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Dec 14, 2023

Hear from Dr. Erica Thompson, as we examine the modelling ecosystem and when we need to escape it to make real-world decisions.

Climate science – like many areas of our lives, such as economics or public health – is based upon models. These models are often used to justify certain courses of action, such as investments in climate mitigation or adaptation, or even lock downs during the pandemic.

But what makes a ‘good model’?  Is it purely how well it forecasts?  Or are there other aspects that we need to consider, such as reliability, complexity, and how well it deals with uncertainty? And who gets to decide how good a model is?

In this episode, we’ll take a closer look at these questions and explore some of the nuances of models, including:

·       How to judge how good a model is,

·       The vital importance of understanding the values which underpin models

·       And how models have the power to shape the very future which they forecast.

To find out more about the Sustainability and Climate Risk (SCR®) Certificate, follow this link: https://www.garp.org/scr

For more information on climate risk, visit GARP’s Global Sustainability and Climate Risk Resource Center: https://www.garp.org/sustainability-climate

If you have any questions, thoughts, or feedback regarding this podcast series, we would love to hear from you at: climateriskpodcast@garp.com

Links from today’s discussion:

 

Speaker’s Bio

Erica Thompson, Associate Professor of Modelling for Decision Making at UCL’s Department of Science, Technology, Engineering and Public Policy

Erica’s book, Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It, was published in December 2022 and was shortlisted for Best Maths Book of 2022 by Chaulkdust Magazine, a specialist magazine for mathematicians.

Erica moved into her current role in April 2023, where she investigates the appropriate use of mathematical modelling to support real-world decisions, from mathematical and statistical questions about methodologies of inference from models, to psycho-social questions about the formation of confidence and the role of expert judgement.

Erica is also a Fellow of the London Mathematical Laboratory, where she leads the research programme on Inference from Models, and is a Visiting Senior Fellow at the LSE Data Science Institute. Erica previously held a series of roles at the LSE’s Centre for the Analysis of Time Series, initially as a Senior Policy Fellow, and subsequently as Co-Director and Acting Director.

Erica holds a BA in Experimental and Theoretical Physics and a Master of Mathematics degree from Cambridge University. She completed her PhD in Physics at Imperial College London.