individual Rationality.” Journal of Political Economy. 101, 119–137.CrossRefGoogle Scholar
Gluck, M. and Bower, G. (1988). “From Conditioning to Category Learning: An Adaptive Network Model.” Journal of Experimental Psychology: General. 117, 225–244.Google ScholarPubMed
Kagel, J. (1994). “Double Auction Markets with Stochastic Supply and Demand Schedules: Clearing House and Continuous Double Auction Trading Mechanisms.” Manuscript, University of Pittsburgh.Google Scholar
Kivinen, J. and Warmuth, M. (1995). “Additive versus Exponentiated Gradient Updates for Linear Prediction.” Proceedings of the 27th Annual ACM Symposium on Theory of Computing. New York: ACM Press, pp. 209218.Google Scholar
McCabe, K., Rassenti, S., and Smith, V. (1992). “Designing Call Auction Institutions: Is Double Dutch the Best?” Economic Journal. 102, 9–23.CrossRefGoogle Scholar
McClelland, J. and Rumelhart, D. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises. Cambridge, MA: MIT Press.Google Scholar
Rescorla, R. and Wagner, A. (1972). “A Theory of Pavlovian Conditioning: Variants in the Effectiveness of Reinforcement and Nonreinforcement.” In Baleck, A. and Prokasy, W. (eds.), Classical Conditioning II: Current Theory and Research. New York: Appleton-Century Crafts, pp. 64–99.Google Scholar
Roth, A. 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
Rustichini, A., Satterthwaite, M., and Williams, S. (1994). “Convergence to Efficiency in a Simple Market with Incomplete Information.” Econometrica. 62, 1041–1063.CrossRefGoogle Scholar
Satterthwaite, M. and Williams, S. (1989). “The Rate of Convergence to Efficiency in the Buyer's Bid Double Auction as the Market Becomes Large.” Review of Economic Studies. 56, 477–498.CrossRefGoogle Scholar
Satterthwaite, M. and Williams, S. (1993). “The Bayesian Theory of the k-Double Auction.” In Friedman, D. and Rust, J. (eds.), The Double Auction Market: Institutions, Theories and Evidence. Redwood City, CA: Addison-Wesley, pp. 99–123.Google Scholar
Selten, R. and Buchta, J. (1998). “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. Mahwak, NJ: Erlaaum Ass.Google Scholar
Smith, V. and Walker, J. (1993). “Rewards, Experience and Decision Costs in First-Price Auction.” Economic Inquiry, 31, 237–244.CrossRefGoogle Scholar