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On the Potential Use of Adaptive Control Methods for Improving Adaptive Natural Resource Management

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  • Bond, Craig A.
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    Abstract

    The paradigm of adaptive natural resource management (AM), in which experiments are used to learn about uncertain aspects of natural systems, is gaining prominence as the preferred technique for administration of large-scale environmental projects. To date, however, tools consistent with economic theory have yet to be used to either evaluate AM strategies or improve decision-making in this framework. Adaptive control (AC) techniques provide such an opportunity. This paper demonstrates the conceptual link between AC methods, the alternative treatment of realized information during a planning horizon, and AM practices; shows how the different assumptions about the treatment of observational information can be represented through alternative dynamic programming model structures; and provides a means of valuing alternative treatments of information and augmenting traditional benefit-cost analysis through a decomposition of the value function. The AC approach has considerable potential to help managers prioritize experiments, plan AM programs, simulate potential AM paths, and justify decisions based on an objective valuation framework.

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    Bibliographic Info

    Paper provided by Colorado State University, Department of Agricultural and Resource Economics in its series Working Papers with number 108721.

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    Date of creation: Dec 2008
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    Handle: RePEc:ags:csdawp:108721

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    Keywords: adaptive control; adaptive management; dynamic programming; value of experimentation; value of information; Resource /Energy Economics and Policy;

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    1. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    2. Kaplan, Jonathan D. & Howitt, Richard E. & Farzin, Y. Hossein, 2003. "An information-theoretical analysis of budget-constrained nonpoint source pollution control," Journal of Environmental Economics and Management, Elsevier, vol. 46(1), pages 106-130, July.
    3. Yaakov Bar-Shalom & Edison Tse, 1976. "Caution, Probing, and the Value of Information in the Control of Uncertain Systems," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 3, pages 323-337 National Bureau of Economic Research, Inc.
    4. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
    5. Tucci, Marco P, 1998. "The Nonconvexities Problem in Adaptive Control Models: A Simple Computational Solution," Computational Economics, Society for Computational Economics, vol. 12(3), pages 203-22, December.
    6. A.L. Norman & M.R. Norman & C.J. Palash, 1979. "Multiple relative maxima in optimal macroeconomic policy: an illustration," Special Studies Papers 134, Board of Governors of the Federal Reserve System (U.S.).
    7. 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.
    8. Mizrach, Bruce, 1991. "Nonconvexities in a stochastic control problem with learning," Journal of Economic Dynamics and Control, Elsevier, vol. 15(3), pages 515-538, July.
    9. Volker Wieland, 1996. "Learning by doing and the value of optimal experimentation," Finance and Economics Discussion Series 96-5, Board of Governors of the Federal Reserve System (U.S.).
    10. Kendrick, David, 1978. "Non-convexities from probing in adaptive control problems," Economics Letters, Elsevier, vol. 1(4), pages 347-351.
    11. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-86, August.
    12. Amman, Hans M & Kendrick, David A, 1995. "Nonconvexities in Stochastic Control Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 455-75, May.
    13. P. Mercado & David Kendrick, 2006. "Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work," Computational Economics, Society for Computational Economics, vol. 27(4), pages 483-496, June.
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    Cited by:
    1. Bond, Craig A. & Loomis, John B., 2008. "Using Numerical Dynamic Programming to Compare Passive and Active Learning in the Adaptive Management of Nutrients in Shallow Lakes," Working Papers 108720, Colorado State University, Department of Agricultural and Resource Economics.

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