Annie Liang

I am an Assistant Professor of Economics (primary appointment) and the Karr Family Assistant Professor of Computer Science at Northwestern University.

 

My research is in economic theory—in particular, learning and information—and the application of machine learning methods for model building and evaluation. Prior to joining Northwestern, I was an Assistant Professor of Economics at the University of Pennsylvania, and a postdoctoral researcher at Microsoft Research-New England.

Kellogg Global Hub, Office 3361

2211 Campus Drive

Evanston, Illinois 60208

Email: annie.liang at northwestern.edu

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Publications

Refereed Articles

1. Measuring the Completeness of Economic Models

with Drew Fudenberg, Jon Kleinberg and Sendhil Mullainathan

Journal of Political Economy, Vol. 130 (4), Pages 956-990, April 2022

extended abstract at EC'17

2. Dynamically Aggregating Diverse Information

with Xiaosheng Mu and Vasilis Syrgkanis

Econometrica, Vol. 90 (1), Pages 47-80, January 2022

extended abstract at EC'21

3. Complementary Information and Learning Traps

with Xiaosheng Mu

Quarterly Journal of Economics, Vol. 135 (1), Pages 389-448, February 2020

extended abstract at EC'18

4. Predicting and Understanding Initial Play

with Drew Fudenberg

American Economic Review, Vol. 109 (12), Pages 4112-4141, December 2019
plenary talk at EC'19 ("Highlights Beyond EC" session)

5. Inference of Preference Heterogeneity from Choice Data

Journal of Economic Theory, Vol. 179, Pages 275-311, January 2019

6. Optimal and Myopic Information Acquisition

with Xiaosheng Mu and Vasilis Syrgkanis

Proceedings of the 2018 ACM Conference on Economics and Computation, 2018

Invited Surveys

7. Machine Learning for Evaluating and Improving Theories

with Drew Fudenberg

SIGEcom Exchanges, Vol. 18 (1), Pages 4-11, 2020
 

Working Papers

8. Data and Incentives

with Erik Madsen

R&R at Theoretical Economics

extended abstract at EC'20

latest draft: April, 2022

9. The Transfer Performance of Economic Models

with Isaiah Andrews, Drew Fudenberg, and Chaofeng Wu

latest draft: July, 2022

10. Algorithmic Design: Fairness Versus Accuracy

with Jay Lu and Xiaosheng Mu 

latest draft: August, 2022

extended abstract at EC'22

11. How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories

with Drew Fudenberg and Wayne Gao

extended abstract at EC'21, selected as the "Exemplary AI and Computation Track Paper"

latest draft: August, 2021

12. Games of Incomplete Information Played by Statisticians

latest draft: June, 2021