Data from: Human judgment vs. quantitative models for the management of ecological resources

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this paper, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known [as a control], (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1 - 3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66 percent of the time.

尽管自然资源管理的定量研究方法已取得重大进展，但在生态种群管理的实际工作中，这类工具的应用仍存在阻力。若将受控系统与一系列假设转化为模型，即可通过优化方法求解最具成本效益的管理方案。然而，当底层假设不成立时，这类方法可能会得出损害环境与经济的决策。未借助数学模型、仅依靠过往经验与判断制定决策的管理者，虽有可能逐步认知系统并形成灵活的管理策略，但这类策略通常基于主观标准，且同样依赖未经证实、往往未明确说明的假设。鉴于两种方法均存在缺陷，目前尚不清楚简单定量模型是否比专家意见更能优化环境决策。本研究通过一款在线计算机游戏，探究学生依靠自身经验与判断管理模拟渔业种群的效果，并将其管理结果与基于模型的决策表现进行对比。本次研究共考量四种不同定量模型生成的捕捞决策：(1) 用于生成游戏中模拟种群动态的模型，且所有参数值均为已知（作为对照组）；(2) 与上述模型一致，但参数值未知，需在游戏过程中通过观测数据估算的模型；(3) 结构与模拟种群动态所用模型不同的模型；(4) 忽略年龄结构的模型。在场景1至3中，人类的平均表现远不及模型，但在极少数情境下，模型的结果不如学生依靠经验与判断做出的决策。当模型忽略年龄结构时，尽管其生成的管理决策效果欠佳，但仍在66%的场景中优于依靠经验与判断的学生。