Abstract
SYNOPTIC ABSTRACTThis paper presents a Bayesian updating algorithm which can be incorporated into fishery management simulation models in order to examine the effects of imperfect knowledge, parameter uncertainty and the role of learning processes in fishery systems. In particular, the algorithm is utilized here to explore the effects of uncertainty on the performance of fishery management and the dynamics of capacity planning. Monte Carlo results indicate that, on average, the algorithm produces parameter estimates which eventually approximate the true values. However, initial uncertainty can affect significantly the economic performance of the fishery, through excessive investment and suboptimal harvesting policies.
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