Liyin Yan

Date of Award

5-2007

Level of Access Assigned by Author

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Marine Biology

Advisor

James A. Wilson

Second Committee Member

Yong Chen

Third Committee Member

James L. Fastook

Abstract

Over the last several decades scientific predictions of imminent over-fishing of the lobster fishery have been very inconsistent with the huge increases in landings and population numbers. To acquire a better understanding of the spatial/temporal dynamics of the fishery and explain this discrepancy, a new modeling approach, an agent-based model with John Holland's learning classifier system, is introduced to model the lobster fishery. Instead of modeling the system from prior knowledge of the major factors affecting the population of lobsters and the population of fishers, an agent-based modeling approach models the system from bottom-up, i.e., it simulates the interactive decisions (where to place traps and whether to cut others' traps) made by large numbers of individual fishers. Learning classifier systems allow 'fishers' to learn from their experiences and adapt their fishing strategies to the changing ecological and social environment. The aggregate patterns of the fishery 'emerge' as the product of thousands of individual decisions and give us a better understanding of the competitive dynamics that lay the foundation for the growth of self-governance in the fishery. The spatial, temporal and behavioral patterns produced by the model nicely match the patterns found in observations of 988,000 trap placements made by 44 fishers in the Gulf of Maine.

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