Welcome to my website!

I am a PhD student at the Department of Economics, University College London.

My research field is microeconomic theory.

Contact: guo.bai.15@ucl.ac.uk

My preferred forename is Mia. My name in Chinese is 白果 (bái guǒ), meaning 'white fruit', or the plant ginkgo.


Private Information Acquisition and Preemption: a Strategic Wald Problem (Paper on arXiv)

This version: July 2022

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: the Optimal Pool Size with Time Risk Preferences

Conducting tests allows people to find positive specimens or defective items. Pooled testing is a method combining specimens together and conducting one single test. A test is returned positive if the pooled specimen contains the positive specimen and is returned negative otherwise. Linear Search refers to the test with a pool size of one. It allows the agent to find the positive specimen with a minimum of one test, but it is risky in terms of when the positive specimen is found. Binary Search refers to the test with a pool size of half of the specimens. It can quickly eliminate the negative specimens but does not allow the agent to learn immediately as follow-up tests are needed. This paper studies how time risk attitude and patience level affect an agent's optimal choice of a sequence of the 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.