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 study, 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% 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（ecological populations）管理的实际工作中，这类工具的应用仍面临阻力。针对某一已开展管理的系统与一系列假设条件，将其转化为模型后，可借助优化方法（optimization methods）求解出成本效益最优的管理措施。然而，当核心假设不成立时，这类方法可能会做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，虽有可能逐步熟悉系统并制定灵活的管理策略，但这类策略往往基于主观标准，其背后的假设同样缺乏合理性，且通常未被明确阐明。鉴于两种方法均存在缺陷，目前尚不清楚简单的定量模型是否比专家意见更能优化环境决策。

本研究中，我们探讨了依靠经验与判断的学生在在线计算机游戏中管理模拟渔业种群（simulated fishery populations）的表现，并将其管理结果与基于模型的决策（model-based decisions）效果进行对比。我们考量了四种不同定量模型生成的捕捞决策（harvest decisions）：（1）用于生成游戏中观测到的模拟种群动态（simulated population dynamics）的模型，且所有参数值（parameter values）均已知（作为对照组）；（2）与上述模型一致，但存在未知参数值，需在游戏过程中通过观测数据进行估计；（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（age structure）的模型。

平均而言，人类玩家的表现远差于第1至3类模型，但在极少数场景中，模型的决策结果不如学生依靠经验与判断得出的方案。当模型忽略年龄结构时，其生成的管理决策效果不佳，但仍在66%的场景中优于依靠经验与判断的学生。