The proposed research has the potential to both reduce at source much unnecessary data collection and improve the results of analysis of data that is collected.
Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems. Our solution is to develop a method to systemize the way expert driven causal (Bayesian Network) models can be built and used effectively either in the absence of data or as a means of determining what future data is really required.
What makes it radical is that it plans to do this in situations (common for crit ical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. The project aims to improve evidence-based decision-making in areas such as medicine, law, forensics, and transport.
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