Precautionary Heuristic Management and Learning for Data-Poor Fisheries

Abstract

AbstractFisheries are subject to multiple forms of uncertainty. One of these, parameter uncertainty, has been largely ignored in the fisheries economics literature even though it is known elsewhere (e.g., macroeconomics) to play an important role in models with a similar structure. Parameter uncertainty is particularly important when data series are relatively short. Managing a fishery with incorrect parameter values for the growth function can lead to collapse. The paper models management of a renewable resource with unknown growth parameters and simulates estimation of the key parameters of the growth equation as the amount of data increases. This exercise demonstrates that, when data is sparse, a simple heuristic form of management can result in reasonable rents from the fishery, improve estimation of the growth parameters in future periods, and reduce the probability that the fishery will collapse.