Modelling and Simulation with Mathematical and Computational Sciences

Modelling and Simulation with Mathematical and Computational Sciences (MODSIM) is uniquely a multi-disciplinary gathering of modellers – from a range of modelling disciplines (statistics, mathematics, computer science, economics, etc) and a diverse range of applications. This provided Dr Low Choy a useful venue for soliciting feedback on her recent research into encoding expert judgements, since expert elicitation is a highly multi-disciplinary and a small but emerging field. Dr Low Choy’s involvement in MODSIM provided useful experience: as session organiser for two strands—on Bayesian statistics and on expert elicitation (including coordinating refereeing of conference papers); presenter of an oral presentation; co-author of two other presentations; and main presenter of a workshop on Expert Elicitation by Design (based on a recent paper, Low-Choy et al, 2009, Ecology).

Dr Low Choy presented a (12 minute) paper on Expert elicitation and its interface with technology: a review with a view to designing Elicitator. Interesting feedback on the talk mostly came from novices to the elicitation field interested in trialling the software. Because of this feedback, Dr Low Choy realised that the workshop planned for the final day, which was originally pitched to elicitation practitioners needed to be modified to target beginners. The workshop provided interesting insights: that researcher interest is growing across many fields (from vulcanology to materials science and planning emergency response), and that software and courses for beginners are lacking.

In 2009, the conference streams were deliberately constructed to encourage multi-disciplinary cross-fertilisation, with no adjacent talks sharing the same modelling discipline. This meant Dr Low Choy was exposed to interesting new approaches. For instance: game theory was used to assess whether small changes in decisions really make a difference in the end-game; to help recover a re-introduced native bird population that was not progressing past fledgling stage, a risk assessment utilised model-based estimates of the probability of survival based on different scientific hypotheses, weighted by expert support for said hypotheses. This has influenced Dr Low Choy’s thinking on decision-theory, a framework for evaluating performance of statistical designs (such as those used for pest surveillance), and on model selection within the Bayesian framework.