  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.

Populations and Evolution