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Optimal Experimentation in Signal Dependent Decision Problems

Author

Listed:
  • Manjira Datta

    (Arizona State University, U.S.A.)

  • Leonard J. Mirman

    (University of Virginia, U.S.A.)

  • Edward E. Schlee

    (Arizona State University, U.S.A.)

Abstract

The literature on experimentation and learning typically imposes a special dynamic structure: The only connection between periods is the updating of beliefs. Hence, both the present action and present signal realization only affect the future by changing the distribution of future beliefs. In many dynamic problems, however, either the present action or the present signal realization may enter future payoffs "directly" and not just through future beliefs: This is called "signal dependence". We analyze optimal experimentation, under signal dependence, and show that experimentation may reduce information. We also provide sufficient conditions on the primitives for information-increasing experimentation. Copyright Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association

Suggested Citation

  • 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.
  • Handle: RePEc:ier:iecrev:v:43:y:2002:i:2:p:577-608
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    Citations

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    Cited by:

    1. Cunha-e-Sa, Maria A. & Santos, Vasco, 2008. "Experimentation with accumulation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 470-496, February.
    2. Leonard J. Mirman & Kevin Reffett & Marc Santugini, 2016. "On learning and growth," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 61(4), pages 641-684, April.
    3. Muendler, Marc-Andreas, 2005. "Rational Information Choice in Financial Market Equilibrium," University of California at San Diego, Economics Working Paper Series qt5q4764nj, Department of Economics, UC San Diego.
    4. Leonard J. Mirman & Marc Santugini, 2012. "Learning and Technology Progress in Dynamic Games," Cahiers de recherche 1217, CIRPEE.
    5. Bertocchi, Graziella & Spagat, Michael, 1998. "Growth under uncertainty with experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 23(2), pages 209-231, September.
    6. Braz Camargo & Elena Pastorino, 2016. "Learning-by-Employing: The Value of Commitment under Uncertainty," Journal of Labor Economics, University of Chicago Press, vol. 34(3), pages 581-620.
    7. 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.
    8. Marc-Arthur Diaye & Jean-Max Koskievic, 2001. "Attitude towards Information and Non-Expected Utility Preferences : a Characterization by Choice Functions," Documents de recherche 01-16, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    9. Godfrey Keller, 2005. "The (in)appropriate benchmark when beliefs are not the only state variable," Economics Series Working Papers 223, University of Oxford, Department of Economics.
    10. Leonard Mirman & Marc Santugini, 2014. "Learning and Technological Progress in Dynamic Games," Dynamic Games and Applications, Springer, vol. 4(1), pages 58-72, March.
    11. 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.
    12. Edward E. Schlee, 2001. "The Value of Information in Efficient Risk-Sharing Arrangements," American Economic Review, American Economic Association, vol. 91(3), pages 509-524, June.
    13. Olson, Lars J. & Roy, Santanu, 2005. "Theory of Stochastic Optimal Economic Growth," Working Papers 28601, University of Maryland, Department of Agricultural and Resource Economics.

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