Shaofei Jiang

PhD Student in Economics at UT Austin

Shaofei Jiang

I am a PhD student in Economics at the University of Texas at Austin. My research interests are information economics and game theory.

E-mail: shaofeij@utexas.edu

Research

  • Shaofei Jiang. (2020). Equilibrium refinement in finite evidence games.

Abstract: Evidence games study situations where a sender persuades a receiver by selectively disclosing hard evidence about an unknown state of the world. Such games often possess multiple equilibria. Hart et al. (2017) propose to focus on truth-leaning equilibria, i.e., perfect Bayesian equilibria where the sender prefers disclosing truthfully when indifferent, and the receiver takes any off-path disclosure at face value. They show that a truth-leaning equilibrium is an equilibrium of a perturbed game where the sender has an infinitesimal reward for truth-telling. We show that, when the receiver's action space is finite, truth-leaning equilibrium may fail to exist, and it is not equivalent to equilibrium of the perturbed game. To restore existence, we introduce a disturbed game where there is a small uncertainty to the receiver's payoff. A purifiable equilibrium is a truth-leaning equilibrium in any infinitesimally disturbed game. It exists and features a simple characterization. Moreover, a truth-leaning equilibrium that is also purifiable is an equilibrium of the perturbed game.

Abstract: This paper studies disclosure games, allowing for large evidence spaces and general disclosure rules. A sender observes a piece of evidence about an unknown state and tries to influence the posterior belief of a receiver by disclosing evidence with possible omission. We focus on truth-leaning equilibria, where the sender discloses truthfully when doing so is optimal, and the receiver does not discount off-path messages. We show that, given any disclosure rule, all equilibria are payoff equivalent and characterize the unique equilibrium value function of the sender. We also propose a method to construct equilibria for a broad class of games. Applying these results, we study left-censored disclosure, where evidence is a sequence of signals, and the sender can truncate evidence from the left. In equilibrium, seemingly sub-optimal messages are disclosed, and the sender's disclosure contains the longest truncation that yields the maximal difference between the number of favorable and unfavorable signals.

Abstract: I study a model of firm dynamics where a firm can invest in product quality and exert efforts to obtain good publicities. The market learns from good publicities without directly observing quality, investment, or efforts. I analyze the relationship between the firm's advertisement and investment incentives. I show that any equilibrium has a threshold structure: the firm invests and advertises when the market belief is low, only advertises for a range of intermediate beliefs, and does neither when the belief is high. I further study whether increased frequency of publicity opportunities will lead to increased investment, and how these results differ when the market learns from bad news.

Teaching

As instructor:

As teaching assistant:

  • Microeconomics II (PhD), for Prof. Caroline Thomas, UT Austin (2018-20) | Select TA session notes
  • Math for Economists (PhD), for Prof. Maxwell Stinchcombe, UT Austin (2019)
  • Probability & Statistics (PhD), for Prof. Maxwell Stinchcombe & Haiqing Xu, UT Austin (2017-18)
  • Macroeconomic Theory, for Prof. Felipe Schwartzman, UT Austin (2017)
  • Introduction to Microeconomics, for Prof. Thomas Wiseman, UT Austin (2016)
  • Applied Econometrics, for Prof. Wanchuan Lin, Peking University (2016)