EconML: Economics for Machine Learning

As machine learning becomes deeply embedded in society, models increasingly interact with strategic incentives, competitive forces, and resource constraints - challenges that economic theory is well-suited to address. This workshop brings together researchers from machine learning, economics, and game theory to examine how economic forces shape the interplay between learning algorithms and the ecosystems that surround them. The program is organized around two complementary themes: (1) the use of economic tools to improve model training, evaluation, and alignment in strategic and competitive environments; and (2) understanding and steering the emergent dynamics that arise when many models interact in shared environments. By bridging micro-scale mechanism design and market-level analysis, the workshop aims to develop economic interventions that help ML ecosystems avoid foreseeable failures and better serve individuals, organizations, and society at large.