Skip to main content

On the Application and Interpretation of Learning Models

  • Chapter
Experimental Business Research

Abstract

Recent research in experimental economics suggests that simple models of learning can have nontrivial practical implications. For example, our research suggests that learning models can be used to design optimal pricing policy (Haruvy & Erev, 2000), efficient rule enforcement rules (Perry, Erev & Haruvy, 2000; Shany & Erev, 2000), efficient bonus systems (Haruvy, Erev, and Perry, 2000) and even optimal gambling devices (Haruvy, Erev, and Sonsino, 2000).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bereby-Meyer, Y., and I. Erev (1998). “On Learning To Become a Successful Loser: A Comparison of Alternative Abstractions of Learning Processes in the Loss Domain.” Journal of Mathematical Psychology 42, 266–86.

    Article  Google Scholar 

  • Camerer, C., and T. Ho (1997). “EWA Learning in Games: Preliminary Estimates from Weak-Link Games.” In Games and Human Behavior: Essays in Honor of Amnon Rapoport, edited by David V. Budescu, I. Erev, and Rami Zwick. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Camerer, C., and T. Ho (1998). “EWA Learning in Coordination Games: Probability Rules, Heterogeneity, and Time-Variation.” Journal of Mathematical Psychology 42, 305–26.

    Article  Google Scholar 

  • Camerer, C., and T. Ho (1999a). “Experience-Weighted Attraction Learning in Games: Estimates from Weak-Link Games.” Chap. 3 in Games and Human Behavior, edited by David V. Budescu, Ido Erev, and Rami Zwick, 31–52. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Camerer, C., and T. Ho (1999b). “Experience-Weighted Attraction Learning in Normal Form Games.” Econometrica 67, 827–74.

    Article  Google Scholar 

  • Camerer, C., T. Ho, and X. Wang (2000). “Individual Differences in the EWA Learning with Partial Payoff Information.” Working Paper.

    Google Scholar 

  • Cheung, Y-W, and D. Friedman (1994). “Learning in Evolutionary Games: Some Laboratory Results.” Working Paper No. 303, Economics, UCSC.

    Google Scholar 

  • Cheung, Y.-W., and D. Friedman (1997). “Individual Learning in Normal Form Games: Some Laboratory Results.” Games and Economic Behavior 19, 46–76.

    Article  Google Scholar 

  • Cheung, Y.-W, and D. Friedman (1998). “Comparison of Learning and Replicator Dynamics Using Experimental Data.“ Journal of Economic Behavior and Organization 35, 263–80.

    Article  Google Scholar 

  • Daniel, T. E., D. A. Seale, and A. Rapoport (1998). ”Strategic Play and Adaptive Learning in Sealed Bid Bargaining Mechanism.“ Journal of Mathematical Psychology 42, 133–66.

    Article  Google Scholar 

  • Erev, I., and E. Haruvy (2000). ”On the Potential Uses and Current Limitations of Data Driven Learning Models.“ Technion Working Paper.

    Google Scholar 

  • Erev, I., and A. Roth (1998). “Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique Mixed Strategy Equilibria.” American Economic Review 88, 848–81.

    Google Scholar 

  • Erev, I., Y. Bereby-Meyer, and A. Roth (1999). “The Effect of Adding a Constant to All Payoffs: Experimental Investigation, and Implications for Reinforcement Learning Models.” Journal of Economic Behavior and Organization 39, 111–28.

    Article  Google Scholar 

  • Fudenberg, D., and D. K. Levine (1998). The Theory of Learning in Games. Cambridge: MIT Press.

    Google Scholar 

  • Haruvy, E., and I. Erev (2000). “When to Pursue Variable Pricing: The Relationship Between Price Format and Quality.” Mimeo.

    Google Scholar 

  • Haruvy, E., I. Erev, and O. Perry (2000). “Probabilistic Employee Incentives.” Mimeo. Haruvy, E., I. Erev, and D. Sonsino (2000). “The Medium Prizes Paradox: Evidence From a Simulated Casino” Mimeo.

    Google Scholar 

  • Haruvy, E., D. O. Stahl, and P. W. Wilson (forthcoming). “Modeling and Testing for Heterogeneity in Observed Strategic Behavior.” Review of Economics and Statistics.

    Google Scholar 

  • Kirman, A. P. (1992). “Whom or What Does the Representative Individual Represent?” Journal of Economic Perspectives 6, 117–36.

    Article  Google Scholar 

  • Lucas, R. E. Jr. (1976). “Econometric Policy Evaluation: A Critique.” Carnegie-Rochester Conference Series on Public Policy 1, 19–46.

    Article  Google Scholar 

  • Nerlove, M. (1958). “Distributed Lags and Demand Analysis.” USDA Handbook No. 141, Government Printing Office.

    Google Scholar 

  • Roth, A., and I. Erev (1995). “Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term.” Games and Economic Behavior 8, 164–212.

    Article  Google Scholar 

  • Roth, A. E., I. Erev, R. L. Slonim, and G. Barron (2000). “Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games.” Working Paper, Harvard University.

    Google Scholar 

  • Sarin, R., and F. Vahid (1999). “Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice.” Games and Economic Behavior 28, 294–309.

    Article  Google Scholar 

  • Stahl, D. O. (1996). “Boundedly Rational Rule Learning in a Guessing Game.” Games and Economic Behavior 16, 303–30.

    Article  Google Scholar 

  • Stahl, D. O. (2000). “Action Reinforcement Learning versus Rule Learning.” Working Paper, University of Texas.

    Google Scholar 

  • Stahl, D. O. (2001). “Population Rule Learning in Symmetric Normal-Form Games: Theory and Evidence.” Journal of Economic Behavior and Organization 45, 19–35.

    Article  Google Scholar 

  • Stahl, D. 0., and P. W. Wilson (1994). “Experimental Evidence on Players’ Models of Other Players:’ Journal of Economic Behavior and Organization 25, 309–27.

    Article  Google Scholar 

  • Stahl, D. O., and P. W. Wilson (1995). “On Players’ Models of Other Players-Theory and Experimental Evidence.” Games and Economic Behavior 10, 213–54.

    Article  Google Scholar 

  • Stoker, T. M. (1993). “Empirical Approaches to the Problem of Aggregation over Individuals” Journal of Economic Literature 31, 1827–74.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media New York

About this chapter

Cite this chapter

Haruvy, E., Erev, I. (2002). On the Application and Interpretation of Learning Models. In: Zwick, R., Rapoport, A. (eds) Experimental Business Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5196-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-5196-3_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4910-3

  • Online ISBN: 978-1-4757-5196-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics