Date/Time
Date(s) - 02/04/2025
11:30 am - 12:30 pm

Speaker: Daniel Presta

Location: Hamilton Hall, Room 217

Title: Machine learning methods for sensitivity analysis of climate-economic models

Abstract: Large scale integrated climate-economic models typically involve numerous underlying parameters with different amounts of uncertainty, some arising from econometric estimates using historical data, some arising from experimental measurement of physical and atmospheric relationships. Because of the intrinsic nonlinear nature of these models, it is often impossible to employ traditional methods for local sensitivity analysis based on comparative statics to understand the effect of a particular parameter on the outcome of the model. Conversely, the high computational cost of the models makes it impractical to use simulation-based methods for global sensitivity analysis. Accordingly, we explore the use of machine learning methods to understand and quantify the influence of multiple parameters, taking full account of nonlinearities, while still being computationally feasible. We illustrate the techniques in the context of an existing stock-flow consistent climate-economic model.