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Optimal Experimentation in Signal Dependent Decision Problems

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  • 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

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Bibliographic Info

Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 43 (2002)
Issue (Month): 2 (May)
Pages: 577-608

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

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Leonard Mirman & Marc Santugini, 2014. "Learning and Technological Progress in Dynamic Games," Dynamic Games and Applications, Springer, vol. 4(1), pages 58-72, March.
  5. Marc-Andreas Muendler, 2005. "Rational Information Choice in Financial Market Equilibrium," CESifo Working Paper Series 1436, CESifo Group Munich.
  6. Leonard J. Mirman & Kevin Reffett & Marc Santugini, 2013. "On Learning and Growth," Cahiers de recherche 1336, CIRPEE.
  7. Bertocchi, Graziella & Spagat, Michael, 1998. "Growth under uncertainty with experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 23(2), pages 209-231, September.
  8. Christos Koulovatianos & Leonard J. Mirman & Marc Santugini, 2007. "Optimal Growth and Uncertainty: Learning," Cahiers de recherche 07-05, HEC Montréal, Institut d'économie appliquée, revised Feb 2008.
  9. Braz Camargo & Elena Pastorino, 2012. "Learning-by-employing: the value of commitment under uncertainty," Staff Report 475, Federal Reserve Bank of Minneapolis.
  10. Olson, Lars J. & Roy, Santanu, 2005. "Theory of Stochastic Optimal Economic Growth," Working Papers 28601, University of Maryland, Department of Agricultural and Resource Economics.
  11. 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.
  12. Leonard J. Mirman & Marc Santugini, 2012. "Learning and Technology Progress in Dynamic Games," Cahiers de recherche 1217, CIRPEE.

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