Human judgment vs. theoretical models for the management of ecological resources

Abstract

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 poor decisions. 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 all underlying parameter values known [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. 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.