Periodic Reporting for period 1 - EPIMP (Epistemic Utility for Imprecise Probability)
Reporting period: 2020-02-01 to 2021-07-31
Scientific modelling informs public policy. So it is critically important to adequately capture our uncertainty regarding our models. Pretend as if there's too much uncertainty regarding our models and policy-makers will be left in the dark about how to act. Pretend as if there's little or no uncertainty regarding our models and policy-makers will be left shifting their course of action wildly as more data comes in. Imprecise probabilities provide more adequate tools for capturing severe uncertainty. But we need more than just a grab bag of imprecise probabilistic tools. From a public policy perspective, we need to be able to specify which types of errors policy-makers care more about---e.g. Are false positives worse than false negatives? By how much?---and then we need to use their preferences to select the right imprecise probabilistic tools for the job. We need to manage our uncertainty in a way that's most likely to avoid the worst types of errors. This is precisely what IP scoring rules do for us.
Unfortunately, there has been very little work to date extending these justifications to the IP framework. The project team has now provided the first characterisation of reasonable IP scoring rules (and a method for constructing them). We have successfully achieved objective 1. This represents a hugely significant advance in the state of the art. With this milestone in hand, we are on track to reach our final 3 objectives:
- To use IP scoring rules to derive epistemic justifications for existing IP methods.
- To extend the range of IP tools available for individual inquirers by engineering new methods for selecting and updating IP distributions.
- To facilitate group inquiry by discovering new deference and aggregation principles for IP distributions.
Achieving these objectives will advance the field in two significant ways:
- It will provide the first sustained investigation into the epistemic foundations of imprecise probability theory. This will make IP a central focus in contemporary epistemology and shape ongoing philosophical debates about IP’s role in inference and decision-making.
- It will develop novel IP methods for both individual and group inquiry. This has the potential to influence how IP methods are used in a range of fields, for example, economics, climate science and bioinformatics.