Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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An agent-based bioeconomic fishery model was developed to assist in the design and evaluation of management instruments in an input controlled fishery. The agent-based structure of the model enables the choice of management instruments to be tailored specifically to account for the competitive 'race to fish' and input substitution behaviour observed in input controlled fisheries. The optimal season length and capital investment in the absence of market failure is determined by assuming there is only one agent in the fishery that can internalise all future benefits resulting from sustainable harvest levels. This is then compared with a number of agent-based scenarios, with varying restrictions on fishing inputs, where each individual agent seeks to maximise its own fishing returns subject to the behaviour of other agents. While the results are for a hypothetical fishery, the model provides estimates of the economic cost of this market failure, which in turn provides a benchmark against which the cost of management can be compared.