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Learning and Technology Progress in Dynamic Games

  • Leonard J. Mirman
  • Marc Santugini

We study investment and consumption decisions in a dynamic game under learning. To that end, we present a model in which agents not only extract a resource for consumption, but also invest in technology to improve the future stock. At the same time, the agents learn about the stochastic process governing the evolution of public capital, including the effect of investment in technology on future stock. Although the characterization of a dynamic game with Bayesian dynamics (and without the assumption of adaptive learning) is generally intractable, we characterize the unique symmetric Bayesian-learning recursive Cournot-Nash equilibrium for any finite horizon and for general distributions of the random variables. We also show that the limits of the equilibrium outcomes for a finite horizon exist. The addition of learning to a stochastic environment is shown to have a profound effect on the equilibrium.

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Paper provided by CIRPEE in its series Cahiers de recherche with number 1217.

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Date of creation: 2012
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Handle: RePEc:lvl:lacicr:1217
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  1. 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 882-98, September.
  2. Aghion Philippe & Bolton, Patrick & Harris Christopher & Jullien Bruno, 1991. "Optimal learning by experimentation," CEPREMAP Working Papers (Couverture Orange) 9104, CEPREMAP.
  3. Mirman, L.J. & Samuelson, L. & Urbano, A., 1989. "Monopoly Experimentation," Papers 8-89-7, Pennsylvania State - Department of Economics.
    • Mirman, Leonard J & Samuelson, Larry & Urbano, Amparo, 1993. "Monopoly Experimentation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 34(3), pages 549-63, August.
  4. Grossman, Sanford J & Kihlstrom, Richard E & Mirman, Leonard J, 1977. "A Bayesian Approach to the Production of Information and Learning by Doing," Review of Economic Studies, Wiley Blackwell, vol. 44(3), pages 533-47, October.
  5. Freixas, Xavier, 1981. "Optimal growth with experimentation," Journal of Economic Theory, Elsevier, vol. 24(2), pages 296-309, April.
  6. Arthur Fishman & Neil Gandal, 1993. "Experimentation and Learning with Network Effects," Industrial Organization 9309001, EconWPA.
  7. G. Berttocchi, 1995. "Growth Under Uncertainty with Experimentation," Working Papers 95-12, Brown University, Department of Economics.
  8. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November.
  9. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
  10. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-86, August.
  11. Aghion, Philippe, et al, 1991. "Optimal Learning by Experimentation," Review of Economic Studies, Wiley Blackwell, vol. 58(4), pages 621-54, July.
  12. Creane, Anthony, 1994. "Experimentation with Heteroskedastic Noise," Economic Theory, Springer, vol. 4(2), pages 275-86, March.
  13. Manjira Datta & Leonard J. Mirman & Edward E. Schlee, 2002. "Optimal Experimentation in Signal Dependent Decision Problems," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 577-608, May.
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