Advertisement

Optimal Fishery Policies

  • Eligius M. T. HendrixEmail author
  • Rene Haijema
  • Diana van Dijk
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 248)

Abstract

This paper describes and analyses a bi-level Markov Decision Problem (MDP). The model has been used to study questions on the setting of fisheries quota. The problem extends earlier models in literature and describes fish stock and economic dynamics. At the first level, an authority decides on the quota to be fished keeping in mind long term revenues. At the second level, fishermen react on the quota set as well as on the current states of fish stock and fleet capacity by deciding on their investment and fishery effort. An analysis of the behaviour of the model is given and used to decide on how to discretize the state space. The aim is to derive optimum quota settings based on value iteration. This chapter illustrates how a MDP with continuous state and action space can be solved by truncation and discretization of the state space and applying interpolation in the value iteration.

Keywords

Dynamic programming Fishery Stochastic programming Continuous state space Discretization 

Notes

Acknowledgements

This work has been funded by grants from the Spanish state (TIN2015-66680-C2-2-R) and, Junta de Andalucía (P11-TIC-7176), in part financed by the European Regional Development Fund (ERDF).

References

  1. 1.
    M. Bailey, U.R. Sumaila, M. Lindroos, Application of game theory to fisheries over three decades. Fish. Res. 102 (1–2), 1–8 (2010)CrossRefGoogle Scholar
  2. 2.
    J.R. Boyce, Optimal capital accumulation in a fishery: A nonlinear irreversible investment model. J. Environ. Econ. Manag. 28 (3), 324–339 (1995)CrossRefGoogle Scholar
  3. 3.
    A.T. Charles, Optimal fisheries investment under uncertainty. Can. J. Fish. Aquat. Sci. 40 (12), 2080–2091 (1983)CrossRefGoogle Scholar
  4. 4.
    L. Kell, P. Bromley, Implications for current management advice for north sea plaice (pleuronectes platessa l.): Part ii. increased biological realism in recruitment, growth, density-dependent sexual maturation and the impact of sexual dimorphism and fishery discards. J. Sea Res. 51 (3–4), 301–312 (2004)Google Scholar
  5. 5.
    M.L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley, New York, 1994)CrossRefGoogle Scholar
  6. 6.
    G. Sethi, C. Costello, A. Fisher, M. Hanemann, L. Karp, Fishery management under multiple uncertainty. J. Environ. Econ. Manag. 50 (2), 300–318 (2005)CrossRefGoogle Scholar
  7. 7.
    R. Singh, Q. Weninger, M. Doyle, Fisheries management with stock growth uncertainty and costly capital adjustment. J. Environ. Econ. Manag. 52 (2), 582–599 (2006)CrossRefGoogle Scholar
  8. 8.
    M. Spence, Blue whales and applied control theory. Technical Report 108, Institute for Mathematical Studies, Stanford University (1973)Google Scholar
  9. 9.
    D. van Dijk, R. Haijema, E.M.T. Hendrix, R.A. Groeneveld, E.C. van Ierland, Fluctuating quota and management costs under multiannual adjustment of fish quota. Ecol. Model. 265 (0), 230–238 (2013)CrossRefGoogle Scholar
  10. 10.
    D. van Dijk, E.M.T. Hendrix, R. Haijema, R.A. Groeneveld, E.C. van Ierland, On solving a bi-level stochastic dynamic programming model for analyzing fisheries policies: Fishermen behavior and optimal fish quota. Econ. Model. 272, 68–75 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Eligius M. T. Hendrix
    • 1
    Email author
  • Rene Haijema
    • 2
  • Diana van Dijk
    • 3
  1. 1.Computer ArchitectureUniversidad de MálagaMálagaSpain
  2. 2.Operations Research and LogisticsWageningen UniversityWageningenThe Netherlands
  3. 3.Department of Environmental Social SciencesSwiss Federal Institute of Aquatic Science and Technology (EAWAG)DübendorfSwitzerland

Personalised recommendations