Welcome to my website!
I am a PhD student at the Department of Economics, University College London.
My research field is microeconomic theory. I was on the 22/23 Job Market.
I joined TWS Partners in May 2023 as a consultant.
Find my CV here.
My preferred forename is Mia. My name in Chinese is 白果 (bái guǒ), meaning 'white fruit', or the plant ginkgo.
Job Market Paper, Submitted
This paper studies a dynamic information acquisition model with payoff externalities. Two players can acquire costly information about an unknown state before taking a safe or risky action. Both information and the action taken are private. The first player to take the risky action has an advantage but whether the risky action is profitable depends on the state. The players face the tradeoff between being first and being right. In equilibrium, for different priors, there exist three kinds of randomisation: when the players are pessimistic, they enter the competition randomly; when the players are less pessimistic, they acquire information and then randomly stop; when the players are relatively optimistic, they randomly take an action without acquiring information.
Linear Search or Binary Search: Time Risk Preferences and Pool Sizes (Link to the paper)
This paper studies how time risk attitude and patience level affect an agent's optimal choice of a sequence of pooled tests within a dynamic single agent's model. To disentangle the effects of time risk attitude and patient level, I consider a generalised expected discounted utility function. I show that when the agent's prior belief is uniform and the preference is time-consistent, only Linear Search and Binary Search can be optimal. All other sequences of the tests are suboptimal. Whether a sequence of linear searches or binary searches is optimal depends on the tradeoff between the time risk attitude and the patience level.
Learning How People Learn (Link to the paper)
This paper investigates whether it is possible for a designer to learn how a decision maker learns from the information. The decision maker observes signals about an unknown payoff-relevant state, learns about the state from the signals and then takes an action at each time. A designer designs the payoff the decision maker obtains, observes the decision maker's signals and actions, and then tries to learn how this decision maker learns from the information. This paper investigates a starting point of this problem: given the common knowledge that the decision maker is Bayesian but the prior is unknown, is it possible for the designer to learn the decision maker's prior? This paper studies the probability of the designer learning the decision maker's prior and characterises the optimal payoff structure that maximises this probability.