About

George Georgiadis is an Associate Professor in the Strategy department at the Kellogg School of Management, Northwestern University. He is an organizational economist with interests in applied microeconomic theory and artificial intelligence. His research studies how incentives, particularly performance pay, influence the behaviors of individuals and organizations. His recent work explores the data organizations need and how to use it in conjunction with contract theoretic models to optimize performance pay plans. His research 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, and the Journal of Public Economics. Professor Georgiadis teaches courses such as AI Foundations for Managers - Strategy, which equips MBA students with frameworks to evaluate and apply AI and machine learning across business domains, and Data-Driven Theory for PhD students, focusing on research that combines theoretical models with real data to answer prescriptive questions related to incentive design and policy formulation. Prior to joining Kellogg, he taught at the California Institute of Technology and Boston University. He holds a Ph.D. in Management from the UCLA Anderson School of Management, along with master's degrees in Electrical Engineering and Economics from UCLA, and a B.S. in Electrical and Computer Engineering from Aristotle University in Greece.

Research topics

• Computer Science
• Economics
• Microeconomics
• Political Science
• Computer Security
• Mathematical economics
• Econometrics
• Mathematics
• Law
• Engineering
• Operations research
• Finance
• Business
• Labour economics
• Arithmetic

Selected publications

• Feedback Design in Dynamic Moral Hazard

  Econometrica · 2025-01-01 · 2 citations

  article

  We study the joint design of dynamic incentives and performance feedback for an environment with a coarse (all‐or‐nothing) measure of performance, and show that hiding information from the agent can be an optimal way to motivate effort. Using a novel approach to incentive compatibility, we derive a two‐phase solution that begins with a “silent phase” where the agent is given no feedback and is asked to work non‐stop, and ends with a “full‐transparency phase” where the agent stops working as soon as a performance threshold is met. Hiding information leads to greater effort, but an ignorant agent is also more expensive to motivate. The two‐phase solution—where the agent's ignorance is fully frontloaded—stems from a “backward compounding effect” that raises the cost of hiding information as time passes.

  PublisherDOI

• Robust Contracts: A Revealed Preference Approach

  The Review of Economics and Statistics · 2024-08-22 · 1 citations

  articleSenior author

  Abstract We study an agency model in which the principal knows the agent-optimal actions in response to K “known” contracts but is unaware of other actions available or their costs, and seeks a contract to maximize worst-case profits. The optimal contract is a mixture of the known contracts and output. Moreover, when K = 1, the single known contract maximizes the principal's profit guarantee, whereas with two known contracts, the optimal mixture puts positive weight on one of the known contracts. Our methodology is straightforward to implement, a point that we demonstrate using data from an experimental study of different incentive schemes.

  PublisherDOI

• Contracting with moral hazard

  Edward Elgar Publishing eBooks · 2024-12-31 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Feedback Design in Dynamic Moral Hazard

  2024-07-08 · 1 citations

  article

  The early stages of many careers serve as a trial period where employees seek a high-value reward (e.g., a promotion) while working hard to demonstrate their productivity. A key design component of such jobs is the performance feedback offered to the employee, as it allows them to adjust their behaviors and learn what their future rewards might look like. While some experts argue that a policy of full transparency is best---that is, keeping employees fully apprised of their performance---such practice is far form uniform as employers may see a strategic gain from hiding information or postponing its release.

  PublisherDOI

• Flexible Moral Hazard Problems

  Econometrica · 2024-01-01 · 12 citations

  article1st authorCorresponding

  This paper considers a moral hazard problem where the agent can choose any output distribution with a support in a given compact set. The agent's effort‐cost is smooth and increasing in first‐order stochastic dominance. To analyze this model, we develop a generalized notion of the first‐order approach applicable to optimization problems over measures. We demonstrate each output distribution can be implemented and identify those contracts that implement that distribution. These contracts are characterized by a simple first‐order condition for each output that equates the agent's marginal cost of changing the implemented distribution around that output with its marginal benefit. Furthermore, the agent's wage is shown to be increasing in output. Finally, we consider the problem of a profit‐maximizing principal and provide a first‐order characterization of principal‐optimal distributions.

  PublisherDOI

• Robust Contracts: A Revealed Preference Approach

  2023-07-07 · 2 citations

  articleSenior author

  We study an agency model in which the principal has outcome data under different incentive schemes and aims to design an optimal contract under minimal assumptions about the way the agent responds to incentives. Events unfold as follows: (1) the principal offers a contract---a mapping from output to nonnegative payments; (2) the agent chooses costly action---a probability distribution over output; and (3) output and payoffs are realized. The principal has outcome data under K different contracts which, sidestepping estimation error, enables her to recover the action corresponding to each of these contracts. We assume that the agent best-responds to the offered contract and has quasi-linear preferences over money and actions, but we make no further assumptions about the production environment. The principal does not have prior beliefs about any of the unknown aspects of the environment. Instead, she seeks a contract that maximizes worst-case profit.

  PublisherDOI

• Optimal technology design

  Journal of Economic Theory · 2023-02-10 · 5 citations

  articleOpen accessCorresponding
  PublisherOA PDFDOI

• Robust Contracts: A Revealed Preference Approach

  SSRN Electronic Journal · 2022 · 5 citations

  Senior authorCorresponding
  • Computer Science
  • Economics
  • Business
  PublisherDOI

• Contracting with Moral Hazard: A Review of Theory & Empirics

  SSRN Electronic Journal · 2022-01-01 · 6 citations

  reviewOpen access1st authorCorresponding
  PublisherDOI

• Optimal Feedback in Contests

  The Review of Economic Studies · 2022 · 18 citations

  • Computer Science
  • Computer Science
  • Operations research

  Abstract We obtain optimal dynamic contests for environments where the designer monitors effort through coarse, binary signals—Poisson successes—and aims to elicit maximum effort, ideally in the least amount of time possible, given a fixed prize. The designer has a vast set of contests to choose from, featuring termination and prize-allocation rules together with real-time feedback for the contestants. Every effort-maximizing contest (which also maximizes total expected successes) has a history-dependent termination rule, a feedback policy that keeps agents fully apprised of their own success, and a prize-allocation rule that grants them, in expectation, a time-invariant share of the prize if they succeed. Any contest that achieves this effort in the shortest possible time must in addition be what we call second chance: once a pre-specified number of successes arrive, the contest enters a countdown phase where contestants are given one last chance to succeed.

  PublisherDOI

Frequent coauthors

• T. Renee Bowen

  42 shared

• Nicolas Lambert

  41 shared

• Balázs Szentes

  32 shared

• Christopher S. Tang

  12 shared

• Jeroen M. Swinkels

  7 shared

• Kumar Rajaram

  University of California, Los Angeles

  5 shared

• Steven A. Lippman

  5 shared

• Christine‐Ivy Liacos

  National and Kapodistrian University of Athens

  4 shared

Awards & honors

• Fellow of the Society for the Advancement of Economic Theory…
• Excellence in Refereeing Award, Review of Economic Studies