Roadmap shows how to achieve good lives for all and a safe climate by reorienting production and distribution toward well-being and ecological transformation instead of capital accumulation and elite consumption.

New research from the Complexity Science Hub (CSH) shows why widely used algorithms for measuring economic complexity produce trustworthy results and how these tools may benefit diverse areas such as ecology, social science, and agentic AI.

A new study presents a general, nonparametric statistical inference theory for integrals of quantiles without assuming a particular sampling design or dependence structure. The authors discuss conditions for consistency, bias, and asymptotic normality, and illustrate how the framework applies to widely used quantities in risk and inequality measurement, including tail-value-at-risk as well as Lorenz and Gini curves. Notes for dependent (time-series) data are also provided.