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Using Numerical Dynamic Programming to Compare Passive and Active Learning in the Adaptive Management of Nutrients in Shallow Lakes

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  • Bond, Craig A.
  • Loomis, John B.

Abstract

This paper illustrates the use of dual/adaptive control methods to compare passive and active adaptive management decisions in the context of an ecosystem with a threshold effect. Using discrete-time dynamic programming techniques, we model optimal phosphorus loadings under both uncertainty about natural loadings and uncertainty regarding the critical level of phosphorus concentrations beyond which nutrient recycling begins. Active management is modeled by including the anticipated value of information (or learning) in the structure of the problem, and thus the agent can perturb the system (experiment), update beliefs, and learn about the uncertain parameter. Using this formulation, we define and value optimal experimentation both ex ante and ex post. Our simulation results show that experimentation is optimal over a large range of phosphorus concentration and belief space, though ex ante benefits are small. Furthermore, realized benefits may critically depend on the true underlying parameters of the problem.

Suggested Citation

  • 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.
  • Handle: RePEc:ags:csdawp:108720
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    Citations

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    Cited by:

    1. Baggio, Michele & Fackler, Paul L., 2016. "Optimal management with reversible regime shifts," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 124-136.
    2. Michele Baggio, 2016. "Optimal Fishery Management with Regime Shifts: An Assessment of Harvesting Strategies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(3), pages 465-492, July.
    3. repec:oup:renvpo:v:12:y:2018:i:1:p:92-112. is not listed on IDEAS
    4. Springborn, Michael & Sanchirico, James N., 2013. "A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 609-624.
    5. Hess, Joshua & Manning, Dale & Iverson, Terry & Cutler, Harvey, 2016. "Uncertainty, Learning, and Local Opposition to Hydraulic Fracturing," MPRA Paper 79238, University Library of Munich, Germany.
    6. James Nolan & Dawn Parker & G. Cornelis van Kooten & Thomas Berger, 2009. "An Overview of Computational Modeling in Agricultural and Resource Economics," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 417-429, December.
    7. Rolf Groeneveld & Michael Springborn & Christopher Costello, 2014. "Repeated Experimentation to Learn About a Flow-Pollutant Threshold," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(4), pages 627-647, August.
    8. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    9. Bond, Craig A. & Iverson, Terrence, 2011. "Modeling Information in Environmental Decision-Making," Western Economics Forum, Western Agricultural Economics Association, vol. 10(02).
    10. Springborn, Michael R., 2014. "Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 226-242.

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