Theatre B, Richard Berry Building
The University of Melbourne
School of Botany, The University of Melbourne
When new scientific fields develop, mathematical models are used sparingly, if at all, because data and understanding are unavailable. In their place, groups of scientists and others with interests in the outcome of research provide expert advice, social commentaries, and other forms of institutionalised subjective judgement. Subjective assessments of uncertain outcomes are subject to a raft of conceptual idiosyncracies and perceptual filters that are divorced from repeatable, testable prediction. In work on the risks posed by genetically modified organisms by Colin Thompson, Ben Thompson, Kerry Landman, Peter Ades, Ed Newbigin, Roger Cousens and myself, we have found that models are indispensible when data and understanding are scarce. They allow us to explore the consequences of what we believe to be true, to look for sensitivities that may direct subsequent research, and make our thinking transparent and internally consistent. Despite these advantages, scientists involved in vetting proposals for the release of GMO's have not embraced their application. There may be a place for extending stochastic models to include uncertainties other than stochastic environmental processes and measurement error.