Machine learning and economics

The goal of this group is to discuss research and develop a common research agenda at the intersection of machine learning (ML) and economics. The focus is on conceptual and methodological contributions of economics to ML and of ML to economics. These two fields share a common language in the frameworks of optimization, probability, and decision theory. Economics has much to contribute to ML with its considerations of multiple agents, inequality and conflicting interests, and private information. ML has much to contribute to economics with its insights on supervised and active learning, considerations of non-traditional data types, and adaptive decision-making. Special emphasis will be put on the social impact of ML, and on non-commercial applications of ML.

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