George Georgiadis
George Georgiadis, an Associate Professor in the Strategy department, is an organizational economist with interests in applied microeconomic theory and artificial intelligence. At a broad level, his research studies how incentives—especially performance pay—affect the behaviors of individuals and organizations. His recent research has explored what data organizations need and how to use it in combination with contract theoretic models to optimize their performance pay plans. His work has been published in leading journals including the American Economic Review, Econometrica, Review of Economic Studies, RAND Journal of Economics, Journal of Economic Theory, Theoretical Economics, Journal of Public Economics and others.
Professor Georgiadis teaches AI Foundations for Managers - Strategy (AIML-901-ST), an elective MBA course that equips students with the frameworks to evaluate and strategically apply AI and machine learning across business domains. Through lectures and hands-on exercises, students learn to take machine learning tools from problem formulation through implementation to drive better business decisions.
Professor Georgiadis also teaches Data-Driven Theory (KPHD-525), a PhD course that introduces students to research that entails theoretical models designed to answer prescriptive questions such as "How should a firm design incentives to motivate its employees?" or "How should a government design its tax schedule?" given the reali es of available data and the knowledge available to a designer.
Prior to joining Kellogg, he taught at the California Institute of Technology and Boston University. He received a B.S. in Electrical and Computer Engineering from the Aristotle University in Greece, a M.S. in Electrical Engineering and a M.A. in Economics from UCLA, and a Ph.D in Management from the UCLA Anderson School of Management.
- Microeconomic Theory
- Organization Economics
- Industrial Organization
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Ph.D., 2013, Management, University of California, Los Angeles
C. Phil (M.S. equivalent), 2011, Management, University of California, Los Angeles
M.A., 2010, Economics, University of California, Los Angeles
M.S., 2010, Electrical Engineering, University of California, Los Angeles
B.S., 2007, Electrical and Computer Engineering, Aristotle University of Thessaloniki -
Associate Professor, Strategy, Kellogg School of Management, Northwestern University, 2018-present
Assistant Professor, Strategy, Kellogg School of Management, Northwestern University, 2015-2018
Assistant Professor, Department of Economics, Boston University, 2014-2015
Postdoctoral Instructor of Business, Economics, and Management, Humanities and Social Sciences, California Institute of Technology, 2013-2014 -
Fellow of the Society for the Advancement of Economic Theory (SAET), Society for Advancement of Economic Theory
2023 Excellence in Refereeing Award, Review of Economic Studies, Review of Economic Studies -
Editorial Board, Economic Theory, 2025-2028
Associate Editor, American Economic Journal: Microeconomics, 2025-2028
Referee, American Economic Review, Econometrica, Journal of Political Economy, Review of Economic Studies, American Economic Journal: Microeconomics, Economic Letters, Games and Economic Behavior, International Economic Review, International Journal of Game Theory, International Game Theory Review, Journal of Economics, Journal of Economic Dynamics & Control, Journal of Economic Theory, Journal of Economics & Management Strategy, Journal of the European Economic Association, Journal of Law, Economics and, 2024-2025
Research in Economics (MECS-560-3)
This course introduces first-year PhD students to the economics research environment. With an emphasis on breadth, and minimal prerequisite knowledge at the graduate level, students are exposed to the process of forming and answering research questions. The course involves multiple faculty providing their perspective on successful approaches to research by highlighting significant recent works in their respective fields of interest.
Data-Driven Theory (KPHD-525-0)
This course focuses on "data-driven" economic theory---that is, papers that take the theory seriously and in combination with data aim to make prescriptive recommendations; for example, how to design a performance pay plan given workers’ productivity data, or how to design an internal labor market. We will cover papers from several literatures including mechanism design and auctions, contract theory, market design, internal labor markets, taxation, and social insurance. Deliverables include several presentations (a central goal of this course is to hone your presenting skills), evaluations of peers’ presentations, and a paper project.
Strategy and Organization (STRT-452-0)
This course focuses on the link between organizational structure and strategy, making use of the microeconomic tools taught in MECN-430. The core question is how firms should be organized to achieve their performance objectives. The first part of the course takes the firm's activities as given and studies the problem of organizational design; topics may include incentive pay, decentralization, transfer pricing, behavioral biases, and complementarities. The second part examines the determinants of a firm's boundaries and may cover such topics as outsourcing, horizontal mergers, and strategic commitment.
AI Foundations for Managers - Strategy (AIML-901ST-5)
A manager's job comprises two fundamental tasks: making predictions (e.g., forecasting demand, assessing risks, evaluating candidates) and acting on those predictions (e.g., launching products, making investments, hiring talent). Machine learning is a powerful tool for making predictions - and increasingly, decisions - with data. This course provides students with a high-level understanding of how artificial intelligence and machine learning fit into business strategy. You will learn to identify opportunities and risks in applying AI across various domains - from finance and marketing to operations and human resources. The course covers the major ML paradigms: supervised learning, unsupervised learning, reinforcement learning, and natural language processing including large language models. Through lectures, hands-on exercises (minimal coding required), and mini-case discussions, you will develop the vocabulary and frameworks to evaluate AI-driven solutions, recognize common pitfalls like data bias and interpretability challenges, and understand how ethical considerations must be integrated into strategic decisions.
