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Markov decision processes in natural resources management: Observability and uncertainty

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  • Williams, Byron K.

Abstract

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Suggested Citation

  • Williams, Byron K., 2009. "Markov decision processes in natural resources management: Observability and uncertainty," Ecological Modelling, Elsevier, vol. 220(6), pages 830-840.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:6:p:830-840
    DOI: 10.1016/j.ecolmodel.2008.12.023
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    References listed on IDEAS

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    1. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
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    Citations

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    Cited by:

    1. do Val, J.B.R. & Guillotreau, P. & Vallée, T., 2019. "Fishery management under poorly known dynamics," European Journal of Operational Research, Elsevier, vol. 279(1), pages 242-257.
    2. Schuurman, Daniel & Weersink, Alfons & Delaporte, Aaron, 2021. "Optimal Sequential Crop Choices for Soil Carbon Management: A Dynamic Programming Approach," 2021 Annual Meeting, August 1-3, Austin, Texas 314042, Agricultural and Applied Economics Association.
    3. Williams, Byron K., 2011. "Resolving structural uncertainty in natural resources management using POMDP approaches," Ecological Modelling, Elsevier, vol. 222(5), pages 1092-1102.
    4. Stéphane S. Couture & Marie-Josée Cros & Régis Sabbadin, 2014. "Risk preferences and optimal management of uneven-aged forests in the presence of climate change: a Markov decision process approach," Post-Print hal-02741407, HAL.
    5. Johnson, Fred A. & Jensen, Gitte H. & Madsen, Jesper & Williams, Byron K., 2014. "Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population," Ecological Modelling, Elsevier, vol. 273(C), pages 186-199.
    6. Li, Y.P. & Huang, G.H. & Zhang, N. & Nie, S.L., 2011. "An inexact-stochastic with recourse model for developing regional economic-ecological sustainability under uncertainty," Ecological Modelling, Elsevier, vol. 222(2), pages 370-379.
    7. David Breininger & Brean Duncan & Mitchell Eaton & Fred Johnson & James Nichols, 2014. "Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk," Land, MDPI, vol. 3(3), pages 1-24, July.
    8. Williams, Byron K., 2012. "Reducing uncertainty about objective functions in adaptive management," Ecological Modelling, Elsevier, vol. 225(C), pages 61-65.
    9. Couture, Stéphane & Cros, Marie-Josée & Sabbadin, Régis, 2016. "Risk aversion and optimal management of an uneven-aged forest under risk of windthrow: A Markov decision process approach," Journal of Forest Economics, Elsevier, vol. 25(C), pages 94-114.
    10. Zhou, Mo, 2017. "Valuing environmental amenities through inverse optimization: Theory and case study," Journal of Environmental Economics and Management, Elsevier, vol. 83(C), pages 217-230.
    11. Schapaugh, Adam W. & Tyre, Andrew J., 2013. "Accounting for parametric uncertainty in Markov decision processes," Ecological Modelling, Elsevier, vol. 254(C), pages 15-21.
    12. You, L. & Li, Y.P. & Huang, G.H. & Zhang, J.L., 2014. "Modeling regional ecosystem development under uncertainty – A case study for New Binhai District of Tianjin," Ecological Modelling, Elsevier, vol. 288(C), pages 127-142.
    13. Williams, Byron K. & Eaton, Mitchell J. & Breininger, David R., 2011. "Adaptive resource management and the value of information," Ecological Modelling, Elsevier, vol. 222(18), pages 3429-3436.

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