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Learning and Technological Progress in Dynamic Games

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  • Leonard Mirman
  • Marc Santugini

Abstract

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. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Leonard Mirman & Marc Santugini, 2014. "Learning and Technological Progress in Dynamic Games," Dynamic Games and Applications, Springer, vol. 4(1), pages 58-72, March.
    • Handle: RePEc:spr:dyngam:v:4:y:2014:i:1:p:58-72
    DOI: 10.1007/s13235-013-0089-4
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    References listed on IDEAS

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    1. Mirman, Leonard J. & To, Ted, 2005. "Strategic resource extraction, capital accumulation and overlapping generations," Journal of Environmental Economics and Management, Elsevier, vol. 50(2), pages 378-386, September.
    2. 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.
    3. Freixas, Xavier, 1981. "Optimal growth with experimentation," Journal of Economic Theory, Elsevier, vol. 24(2), pages 296-309, April.
    4. Bertocchi, Graziella & Spagat, Michael, 1998. "Growth under uncertainty with experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 23(2), pages 209-231, September.
    5. El-Gamal, Mahmoud A. & Sundaram, Rangarajan K., 1993. "Bayesian economists ... Bayesian agents : An alternative approach to optimal learning," Journal of Economic Dynamics and Control, Elsevier, vol. 17(3), pages 355-383, May.
    6. 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.
    7. 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.
    8. David Levhari & Leonard J. Mirman, 1980. "The Great Fish War: An Example Using a Dynamic Cournot-Nash Solution," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 322-334, Spring.
    9. Antoniadou, Elena & Koulovatianos, Christos & Mirman, Leonard J., 2013. "Strategic exploitation of a common-property resource under uncertainty," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 28-39.
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    Cited by:

    1. Nahid Masoudi & Marc Santugini & Georges Zaccour, 2016. "A Dynamic Game of Emissions Pollution with Uncertainty and Learning," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(3), pages 349-372, July.
    2. Agbo, Maxime, 2014. "Strategic exploitation with learning and heterogeneous beliefs," Journal of Environmental Economics and Management, Elsevier, vol. 67(2), pages 126-140.
    3. Christos Koulovatianos, 2015. "Strategic Exploitation of a Common-Property Resource Under Rational Learning About its Reproduction," Dynamic Games and Applications, Springer, vol. 5(1), pages 94-119, March.

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