Naive Reinforcement Learning with Endogenous Aspirations
AbstractThis article considers a simple model of reinforcement learning. All behavior change derives from the reinforcing or deterring effect of instantaneous payoff experiences. Payoff experiences are reinforcing or deterring depending on whether the paxoff exceeds an aspiration level or falls short of it. Over time, the aspiration level is adjusted toward the actually experienced payoffs. This article shows that aspiration level adjustments may improve the decision maker's long-run performance by preventing him or her from feeling dissatisfied with even the best available strategies. However, such movements also lead to persistent deviations from expected payoff maximization by creating "probability matching" effects. Copyright 2000 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoArticle provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.
Volume (Year): 41 (2000)
Issue (Month): 4 (November)
Contact details of provider:
Postal: 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297
Phone: (215) 898-8487
Fax: (215) 573-2057
Web page: http://www.econ.upenn.edu/ier
More information through EDIRC
Other versions of this item:
- T. Borgers & R. Sarin, 2010. "Naïve Reinforcement Learning With Endogenous Aspirations," Levine's Working Paper Archive 381, David K. Levine.
- Tilman B�rgers & Rajiv Sarin, . "Naive Reinforcement Learning With Endogenous Aspiration," ELSE working papers 037, ESRC Centre on Economics Learning and Social Evolution.
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bendor, J. & Mookherjee, D. & Ray, D., 1994.
"Aspirations, Adaptive Learning and Cooperation in Reapeted Games,"
27, Boston University - Department of Economics.
- Bendor, J. & Mookherjee, D. & Ray, D., 1994. "Aspirations, adaptive learning and cooperation in repeated games," Discussion Paper 1994-42, Tilburg University, Center for Economic Research.
- Gilboa, Itzhak & Schmeidler, David, 1996.
Games and Economic Behavior,
Elsevier, vol. 15(1), pages 1-26, July.
- Debraj Ray & Dilip Mookherjee & Fernando Vega Redondo & Rajeeva L. Karandikar, 1996.
"Evolving aspirations and cooperation,"
Working Papers. Serie AD
1996-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Cross, John G, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, MIT Press, vol. 87(2), pages 239-66, May.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or ().
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.