Wanying (Kate) Huang

Hi, and welcome! I am a Ph.D. candidate in economics at the California Institute of Technology, advised by Omer Tamuz. My research interests lie in the field of microeconomic theory, with a specific focus on information and social learning.

I received my Bachelor of Economics (with First Class Honors) from the University of Queensland in 2018, advised by Priscilla Man and Jeffrey Kline. Prior to that, I studied Economics at Shandong University.

I will be joining the Department of Economics at Monash University (Melbourne, Australia) as an Assistant Professor in September 2024.

My Curriculum Vitae

You can reach me at whhuang@caltech.edu.

Wanying Huang

Research

  1. Learning about Informativeness (Job Market Paper)
    2. Abstract: We study whether individuals can learn the informativeness of their information technology through social learning. As in the classic sequential social learning model, rational agents arrive in order and make decisions based on the past actions of others and their private signals. There is uncertainty regarding the informativeness of the common signal-generating process. We show that learning in this setting is not guaranteed, and depends crucially on the relative tail distributions of private beliefs induced by uninformative and informative signals. We identify the phenomenon of perpetual disagreement as the cause of learning and provide a characterization of learning in the canonical Gaussian environment.

  2. The Emergence of Fads in a Changing World | Working Paper
    3. Abstract: We study how fads emerge from social learning in a changing environment. We consider a simple sequential learning model in which rational agents arrive in order, each acting only once, and the underlying unknown state is constantly evolving. Each agent receives a private signal, observes all past actions of others, and chooses an action to match the current state. Since the state changes over time, cascades cannot last forever, and actions fluctuate too. We show that in the long run, actions change more often than the state. This describes many real-life faddish behaviors in which people often change their actions more frequently than what is necessary.

  3. Learning in Repeated Interactions on Networks

    4. with Philipp Strack and Omer Tamuz, Econometrica, 2024


  4. Social Learning in Lung Transplant Decisions | Work in Progress

    5. with Laura Doval, Federico Echenique and Yi Xin

Teaching

Teaching Assistant at Caltech

  • Mathematics (Ma 3/103) -- Introduction to Probability and Statistics

  • Economics (EC 11) -- Introduction to Economics

  • Economics (EC 105) -- Firms, Competition, and Industrial Organization