Policy Sensitivity Analysis: simple versus complex fishery models
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Sensitivity analysis is often used to judge the sensitivity of model behaviour to uncertain assumptions about model formulations and parameter values. Since the ultimate goal of modelling is typically policy recommendation, one may suspect that it is even more useful to test the sensitivity of policy recommendations. A major reason for this is that behaviour sensitivity is not necessarily a reliable predictor of policy sensitivity. Policy sensitivity analysis is greatly simplified if one can find optimal policies. Then one can simply see how the optimal policy changes when the model assumptions are altered. Our case is a fishery model. We investigate how (near-to) optimal policies change when we correct for a typical estimation bias in an aggregate model, when we substitute the aggregate model with a cohort representation of the same fishery, and when we switch from assuming variable to assuming constant fish prices and per unit variable costs. Normally these assumptions follow from the analyst’s school of thought without testing. The most surprising result is that while assumptions about the fish price and the per unit variable costs matter a lot, the choice between an aggregate and a cohort model is of little importance.