A decision theory case study: Choosing a season opening for a spiny lobster (Panulirus argus L.) fishery

The present work illustrates a robust rational approach to making decisions under uncertainty. The optimal opening for the Turks and Caicos Islands lobster season is estimated using Bayesian decision theory. Information from a variety of sources is combined to obtain posterior probability distributions for two uncertain parameters used in a simple bioeconomic utility model. A moult rate parameter posterior probability distribution is obtained from a subjective prior updated with size frequency and tagging data. A natural mortality posterior is obtained from a prior derived from published estimates and a fitted catch-effort model. In the case of the size-frequency and catch-effort models, the parameter likelihoods are generated using a Bayesian bootstrap technique which does not assume any particular likelihood model.