Abstract
ABSTRACT. This paper describes my experiences designing fishery models, starting from a mathematical background in the differential equations of theoretical physics. Three examples from my early research, cited by Quinn in the lead article for this issue, illustrate a historical approach to model design. Although such analytical results provide useful tools for thought, they sometimes gloss over important assumptions and limitations. I describe the series of questions that led me from simple models to a more complete statistical framework, involving state space models and Bayes statistics. Modern fishery models often grow into complex structures that depend on numerous arbitrary choices about underlying deterministic processes, process error, and measurement error. Given this inherent ambiguity and uncertainty, I discuss scientific limits to quantitative fishery models and future prospects for devising robust management algorithms.
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