Zhuoyang Liu
Zhuoyang Liu
Research Statement
I am broadly interested in how strategic interactions shape optimal decision-making under uncertainty and information asymmetry, and in applying these insights to practical challenges. During my PhD, I develop models to address decision-making problems in two domains: environmental conservation and healthcare operations. Beyond theoretical contributions, I focus on translating insights into actionable solutions by building numerical tools and integrating empirical data to calibrate models and validate their effectiveness.
Research Interests
- Mechanism design
- Stochastic modeling
- Sustainability
- Healthcare operations
Job Market Paper
Designing cost-effective incentives for farmers to engage in conservation is difficult, as farmers optimize their actions based on private, heterogeneous cost structures unknown to program designers. To support better program design, I build a principal-agent screening model that incorporates both farmers’ strategic behavior and the ecological and equity goals of program sponsors. I find that optimal contracts should balance upfront and outcome-based payments to simultaneously support liquidity and provide incentives. Upfront transfers should depend only on observable traits, while outcome-based payments should adjust for unobserved heterogeneity in costs and preferences. In addition to detailed analytical characterizations of the optimal contract, I also develop numerical frameworks to calibrate my model, demonstrating the validity and effectiveness of my insights using field data, and enabling practitioners to easily access my incentive design methodology.
Work in Progress
Capacity planning for hospital nurses is complicated by uncertain patient demand and persistent labor shortages. We analyze how hospitals should coordinate long- and short-term nurse hiring, accounting for the strategic career choices of prospective nurses, who may also consider gig work or alternative occupations. We characterize how optimal staffing policies shift with labor market tightness and identify equilibrium labor allocations under different demand–supply regimes. When labor supply is relatively abundant, optimal staffing mirrors classic Newsvendor solutions. As supply becomes scarce, long- and short-term employment options interact in complex ways, yielding highly nontrivial capacity planning decisions.