Practical Precautionary Resource Management Using Robust Optimization
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Uncertainties inherent in fisheries motivate a precautionary approach to management, meaning an approach specifically intended to avoid bad outcomes. Stochastic dynamic optimization models, which have been in the fisheries literature for decades, provide a framework for decision making when uncertain outcomes have known probabilities. However, most such models incorporate population dynamics models for which the parameters are assumed known. In this paper, we apply a robust optimization approach to capture a form of uncertainty nearly universal in fisheries, uncertainty regarding the values of model parameters. Our approach, developed by Nilim and El Ghaoui (Oper Res 53(5):780–798, 2005), establishes bounds on parameter values based on the available data and the degree of precaution that the decision maker chooses. To demonstrate the applicability of the method to fisheries management problems, we use a simple example, the Skeena River sockeye salmon fishery. We show that robust optimization offers a structured and computationally tractable approach to formulating precautionary harvest policies. Moreover, as better information about the resource becomes available, less conservative management is possible without reducing the level of precaution.
KeywordsPrecautionary management Robust optimization Dynamic optimization Fisheries management Numerical methods
This research was conducted with support from Maryland Sea Grant under award R/FISH/EC-103 from the National Oceanic and Atmospheric Administration, US Department of Commerce, and Texas AgriLife Research with support from the Cooperative State Research, Education & Extension Service, Hatch Project TEX8604. We acknowledge the help of Ray Hilborn who provided some of the data used in the empirical application, Michele Zinn for editorial assistance, and reviewers for many helpful comments.
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