The basic question being addressed by this project is "how should rational agents structure their beliefs?" A standard answer to this question is that probability theory is the correct mathematical model of rational belief: an agent's rational beliefs should conform to the structure of a probability function. This project puts forward several problems with the standard picture, and proposes that an alternative mathematical theory -- the theory of "Imprecise Probabilities" (IP) -- does a better job. This theory is a powerful generalisation of standard probability theory.
Reasoning under uncertainty is something that all kinds of individual, corporate and government actors have to do, and doing so on the basis of the best theory of rational belief will allow better decisions to be made. Since some of the problems with the standard probabilistic theory arise in contexts involving severe uncertainty,
and since Imprecise Probability Models do better in those circumstances, this project is particularly relevant to those working on decision making under severe uncertainty. For example, the success of climate adaptation decisions depends on fine-grained, long-timescale information about extremes of future weather that we don't yet have reliable ways of providing.
The objectives of the project were to produce several research articles on various aspects of IP that, together, provide a solid foundational theory of rational belief, inference and decision making using Imprecise Probabilities; and to disseminate that theory to relevant researcher communities.
