Evaluating model complexity in fisheries management procedures

本研究的主要目的是评估渔业管理中模型复杂性的鲁棒性。 在过去的几十年里，现代种群评估模型变得非常复杂。这种复杂性的增加似乎是合理的增加生物现实主义和拟合模型更复杂的数据。 有一个众所周知的偏差方差权衡与简单与复杂的模型;这是合理的增加模型的复杂性从渔业管理的角度来看，还是我们失去了预测能力，随着模型的复杂性增加？ 本研究旨在通过两个项目来解决这个问题：1）使用模拟估计实验和真实的案例研究来评估简单模型与复杂模型相对于政策参数的鲁棒性，以及2）使用管理程序评估来设计更好的管理程序，用于数据丰富和数据贫乏的情况。 管理程序评估是渔业科学中用于设计和测试渔业管理程序的最新工具;它基于处理动态系统行为的系统控制理论。

The over-arching objective of this research is to evaluate the robustness of model complexity in fisheries management. Over the past few decades modern stock assessment models have grown incredibly complex. This increase in complexity appears to be justified by increasing biological realism and fitting models to more complex data. There is a well known bias-variance tradeoff associated with simple versus complex models; is this increase in model complexity justifiable from a fisheries management perspective or are we losing predictive power with increasing model complexity? The proposed research aims to address this question through two projects: 1) evaluate the robustness of simple versus complex models with respect to policy parameters using simulation estimation experiments and a real case-study, and 2) design better management procedures for data rich and data poor situations using management procedure evaluation. Management procedure evaluation is the newest tool being used in fisheries science for designing and testing fisheries management procedures; it is based on systems control theory that deals with the behaviour of dynamical systems.