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Monitoring as a partially observable decision problem

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  • Fackler, Paul L.
  • Haight, Robert G.

Abstract

Monitoring is an important and costly activity in resource management problems such as containing invasive species, protecting endangered species, preventing soil erosion, and regulating contracts for environmental services. Recent studies have viewed optimal monitoring as a Partially Observable Markov Decision Process (POMDP), which provides a framework for sequential decision making under stochastic resource dynamics and uncertainty about the resource state. We present an overview of the POMDP framework and its applications to resource monitoring. We discuss the concept of the information content provided by monitoring systems and illustrate how information content affects optimal monitoring strategies. Finally, we demonstrate how the timing of monitoring in relation to resource treatment and transition can have substantial effects on optimal monitoring strategies.

Suggested Citation

  • Fackler, Paul L. & Haight, Robert G., 2014. "Monitoring as a partially observable decision problem," Resource and Energy Economics, Elsevier, vol. 37(C), pages 226-241.
    • Handle: RePEc:eee:resene:v:37:y:2014:i:c:p:226-241
    DOI: 10.1016/j.reseneeco.2013.12.005
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Fan, Xiaoli & Gómez, Miguel & Atallah, Shadi, 2016. "Optimal Monitoring and Controlling of Invasive Species: The Case of Spotted Wing Drosophila in the United States," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236042, Agricultural and Applied Economics Association.
    2. Sloggy, Matthew R. & Kling, David M. & Plantinga, Andrew J., 2020. "Measure twice, cut once: Optimal inventory and harvest under volume uncertainty and stochastic price dynamics," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    3. Kling, David M. & Sanchirico, James N. & Fackler, Paul L., 2017. "Optimal monitoring and control under state uncertainty: Application to lionfish management," Journal of Environmental Economics and Management, Elsevier, vol. 84(C), pages 223-245.
    4. Jacob LaRiviere & David Kling & James N Sanchirico & Charles Sims & Michael Springborn, 2018. "The Treatment of Uncertainty and Learning in the Economics of Natural Resource and Environmental Management," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 92-112.
    5. Baggio, Michele & Fackler, Paul L., 2016. "Optimal management with reversible regime shifts," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 124-136.
    6. Thakur, Tiesta & Hurley, Terrance M., 2020. "Will Farmers adopt Remote Sensing for Soybean Aphid Management? An Economic Perspective," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304267, Agricultural and Applied Economics Association.

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    More about this item

    Keywords

    Environmental monitoring; Dynamic programming; Partial observability; Value of information; POMDP;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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