• 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.

    • Curriculum Vitae

      Contact me for more info. (Last update: October 23rd, 2019)

    • Working Papers

      Comments welcome!

      Research Registries: Facts, Myths, and Possible Improvements

      with Eliot Abrams and John A. List NEW VERSION!

      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.

      Machine Learning for Strategic Inference

      with In-Koo Cho NEW VERSION!

      In a simple buyer-seller game, we exhibit an algorithm which ensures that agents play a Bayesian best reply (approximately).

      Unlearning via Retractions

      with Duarte Gonçalves and Jack Willis (Draft to be posted shortly--available upon request)

      We experimentally document the distinct ways in which retractions of information influence beliefs.

    • Publications

      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,

      Forthcoming

      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.

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