Simulation techniques for the bioeconomic analyis of mediterranean fisheriesgame theory and effort dynamics. Gamefisto model

Resumen

The presented thesis resumes the techniques investigated for the construction of a bioeconomic model adequate to Mediterranean Fisheries. It is composed by a target species described by a global production model and a pool of secondary species. The population dynamics output are landings that after a market submodel become revenues. These revenues are transformed into net profits after the discounting of a detailed cost structure. One of the particularities of Mediterranean fisheries is the heterogeneity of its fleets, and as a consequence, a vessel level economic analysis is suggested. The GAMEFISTO model presents a game theoretic approach for the prediction of effort dynamics as its main novelty. This approach is based on Nash equilibrium (Nash, 1951) and the technical differences between the vessels of a fishery. Game theory is a tool to analyze the strategic interactions between the agents of a given exploitation. For natural resources this interaction is referred to the fact that what one user extracts will not be available for the other users (stock externality) and to the fact that one users landings will determine the sell price for the others. Fishermen in the actual Mediterranean management scheme can decide whether go fishing or not and the presented model describes such a decision. Fishing effort is expressed as days fishing and the presented model is proposed to explain the observed effort and catch trends in Mediterranean fisheries. The document starts with an introduction where the particularities of Mediterranean fishing systems and its problems are highlighted. These particularities suggest some variations on the traditional methodology of bioeconomic analysis that are reviewed in chapter I. Chapter II describes a game theoretic approach from the simplest models towards more complex ones to describe the total effort as a sum of a finite number of agents decisions. The effect of increasing the number of agents, the price dynamics and population dyn