Decision-makers are often keen to "follow the science" in highly-charged contexts such as climate policy, pandemic response, economic policy and humanitarian crisis response. In situations like these, where decisions are informed by complex modelling or simulation and there is inevitable disagreement and uncertainty, it is unclear what "following the science" really means.

Some existing paradigms of model use and interpretation are beginning to reveal themselves as unfit for purpose in the present age. We can see this in the roots of the financial crisis in 2008 (over-confidence in models), public discourse on the developing climate crisis (no one quite knows how to interpret different model results), and the present pandemic crisis (where various models appear to be driving policy in conflicting directions). These are not coincidental: as computers become more powerful, and models become more complex, the role of the model within the scientific process has become larger and larger and so has the power of the model to influence decision-making. Limitations and biases of the scientific approach in decision-making contexts have been studied extensively, but the specific role of mathematical models is becoming more and more important.

The overall aim of this project is to understand both mathematical and social aspects of the role of models in decision-making, and to connect these with real-world examples and case studies. The project crosses disciplinary boundaries, and also has a strong emphasis on impact and engagement with government, business and the third sector. This will help to ensure as wide as possible a dissemination of my recommendations about good practice for modelling and the interpretation and use of model outputs.

The four case studies speak to the key societal challenges that are addressed by this work: mitigation of and adaptation to climate change, managing epidemic/pandemic response strategies, economic stability and management of the energy transition, and the need to respond to potentially-predictable humanitarian crises. I will work closely on one case study with partners at the Start Network, a consortium of humanitarian agencies aiming to take forecast-based action in anticipation of humanitarian crises.

Mathematically, I will develop a new, coherent framework for modelling methods and the quantification of uncertainty. But models and mathematics do not exist in a vacuum. I will also investigate the social and political context of model-making and model use, and the consequences of that context for the applicability of methods and robustness of model-based results, in particular for their use in decision support.

The results and outcomes of this project will be directly relevant to UK decision-makers in government, business and the third sector, with potential to be transformative for the use of models as decision support tools. The programme of outreach and engagement is therefore very important to this project, including a book, website, blog, Twitter and engaging with subject-specific conferences. I will also invite/ encourage a diverse range of participants at the annual workshops, conferences, and the Advanced Training Course which will build the skills of UK-based early career researchers.