IDEAS home Printed from https://ideas.repec.org/p/nwu/cmsems/1015.html
   My bibliography  Save this paper

Learning about Variable Demand in the Long Run

Author

Listed:
  • Aldo Rustichini
  • Asher Wolinsky

Abstract

This paper studies the problem of a monopoly who is uncertain about the demand it faces and learns about it over time through its pricing experience. The demand curve facing the monopoly is not constant--it changes over time in how it differs from an informed monopoly's policy. It turns out that, even when the rate at which the demand varies is negligible, the stationary probability with which the monopoly's policy deviates from its informed counterpart is non-negligible, as long as the discount factor is below 1.

Suggested Citation

  • Aldo Rustichini & Asher Wolinsky, 1992. "Learning about Variable Demand in the Long Run," Discussion Papers 1015, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  • Handle: RePEc:nwu:cmsems:1015
    as

    Download full text from publisher

    File URL: http://www.kellogg.northwestern.edu/research/math/papers/1015.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Philippe Aghion & Patrick Bolton & Christopher Harris & Bruno Jullien, 1991. "Optimal Learning by Experimentation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(4), pages 621-654.
    2. Balvers, Ronald J & Cosimano, Thomas F, 1990. "Actively Learning about Demand and the Dynamics of Price Adjustment," Economic Journal, Royal Economic Society, vol. 100(402), pages 882-898, September.
    3. Easley, David & Kiefer, Nicholas M, 1988. "Controlling a Stochastic Process with Unknown Parameters," Econometrica, Econometric Society, vol. 56(5), pages 1045-1064, September.
    4. McLennan, Andrew, 1984. "Price dispersion and incomplete learning in the long run," Journal of Economic Dynamics and Control, Elsevier, vol. 7(3), pages 331-347, September.
    5. Balvers, Ronald J. & Cosimano, Thomas F., 1993. "Periodic learning about a hidden state variable," Journal of Economic Dynamics and Control, Elsevier, vol. 17(5-6), pages 805-827.
    6. Nyarko, Yaw & Olson, Lars J., 1996. "Optimal growth with unobservable resources and learning," Journal of Economic Behavior & Organization, Elsevier, vol. 29(3), pages 465-491, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    2. Cosimano, Thomas F., 2008. "Optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1857-1894, June.
    3. Mason, Robin & Välimäki, Juuso, 2011. "Learning about the arrival of sales," Journal of Economic Theory, Elsevier, vol. 146(4), pages 1699-1711, July.
    4. Klimenko, Mikhail M., 2004. "Industrial targeting, experimentation and long-run specialization," Journal of Development Economics, Elsevier, vol. 73(1), pages 75-105, February.
    5. Vives, Xavier, 1997. "Learning from Others: A Welfare Analysis," Games and Economic Behavior, Elsevier, vol. 20(2), pages 177-200, August.
    6. Bergemann, Dirk & Valimaki, Juuso, 2002. "Entry and Vertical Differentiation," Journal of Economic Theory, Elsevier, vol. 106(1), pages 91-125, September.
    7. Umberto Garfagnini & Bruno Strulovici, 2012. "Social Learning and Innovation Cycles (revision of DP#1516, The Dynamics of Innovation)," Discussion Papers 1546, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    8. Fishman, Arthur & Rob, Rafael, 1998. "Experimentation and Competition," Journal of Economic Theory, Elsevier, vol. 78(2), pages 299-320, February.
    9. Arnoud V. den Boer & Bert Zwart, 2014. "Simultaneously Learning and Optimizing Using Controlled Variance Pricing," Management Science, INFORMS, vol. 60(3), pages 770-783, March.
    10. Koulovatianos, Christos & Mirman, Leonard J. & Santugini, Marc, 2009. "Optimal growth and uncertainty: Learning," Journal of Economic Theory, Elsevier, vol. 144(1), pages 280-295, January.
    11. Christos Koulovatianos & Leonard J. Mirman & Marc Santugini, 2006. "Investment in a Monopoly with Bayesian Learning," Vienna Economics Papers vie0603, University of Vienna, Department of Economics.
    12. Christos Koulovatianos & Leonard J. Mirman & Marc Santugini, 2006. "Investment in a Monopoly with Bayesian Learning," Vienna Economics Papers 0603, University of Vienna, Department of Economics.
    13. Spagat, M., 1995. "Leaving some stones unturned: A reassessment of iterative planning theory," Journal of Public Economics, Elsevier, vol. 58(1), pages 85-105, September.
    14. Eric Cope, 2007. "Bayesian strategies for dynamic pricing in e‐commerce," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(3), pages 265-281, April.
    15. Klumpp, Tilman, 2006. "Linear learning in changing environments," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2577-2611, December.
    16. Batlome Janjgava, 2013. "Free Entry and Social Efficiency under Unknown Demand Parameters," CERGE-EI Working Papers wp495, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    17. Vettas, Nikolaos, 1997. "Entry and exit under demand uncertainty," Economics Letters, Elsevier, vol. 57(2), pages 227-234, December.
    18. Hans M. Amman & Marco Paolo Tucci, 2018. "How active is active learning: value function method vs an approximation method," Department of Economics University of Siena 788, Department of Economics, University of Siena.
    19. Bertocchi, Graziella & Spagat, Michael, 1997. "Structural uncertainty and subsidy removal for economies in transition," European Economic Review, Elsevier, vol. 41(9), pages 1709-1733, December.
    20. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nwu:cmsems:1015. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Fran Walker The email address of this maintainer does not seem to be valid anymore. Please ask Fran Walker to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/cmnwuus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.