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A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

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  • Springborn, Michael
  • Sanchirico, James N.

Abstract

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.

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  • 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.
  • Handle: RePEc:eee:jeeman:v:66:y:2013:i:3:p:609-624
    DOI: 10.1016/j.jeem.2013.07.003
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    1. Smith, Martin D. & Zhang, Junjie & Coleman, Felicia C., 2008. "Econometric modeling of fisheries with complex life histories: Avoiding biological management failures," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 265-280, May.
    2. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, September.
    3. Péter Hudomiet & Robert J. Willis, 2013. "Estimating Second Order Probability Beliefs from Subjective Survival Data," Decision Analysis, INFORMS, vol. 10(2), pages 152-170, June.
    4. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    5. Massey, D. Matthew & Newbold, Stephen C. & Gentner, Brad, 2006. "Valuing water quality changes using a bioeconomic model of a coastal recreational fishery," Journal of Environmental Economics and Management, Elsevier, vol. 52(1), pages 482-500, July.
    6. Itzhak Gilboa & Massimo Marinacci, 2011. "Ambiguity and the Bayesian Paradigm," Working Papers 379, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Sethi, Gautam & Costello, Christopher & Fisher, Anthony & Hanemann, Michael & Karp, Larry, 2005. "Fishery management under multiple uncertainty," Journal of Environmental Economics and Management, Elsevier, vol. 50(2), pages 300-318, September.
    8. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    9. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
    10. Kyoungwon Seo, 2009. "Ambiguity and Second-Order Belief," Econometrica, Econometric Society, vol. 77(5), pages 1575-1605, September.
    11. Sanchirico, James N., 2005. "Additivity properties in metapopulation models: implications for the assessment of marine reserves," Journal of Environmental Economics and Management, Elsevier, vol. 49(1), pages 1-25, January.
    12. Craig A. Bond & John B. Loomis, 2009. "Using Numerical Dynamic Programming to Compare Passive and Active Learning in the Adaptive Management of Nutrients in Shallow Lakes," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 555-573, December.
    13. Grimsrud, Kristine M. & Huffaker, Ray, 2006. "Solving multidimensional bioeconomic problems with singular-perturbation reduction methods: Application to managing pest resistance to pesticidal crops," Journal of Environmental Economics and Management, Elsevier, vol. 51(3), pages 336-353, May.
    14. James Sanchirico & Michael Springborn, 2011. "How to Get There From Here: Ecological and Economic Dynamics of Ecosystem Service Provision," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(2), pages 243-267, February.
    15. Reed, William J., 1979. "Optimal escapement levels in stochastic and deterministic harvesting models," Journal of Environmental Economics and Management, Elsevier, vol. 6(4), pages 350-363, December.
    16. 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.
    17. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
    18. Tahvonen, Olli, 2009. "Economics of harvesting age-structured fish populations," Journal of Environmental Economics and Management, Elsevier, vol. 58(3), pages 281-299, November.
    19. Eguchi, Shinto & Copas, John, 2006. "Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2034-2040, October.
    20. Ming-Hui Chen & Qi-Man Shao, 1997. "Performance study of marginal posterior density estimation via Kullback-Leibler divergence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 6(2), pages 321-350, December.
    21. Golan, Amos & Judge, George & Karp, Larry, 1996. "A maximum entropy approach to estimation and inference in dynamic models or Counting fish in the sea using maximum entropy," Journal of Economic Dynamics and Control, Elsevier, vol. 20(4), pages 559-582, April.
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    Cited by:

    1. 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.
    2. Ivan Rudik & Derek Lemoine & Maxwell Rosenthal, 2018. "General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy," 2018 Meeting Papers 369, Society for Economic Dynamics.
    3. 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).
    4. Baggio, Michele & Fackler, Paul L., 2016. "Optimal management with reversible regime shifts," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 124-136.
    5. 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.
    6. 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.

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