UC Davis

Standard dynamic resource optimization approaches, such as value function iteration, are challenged by problems involving complex uncertainty and a large state space. We extend a solution technique to address these limitations called approximate dynamic programming (ADP). ADP recently emerged in the macroeco-nomics literature and is novel to bioeconomics. We demonstrate ADP in solving a simple fishery management model under uncertainty to show: the mechanics of ADP in simplest form; the accuracy of ADP; the value of a nonparametric extension; and readily adaptable, non-specialized code. We then demonstrate ADP’s capacity to handle rich bioeconomic problems by solving the fishery management problem subject to four autocorrelated shock processes (governing economic returns and biological dynamics) which entails four sources of stochasticity and five continuous state variables. We find that accounting for multiple autocorrelation has important impacts on harvest policy and generates gains that depend crucially on the structure of harvest cost.