POST PROVIDED BY NICK BEETON
How Simple Should a Model Be?
Should scientists make simplifying assumptions in complex models? This is a debate as old as the hills, and one that everyone seems to have strong opinions about. Some argue that because even the most simplistic model based on the best available estimates is objective, it is better than relying solely on “gut feelings”. In such a model, estimates based on expert opinion or simplifying assumptions can at least be included in a transparent fashion. Others argue that such an approach can miss important emergent properties as a result of missed complexity, making any results misleading and potentially even worse than not using a model at all.
Models to Support Management: Invasive Horses, Cats and Deer
Both sides are right in their own way, of course, but perhaps unusually (as an applied mathematics graduate working in ecology), I’ve found myself leaning towards the former view as my career progresses. During my last postdoc, I was confronted with a large, vexing problem: the incursion of wild horses in the Australian Alps. The species was already impacting bogs and wetlands, overpopulated in some places to the point of starvation, and spreading to previously pristine areas of National Park. The issue was (and still is) highly contentious, with activists applying considerable political pressure against lethal forms of control. Knowledge of population densities across the horses’ range was patchy and ability to predict their likely movements equally unreliable. Even predicting their demographics was difficult, with most values for population growth rates conflicting and spatially variable. Continue reading