Xiaohong Chen, Malcolm K. Brachman Professor, Department of Economics; Professor of Management; Professor of Statistics & Data Science, and member at the Yale Institute for Foundations of Data Science (FDS), has published a new paper in the Proceedings of the National Academy of Sciences (PNAS) titled Optimization via the strategic law of large numbers. The paper was selected for the PNAS Showcase, highlighting research with broad interdisciplinary significance.
The work introduces a new theoretical principle—the “strategic law of large numbers”—that helps explain why simple, scalable optimization methods often succeed in high-dimensional, noisy, and uncertain settings. By showing how randomness and scale can be harnessed strategically rather than carefully controlled, the framework offers a unifying perspective on modern optimization approaches widely used in statistics, machine learning, and data science.
Chen’s results connect foundational probability theory with practical algorithm design, providing both mathematical insight and guidance for building more reliable large-scale systems.
Why it matters
Many of today’s advances in AI and data science rely on solving massive optimization problems efficiently. This research clarifies why these methods work so well at scale—and how to design them to be faster, more stable, and more robust—strengthening the theoretical foundations behind tools used across science, engineering, and economics.
Chen, Xiaohong, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang. “Optimization via the Strategic Law of Large Numbers.” PNAS 123, no. 4 (2026): e2519845123. https://doi.org/10.1073/pnas.2519845123.