IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Adaptive Learning Models of Consumer Behaviour (first version)

This paper applies recent advances in the theory of learning to the analysis of consumer behaviour. The working assumption is that while sellers are rational in the traditional sense, consumers are boundedly rational. The differences in outcomes for search goods and experience goods are investigated. In the latter case, if consumers fail to take into account that information is only partial, they can become locked into the habit of purchasing inferior goods. Surprisingly, however, prices are lower than when information is complete. Firms have an incentive to offer lower prices to prevent consumers becoming locked into their rival's product.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.econ.ed.ac.uk/papers/id80_esedps.pdf
Download Restriction: no

Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 80.

as
in new window

Length: 14
Date of creation: Feb 2002
Date of revision:
Handle: RePEc:edn:esedps:80
Contact details of provider: Postal:
31 Buccleuch Place, EH8 9JT, Edinburgh

Phone: +44(0)1316508361
Fax: +44(0)1316504514
Web page: http://www.econ.ed.ac.uk/

More information through EDIRC

References listed on IDEAS
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.:

as in new window
  1. Tilman B�rgers & Rajiv Sarin, . "Naive Reinforcement Learning With Endogenous Aspiration," ELSE working papers 037, ESRC Centre on Economics Learning and Social Evolution.
  2. Hopkins, Ed, 1999. "Learning, Matching, and Aggregation," Games and Economic Behavior, Elsevier, vol. 26(1), pages 79-110, January.
  3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  4. Erdem, Tulin & Broniarczyk, Susan & Charavarti, Dipankar & Kapferer, Jean-Noel & Keane, Michael & Roberts, John & Steenkamp, Jan-Benedict & Swait, Joffre & Zettelmeyer, Florian, 1999. "Brand Equity, Consumer Learning and Choice," MPRA Paper 53022, University Library of Munich, Germany.
  5. Yongmin Chen & Robert W. Rosenthal, 1992. "Dynamic Duopoly with Slowly Changing Customer Loyalties," Papers 0037, Boston University - Industry Studies Programme.
  6. repec:oup:restud:v:61:y:1994:i:1:p:153-72 is not listed on IDEAS
  7. Ed Hopkins, 2001. "Two Competing Models of How People Learn in Games," Levine's Working Paper Archive 625018000000000226, David K. Levine.
  8. To, Theodore, 1996. "Multi-period Competition with Switching Costs: An Overlapping Generations Formulation," Journal of Industrial Economics, Wiley Blackwell, vol. 44(1), pages 81-87, March.
  9. C. Monica Capra & Jacob K. Goeree & Rosario Gomez & Charles A. Holt, 2000. "Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition," Virginia Economics Online Papers 336, University of Virginia, Department of Economics.
  10. Rustichini, Aldo, 1999. "Optimal Properties of Stimulus--Response Learning Models," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 244-273, October.
  11. Ed Hopkins & Roberty M. Seymour, 2002. "The Stability of Price Dispersion under Seller and Consumer Learning," Game Theory and Information 0203002, EconWPA.
  12. Caplin, Andrew & Nalebuff, Barry, 1991. "Aggregation and Imperfect Competition: On the Existence of Equilibrium," Econometrica, Econometric Society, vol. 59(1), pages 25-59, January.
  13. Fishman, Arthur & Rob, Rafael, 1998. "Experimentation and Competition," Journal of Economic Theory, Elsevier, vol. 78(2), pages 299-320, February.
  14. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
  15. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  16. Pradeep K. Chintagunta & Vithala R. Rao, 1996. "Pricing Strategies in a Dynamic Duopoly: A Differential Game Model," Management Science, INFORMS, vol. 42(11), pages 1501-1514, November.
  17. Drew Fudenberg & David K. Levine, 1996. "The Theory of Learning in Games," Levine's Working Paper Archive 624, David K. Levine.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:edn:esedps:80. See general 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: (Gina Reddie)

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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.