DSEWELL: Data Science and The Economics of WELLbeing

by C. d’Ambrosio and A. Tkatchenko

We propose to bring together machine learning approaches, physics-inspired descriptors, and the economics of wellbeing to address questions broadly related to predicting life satisfaction and health of individuals in a data-driven manner.

In the context of improving individuals’ life, one is often faced with the question of ordering individual/societal parameters (i.e. health and wealth) in terms of their importance. Up to now, state-of-the-art approaches relied on linear regression with very low Pearson correlation coefficients (R^2=0.2-0.3). The main goal of this project is to apply modern nonlinear machine-learning techniques to data on individual and social wellbeing with the aim to:

1. understand the structure of the data and signal/noise ratio of many existing datasets,
2. go significantly beyond linear regression with kernel-based methods and neural networks to search for multi-property correlations,
3. assess different descriptors of individuals and metric definitions in ‘individual spaces’. To our knowledge, such fundamental ‘first-principles’ approaches to data analysis and nonlinear regression are only now starting to be applied to data on individual and social wellbeing, hence our project is timely and of potentially substantial impact.