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Wald Revisited: The Optimal Level of Experimentation

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  • Giuseppe Moscarini
  • Lones Smith

Abstract

This paper revisits Wald's (1947) sequential experimentation paradigm, now assuming that an impatient decision maker can run variable-size experiments each period at some increasing and strictly convex cost before finally choosing an irreversible action. We translate this natural discrete time experimentation story into a tractable control of variance for a continuous time diffusion. Here we robustly characterize the optimal experimentation level: It is rising in the confidence about the project outcome, and for not very convex cost functions, the random process of experimentation levels has a positive drift over time. We also explore several parametric shifts unique to our framework. Among them, we discover what is arguably an 'anti-folk' result: Where the experimentation level is positive, it is often higher for a more impatient decision maker. This paper more generally suggests that a long-sought economic paradigm that delivers a sensible law of demand for information is our dynamic one namely, allowing the decision maker an eternal repurchase (resample) option.
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Suggested Citation

  • Giuseppe Moscarini & Lones Smith, 1998. "Wald Revisited: The Optimal Level of Experimentation," Working papers 98-4, Massachusetts Institute of Technology (MIT), Department of Economics.
  • Handle: RePEc:mit:worpap:98-4
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    References listed on IDEAS

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    1. repec:cup:etheor:v:9:y:1993:i:3:p:431-50 is not listed on IDEAS
    2. Godfrey Keller & Sven Rady, 1999. "Optimal Experimentation in a Changing Environment," Review of Economic Studies, Oxford University Press, vol. 66(3), pages 475-507.
    3. Patrick Bolton & Christopher Harris, 1999. "Strategic Experimentation," Econometrica, Econometric Society, vol. 67(2), pages 349-374, March.
    4. Cressie, Noel & Morgan, Peter B., 1993. "The VPRT: A Sequential Testing Procedure Dominating the SPRT," Econometric Theory, Cambridge University Press, vol. 9(03), pages 431-450, June.
    5. Dutta, Prajit K., 1997. "Optimal management of an R&D budget," Journal of Economic Dynamics and Control, Elsevier, vol. 21(2-3), pages 575-602.
    6. Trefler, Daniel, 1993. "The Ignorant Monopolist: Optimal Learning with Endogenous Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 34(3), pages 565-581, August.
    7. Joseph Dimasi & Henry Grabowski & John Vernon, 1995. "R&D Costs, Innovative Output and Firm Size in the Pharmaceutical Industry," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 2(2), pages 201-219.
    8. McLennan, Andrew, 1984. "Price dispersion and incomplete learning in the long run," Journal of Economic Dynamics and Control, Elsevier, vol. 7(3), pages 331-347, September.
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    Cited by:

    1. Rauch, James E. & Watson, Joel, 2003. "Starting small in an unfamiliar environment," International Journal of Industrial Organization, Elsevier, vol. 21(7), pages 1021-1042, September.
    2. Cristina Mitaritonna & Zhanar Akhmetova, 2013. "A Model of Firm Experimentation under Demand Uncertainty: an Application to Multi-Destination Exporters," Working Papers 2013-10, CEPII research center.
    3. Hector Chade & Edward E. Schlee, 2000. "Increasing Returns in the Value of Information," Econometric Society World Congress 2000 Contributed Papers 1715, Econometric Society.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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