Optimal Model Complexity for Decision Analytic Approaches to Natural Resource Management and Conservation

自然资源管理和保护决策分析方法的最优模型复杂度

• 批准号: 9905197
• 负责人: Milo Adkison
• 金额: $ 4.68万
• 依托单位: University of Alaska Fairbanks Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9905197&HistoricalAwards=false
• 关键词: Optimal; Model; Complexity; Decision; Analytic

Managers of natural resources are often criticized for approaches that are too simplistic. The lay person often feels they ignore critical components of the ecosystem that affect the resource they are managing. Modelers, on the other hand, often feel quite differently about this issue. In a classic study (1985, Canadian Journal of Fisheries and Aquatic Sciences 42:1066-1072), fisheries modelers Ludwig and Walters demonstrated that management could be more effective using a model of the fishery that was too simple than using the correct model form. While considering uncertainty is important, the amount and quality of data limits model complexity.The reason simple models that ignore certain biological processes can perform better for guiding management decisions is that our knowledge of biological systems is limited. For typical natural resource data sets, the poor quality of the available data means that models that are highly parameterized give poor predictions. For example, we may know a predator affects a deer population but not be sure how many deer it takes or whether it would switch to hunting another prey if deer became scarce. If we tried to incorporate this predation into our management model for deer, we might estimate its effect so poorly that we'd generate worse predictions than if we'd left out the predator altogether. The simpler, incorrect model might lead to better deer management.Recent developments may have changed the advantage for simpler models in natural resource management. Decision analytic approaches have recently experienced a rapid growth in fisheries, forestry, and conservation biology. Rather than relying on a single, "best" model for management, modelers are now considering a wide range of possible models and looking for management strategies that are robust to most of the possibilities. If multiple rather than single models are considered, it's no longer evident that complicated models will perform poorly for management purposes. Considering more models increases the chance of including some that reflect the true state of nature. It may be optimal, therefore, to admit as much uncertainty as possible so that all eventualities are considered. In fact, natural resources modelers are now building models with hundreds of parameters. However, including many models greatly increases the amount of computation that is required. Many biologists and managers will be limited in their ability to analyze such complex models and may use simple models because of their tractability.This study will investigate the optimal model complexity for decision analytic approaches to conserving and managing natural resources. The approach is to simulate an exploited natural resource, the noisy data available to managers, and management based on decision analysis. Management performance will be compared between simulations where the decision analysis is based on an overly simple model of the system and simulations where the analysis is based on the correct, more complex system model. The study will determine whether admitting more uncertainty in model parameters is always better, whether simple models are preferable in some circumstances, and the consequences of using models that are simpler than optimal. In particular, it will investigate how the optimal model complexity is affected by the amount and quality of the data available.

自然资源管理者经常因过于简单化而受到批评。 外行人经常觉得他们忽视了生态系统中影响他们管理的资源的关键组成部分。 另一方面，建模者对这个问题的看法往往完全不同。 在一项经典研究（1985年，Canadian Journal of Fisheries and Aquatic Sciences 42：1066-1072）中，渔业模型专家Ludwig和Walters证明，使用过于简单的渔业模型比使用正确的模型形式更有效。 虽然考虑不确定性很重要，但数据的数量和质量限制了模型的复杂性。忽略某些生物过程的简单模型可以更好地指导管理决策的原因是我们对生物系统的了解有限。 对于典型的自然资源数据集，可用数据的质量差意味着高度参数化的模型预测效果差。 例如，我们可能知道捕食者会影响鹿的数量，但不确定它会吃掉多少鹿，或者如果鹿变得稀少，它是否会转而捕食另一种猎物。 如果我们试图将这种捕食纳入我们的鹿的管理模型，我们可能会估计它的影响如此之差，以至于我们会产生比我们完全忽略捕食者更糟糕的预测。 更简单、不正确的模型可能会导致更好的鹿管理。最近的发展可能已经改变了自然资源管理中更简单模型的优势。 决策分析方法最近在渔业、林业和保护生物学领域得到了快速发展。 建模者现在不再依赖单一的“最佳”管理模式，而是考虑各种可能的模式，并寻找对大多数可能性都很稳健的管理战略。 如果考虑多个模型而不是单个模型，那么复杂的模型在管理方面表现不佳就不再明显了。 考虑更多的模型增加了包括一些反映真实自然状态的机会。 因此，最好是承认尽可能多的不确定性，以便考虑所有可能性。 事实上，自然资源建模人员现在正在构建具有数百个参数的模型。 然而，包含许多模型会大大增加所需的计算量。 许多生物学家和管理人员将在他们的能力有限，分析这样复杂的模型，并可能使用简单的模型，因为他们tractable.This研究将探讨最佳的模型复杂性的决策分析方法，以保护和管理自然资源。 该方法是模拟开发的自然资源，噪声数据提供给管理人员，管理决策分析的基础上。 管理绩效将在决策分析基于过于简单的系统模型的模拟与分析基于正确的、更复杂的系统模型的模拟之间进行比较。这项研究将确定是否承认更多的模型参数的不确定性总是更好，是否简单的模型在某些情况下是更可取的，以及使用比最佳模型更简单的模型的后果。 特别是，它将研究最佳模型复杂性如何受到可用数据的数量和质量的影响。