Yichuan Cai

CV

You can download my CV in PDF format here.

Research (Research Statement)

Working papers:

Multi-Battle Group Contests (Job Market Paper)

with Sudipta Sarangi

We consider a situation in which two groups compete in a series of battles with complete information. Each group has multiple heterogenous players. The group who first wins a predetermined number of battles wins a prize which is a public good for the winning group. A discriminatory state-dependent contest success function will be employed in each battle. We found that in the subgame perfect Nash equilibrium (equilibria), the lower valuation players can only exert effort in earlier battles, while the higher valuation players may exert effort throughout the entire series of battles. The typical discouragement effect in a multi-battle contest is mitigated when players compete as a group. We also provide two types of optimal contest designs which can fully resolve the free-rider problem in group contests. The intermediate prize and weighted battle scenarios are considered in the extensions.

Asymmetric Multi-Player Contests with Sequential Battles and the All-Pay-Auction Selection Rule

with Kyung Hwan Baik

This paper investigates multi-battle contests with n players where n ≥ 2. Players compete in a sequence of simultaneous move component battles, and the player who first wins a certain number of component battles secures the prize. Each component battle is an all-pay auction. We proved that every state in a multi-battle contest with more than two players can be a standard all-pay auction. We find that the subgame perfect Nash equilibria of the multi-battle contest varies based on the valuation for the prize. However, for each reachable state, the subgame perfect Nash equilibrium is unique. We also find that the discouraged players make the total number of battles relatively small.

The Optimal Multi-Battle Contests

with Sudipta Sarangi

In this paper, we examine optimal contest design with multiple heterogeneous players. We allow the contest designer to have a generalized objective, a weighted function, which includes the following parts: the total effort; the winner’s effort; the maximal effort; and the winning probability of the strongest player. We provide a one-size-fits-all contest design that is optimal given any objective function. In the optimal contest, the designer will have one of the weaker players exhaust the strongest in the contest with infinite battles. We obtain the required conditions on different contest frameworks (e.g., all-pay auction and lottery contest) and bias instruments (e.g., head starts and multiplicative bias). This means the contest designer has multiple alternatives to design the optimal contest.

Working in progress:

1. Group Size Effect in Multi-Battle Contests: An Experiment Study (Data Recently Collected), with Kevin Zou

2. Momentum Effect in Best-of-Three Contests

3. Group Contests with Multiplicative Impact Function, with Sudipta Sarangi

4. Do higher weighted battles attribute higher efforts?

Teaching （Teaching Statement)

Instructor

Principles of Microeconomics

• Department of Economics, Virginia Tech, Winter 2022

Principles of Microeconomics

• Department of Economics, Virginia Tech, Summer 2021, Avg. Evaluation: 5.44/6.00

Principles of Microeconomics

• Department of Economics, Virginia Tech, Spring 2021, Avg. Evaluation: 5.23/6.00

Teaching Assistant

Department of Economics, Virginia Tech

Managerial Economics,

Fall 2021

Principles of Macroeconomics

Spring 2020, Fall 2020

Money and Banking

Fall 2017, Fall 2018, Spring 2019

Department of Economics, Sungkyunkwan University

Mathematical Economics

Fall 2015, Spring 2017

Game Theory and Applications

Spring 2016