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A Partially Observable Model of Decision Making by Fishermen

Author

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  • Daniel E. Lane

    (University of Ottawa, Ottawa, Ontario, Canada)

Abstract

This paper presents an application of a partially observable Markov decision process for the intraseasonal decisions of fishing vessel operators. Throughout each fishing season, independent vessel operators must decide in which zone or fishing ground of the fishery to fish during each period to catch the most fish with the highest return to fishing effort. Fishermen's decisions are assumed to be made to maximize net operating income. The decision model incorporates the potential fish catch, the cost of the fishing effort, and the unit price of fish. Catch potential is modeled by considering the abundance of the fish stock and the catchability of the fishing technique. Abundance dynamics not observed directly are modeled as a Markov chain with a parsimonious state-space representation, which renders the problem practicable. Dynamic decision policies are computed by the method of optimal control of the process over a finite horizon. The resultant policies are used to simulate distributions of fishermen's net operating income, fishing effort dynamics, and catch statistics. The model may be used as a decision aid in the regulation of the common property fisheries resource.

Suggested Citation

  • Daniel E. Lane, 1989. "A Partially Observable Model of Decision Making by Fishermen," Operations Research, INFORMS, vol. 37(2), pages 240-254, April.
    • Handle: RePEc:inm:oropre:v:37:y:1989:i:2:p:240-254
    DOI: 10.1287/opre.37.2.240
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    Citations

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    Cited by:

    1. Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
    2. Williams, Byron K., 2009. "Markov decision processes in natural resources management: Observability and uncertainty," Ecological Modelling, Elsevier, vol. 220(6), pages 830-840.
    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. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    5. Kvamsdal, Sturla F. & Maroto, José M. & Morán, Manuel & Sandal, Leif K., 2020. "Bioeconomic modeling of seasonal fisheries," European Journal of Operational Research, Elsevier, vol. 281(2), pages 332-340.
    6. Shoshana Anily & Abraham Grosfeld-Nir, 2006. "An Optimal Lot-Sizing and Offline Inspection Policy in the Case of Nonrigid Demand," Operations Research, INFORMS, vol. 54(2), pages 311-323, April.
    7. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
    8. Abraham Grosfeld‐Nir & Eyal Cohen & Yigal Gerchak, 2007. "Production to order and off‐line inspection when the production process is partially observable," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 845-858, December.
    9. 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.
    10. Bjorndal, Trond & Lane, Daniel E. & Weintraub, Andres, 2004. "Operational research models and the management of fisheries and aquaculture: A review," European Journal of Operational Research, Elsevier, vol. 156(3), pages 533-540, August.
    11. Ives, M.C. & Scandol, J.P. & Greenville, J., 2013. "A bio-economic management strategy evaluation for a multi-species, multi-fleet fishery facing a world of uncertainty," Ecological Modelling, Elsevier, vol. 256(C), pages 69-84.
    12. Grosfeld-Nir, Abraham, 2007. "Control limits for two-state partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 182(1), pages 300-304, October.

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