Confidence of the Trembling Hand: Bayseian Learning with Data Poor Stocks Public Deposited

http://ir.library.oregonstate.edu/concern/conference_proceedings_or_journals/t435gj188

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  • Management of data-poor fisheries typically relies on setting an annual catch limit (ACL) using catch-based methods that are concerned with estimating a sustainable harvest and hinge on the logic that historic catches reflect a level of exploitation the stock can sustain. The resulting harvest control rules routinely set ACLs at the median (or mean) landings over a reference period, with or without a scalar downward adjustment according to the assumed level of depletion. Thus, the only way for the ACL to increase in the future is for the fishery to routinely exceed the ACL, which may be highly unlikely with strong enforcement. We propose an alternative approach: perturbations in the form of small, temporary and intermittent increases to ACLs between stock assessments (combined with a safety valve) can increase the permanent value of the stock. On one hand, these perturbations translate into a more accurate estimation of the population model in the next assessment, which leads to ACLs closer to the true maximum sustainable yield thereafter. On the other hand, increases in ACLs imply a higher management risk in the form of overfishing. To characterize this tradeoff, we first build a dynamic stochastic model that summarizes the interaction among the stock, the industry and the regulator. Then we perform several Monte Carlo experiments. We find that under reasonable parameterization, a wide range of perturbations give rise to a net present value of the stock that not only in expectation is higher than, but oftentimes first-order stochastically dominates that without perturbation.
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  • 097634324X

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