Science

As multi-agent systems become increasingly prevalent, a critical examination of their vulnerabilities and potential security failures is paramount.

A rigorous analytical approach, drawing on complex systems theory, can unlock more effective strategies for reducing the climate impact of our food supply.

A new framework reconstructs the complex web of production and financial relationships to reveal how vulnerabilities cascade through the modern economy.

A rigorous new framework assesses the performance of ensemble learning methods in detecting financial risk within Enterprise Resource Planning systems.

A new approach to detecting anomalies in chemical processes uses symbolic machine learning to build understandable models, offering a compelling alternative to ‘black box’ neural networks.

A new approach to forecasting U.S. recessions focuses on identifying economic indicators that shift from stable to ‘at-risk,’ proving surprisingly effective even with basic modeling techniques.

A new approach combines deep learning with explainable AI to pinpoint and understand errors in complex automotive systems, improving both performance and safety.

A new framework leverages causal inference to move beyond simple prediction in stress testing, providing more robust and interpretable risk assessments.

A new benchmark assesses how well artificial intelligence can navigate the complexities of real-world financial tool use and regulatory constraints.

A new macro-financial model reveals how debt-fueled speculation can drive asset bubbles and simultaneously increase the risk of devastating crashes.