Jonathan A. Libgober
Assistant Professor of Economics, University of Southern California
Welcome to my website!
I am a microeconomic theorist studying the acquisition, transmission or dynamics of information. I am particularly focused on the design of optimal policies, and am interested in both pure and applied theory.
I received my PhD in Economics from Harvard in May 2018. I have been at USC since the start of 2019.
Comments welcome! Presentation videos linked when available (though may not reflect current versions)
Evolutionarily Stable (Mis)specifications: Theory and Applications
with Kevin He (Last update: February 2021)
We introduce a selection criterion on behavioral biases in environments with learning, and show that it need not select for a bias-free worldview in some common applications.
Research Registries: Taking Stock and Looking Forward
We empirically study pre-registration patterns in economics, and discuss the implications of policies related to the timing of registration. The Mathematica notebook referenced in the Appendix can be found here.
Hypothetical Beliefs Identify Information
(Last update: March 2021)
I demonstrate how to recover a decisionmaker's information structure from posterior beliefs over states, together with posterior beliefs that each signal could be observed. In the process, I make new observations on the geometric structure of information.
Iterative Weak Learnability and Multi-Class AdaBoost
We propose a classification algorithm with generalizes AdaBoost to a multi-class, which requires an intuitive and simple to check condition to be valid.
I heard it was this or perish...
False Positives and Transparency
American Economic Journal: Microeconomics, Forthcoming
Lack of transparency over research methods can induce bias. But the incentive to de-bias may lead to more informative experiments.
The model introduced is one of costly communication with partial (sender) commitment.
Informational Robustness in Intertemporal Pricing
(with Xiaosheng Mu)
Review of Economic Studies,
Constant price paths deliver the optimal profit guarantee when a seller does not know how buyers learn about a product.
Formally, this paper introduces an informationally robust approach into the dynamic pricing literature.