Research Workshop "Observational Learning, Herding & Recommender Systems"

Date: July 6.- 8., 2022
Speaker: PD Dr. Christoph March


  • Rational herd models

  • Economic experiments on herd behavior

  • Behavioral social learning

  • Extensions: Financial markets, networks, platforms, AI


Imitation is a basic element of human behavior. Whether it is bestseller lists, recommendations on online portals such as Amazon or Twitter, or political participation, people place a high value on the decisions of others. While early studies tried to explain this propensity mainly with network effects and socio-psychological influences, a strongly growing theoretical, empirical and experimental literature emphasizes the influence of information externalities. Accordingly, imitation, or herd behavior, is a rational response to the information contained in observed decisions. Rational herd behavior differs in key characteristics from irrational herd behavior - with consequences for the design of economic and political mechanisms of information diffusion. At the same time, socio-psychological influences also play a role.

The workshop provides a survey of the economic literature on rational herd behavior, from the earliest models through experimental and empirical studies and findings to behavioral economic approaches. In addition, current research directions are highlighted and discussed.

Prior knowledge of game theory and experimental economics is helpful, but not a prereqisite.

This course is open for advanced masters students and PhD candidates. If you would like to participate, please send a short email describing your motivation, together with your  CV, and with a statement of whether you would like to participate virtually or in person, to kangkan.choudhury(at) before 22.06.2022. You will be notified if you are allowed to participate in the workshop by 25.06.2022.

The workshop will be held in a hybrid-mode. Upon acceptance, participants must make their own travel arrangements for their stay in Bamberg, if the wish to participate in the workshop in person. No financial support is available.

About the speaker:

PD Dr. Christoph March is a private lecturer at the University of Bamberg since December 2021. He has already been holding regular lectures since 2018, among others on game and contract theory as well as on the economics of digitalization. Previously, he worked at the TU Munich and the Paris School of Economics, among others. His research interests include models of social learning and the causes and effects of information-based herd behavior. To this end, he develops theoretical, experimental and statistical approaches to advance the development of a behavioral economic model of herding. In parallel, he also conducts research on the economics of artificial intelligence.


Wednesday, July 6

09:00 - 10:30 Introduction: basic model, information cascades, properties.

10:30 - 11:00 Coffee break

11:00 - 12:00 Robustness: the influence of decision and signal space

12:00 - 13:30 Lunch break

13:30 - 15:00 Empirical tests of rational herd behavior

15:00 - 15:15 Coffee break

15:15 - 16:45 Experiments I: Balls-and-Urns and the "Overweighting" Phenomenon

Thursday, July 7

09:00 - 10:30 Experiments II: Excessive herd behavior

10:30 - 11:00 Coffee break

11:00 - 12:00 Experiments III: Altruism and Welfare Optimum

12:00 - 13:30 Lunch break

13:30 - 15:00 Behavioral Social Learning

15:00 - 15:15 Coffee Break

15:15 - 16:45 Extensions I: Observations, Networks, Financial Markets

Friday, July 8

09:00 - 10:30 Extensions II: Design and Artificial Intelligence

10:30 - 11:30 Coffee Break, Questions & Answers


Wednesday, July 6

  • Chamley, C. (2004): Rational Herds: Economic Models of Social Learning, Cam-bridge University Press. 

  • Bikhchandani, S., D. Hirshleifer, O. Tamuz, and I. Welch (2021): “Information Cascades and Social Learning”, NBER Working Paper, No. 28887,
  • Bikhchandani, S., Hirshleifer, D., and Welch, I. (1992): “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades”, Journal of Political Economy, Vol. 100, pp. 992-1026.

  • Smith, L., and Sorensen, P. N. (2000): “Pathological Outcomes of Observational Learning”, Econometrica, Vol. 78 (2), pp. 371-398.

  • Zhang, J., and Liu, P. (2012): “Rational Herding in Microloan Markets”, Management Science, Vol. 58, pp. 892-912.

  • Anderson, L. R., and Holt, C. A. (1997): “Information Cascades in the Laboratory”, American Economic Review, Vol. 87 (5), pp. 847-862.

  • Weizsäcker, G. (2010): “Do We Follow Others When We Should? A Simple Test of Rational Expectations”, American Economic Review, Vol. 100, pp. 340-360.

  • Ziegelmeyer, A., March, C., and Krügel, S. (2013): “’Do We Follow Others When We Should? A Simple Test of Rational Expectations’: Comment”, American Economic Review, Vol. 103 (6), pp. 2633-2642.

Thursday, July 7

  • March, C., and Ziegelmeyer, A. (2018): “Excessive Herding in the Laboratory: The Role of Intuitive Judgments”, CESifo Working Paper, No. 6855,

  • March, C., and Ziegelmeyer, A. (2020): “Altruistic Observational Learning”, Journal of Economic Theory, Vol. 190, 105123.

  • Eyster, E., and Rabin, M. (2010): „Naïve Herding in Rich Information Settings”, American Economic Journal: Microeconomics, Vol. 2, 221-243.

  • Bohren, J. A. (2020): “Informational Herding with Model Misspecification”, Journal of Economic Theory, Vol. 163, 222-247.

  • Eyster, E., and Rabin, M. (2014): “Extensive Imitation is Irrational and Harmful”, Quarterly Journal of Economics, Vol. 129 (4), 1861-1898.

  • Rosenberg, D., and Vieille, N. (2019): “On the Efficiency of Social Learning”, Econometrica, Vol. 87 (6), 2141-2168.

  • Park, A., and Sabourian, H. (2011): “Herding and Contrarian Behavior in Financial Markets”, Econometrica, Vol. 79 (4), 973-1026.

Friday, July 8.

  • Che, Y.-K., and Hörner, J. (2018): “Recommender Systems as Mechanisms for Social Learning”, Quarterly Journal of Economics, Vol. 133 (2), 871-925.

  • March, C. (2021): “Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players”, Journal of Economic Psychology, Vol. 87, paper 102426.

  • Agrawal, A., J. Gans, and A. Goldfarb (2019): “The economics of artificial intelligence: an agenda”, Chicago and London: The University of Chicago Press.