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Finite State Dynamic Games with Asymmetric Information: A Framework for Applied Work

Author

Listed:
  • Ariel Pakes

    (Harvard University)

  • Chaim Fershtman

    (Tel Aviv University)

Abstract

With applied work in mind, we define an equilibrium notion for dynamic games with asymmetric information which does not require a specification for players' beliefs about their opponent types. This enables us to define equilibrium conditions which, at least in principal, are testable and can be computed using a simple reinforcement learning algorithm. We conclude with an example that endogenizes the maintenance decisions for electricity generators in a dynamic game among electric utilities in which the costs states of the generators are private information.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ariel Pakes & Chaim Fershtman, 2009. "Finite State Dynamic Games with Asymmetric Information: A Framework for Applied Work," 2009 Meeting Papers 209, Society for Economic Dynamics.
    • Handle: RePEc:red:sed009:209
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    Cited by:

    1. Weintraub, Gabriel Y. & Benkard, C. Lanier & Van Roy, Benjamin, 2007. "Computational Methods for Oblivious Equilibrium," Research Papers 1969, Stanford University, Graduate School of Business.
    2. Weintraub, Gabriel Y. & Benkard, C. Lanier & Van Roy, Benjamin, 2007. "Markov Perfect Industry Dynamics with Many Firms," Research Papers 1919r, Stanford University, Graduate School of Business.
    3. Bernard Caillaud & Romain de Nijs, 2011. "Strategic loyalty reward in dynamic price Discrimination," Working Papers halshs-00622291, HAL.
    4. Lewis, Greg & Backus, Matthew, 2009. "An Estimable Demand System for a Large Auction Platform Market," Department of Economics, Working Paper Series qt8vk5j2kr, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    5. Bernard Caillaud & Romain de Nijs, 2011. "Strategic loyalty reward in dynamic price Discrimination," PSE Working Papers halshs-00622291, HAL.
    6. Bernard Caillaud & Romain De Nijs, 2014. "Strategic Loyalty Reward in Dynamic Price Discrimination," Marketing Science, INFORMS, vol. 33(5), pages 725-742, September.
    7. Mar Reguant, 2014. "Complementary Bidding Mechanisms and Startup Costs in Electricity Markets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1708-1742.
    8. Luís Cabral, 2005. "Collusion Theory: Where to Go Next?," Journal of Industry, Competition and Trade, Springer, vol. 5(3), pages 199-206, December.
    9. Patrick Bajari & Victor Chernozhukov & Han Hong & Denis Nekipelov, 2015. "Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game," NBER Working Papers 21125, National Bureau of Economic Research, Inc.
    10. Gabriel Y. Weintraub & C. Lanier Benkard & Benjamin Van Roy, 2010. "Computational Methods for Oblivious Equilibrium," Operations Research, INFORMS, vol. 58(4-part-2), pages 1247-1265, August.
    11. Ariel Pakes, 2008. "Theory and Empirical Work on Imperfectly Competitive Markets," NBER Working Papers 14117, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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