Ph.D. Candidate in Economics, George Mason University
Interdisciplinary Center for Economic Science
I am on the 2026–2027 job market.
I am a Ph.D. candidate in Economics at George Mason University and a graduate research assistant at the Interdisciplinary Center for Economic Science. I study human and AI decision-making using economic theory, behavioral experiments, and meta-analysis.
My job market paper asks whose welfare AI prioritizes when making economic decisions on people’s behalf, and shows that the answer depends on the decision perspective a task induces. My other research connects deliberate strategy search with learning from feedback to explain how people choose and reset strategies. I also develop reproducible methods for AI-assisted evidence synthesis, and I independently created and launched AI Behavioral Profile, a bilingual platform for understanding and comparing AI behavior across models and their successive versions, using 30 behavioral measures.
Fields Applied Microeconomics · Behavioral Economics · Experimental Economics · Applied Econometrics · Computational Economics (AI)
Read paper on SSRNDOIOnline appendix
When people delegate economic decisions to AI, whose welfare does it prioritize? Using a reproducible AI-assisted evidence synthesis workflow, we synthesize 757 comparisons from 54 studies of economic games. In the main analysis, AI shares and cooperates more than humans on average and responds more strongly to unfair treatment, with substantial variation across studies. We model AI choices using two decision perspectives: prioritizing one participant's payoff (self-interested) or weighing both participants' payoffs (third-party). Experiments with GPT-4o and GPT-5.5 show that changing whom AI represents changes who benefits. A welfare model estimated in separate allocation and ultimatum tasks quantifies these priorities. Moving from the self-interested to the third-party perspective reduces the weight on the participant AI represents from 1.00 to 0.29 for GPT-4o and from 0.96 to 0.39 for GPT-5.5. Holding these estimates fixed, we identify perspectives from instructions in the studies included in our synthesis and predict AI choices and AI–human differences in one-shot, two-player games. The fixed predictions are positively and significantly associated with observed outcomes. In the literature sample, both parameter sets outperform most external benchmarks in ranking. In the main analysis, predictions using GPT-5.5 parameters also have significantly lower absolute errors than most benchmarks. Task descriptions can therefore change whose interests delegated AI serves.
How do people reset a strategy, and how does that choice shape what they do next? The framework connects deliberate strategy search at the start of a period with learning from feedback within it, tested in multi-period public goods experiments.
Develops a reusable workflow linking researcher-defined rules, AI execution, independent AI audits, deterministic computation, and offline reproduction.
Zhu, Q. (2021). Confirmation Bias and Gambler’s Fallacy Effect with Bayesian Method. In Proceedings of the 4th International Conference on Economic Management and Green Development (pp. 408–414). Springer. doi:10.1007/978-981-16-5359-9_47
A bilingual platform that helps researchers, developers, and users understand and compare AI behavior across models and their successive versions, using 30 behavioral measures. It documents task designs and scoring methods from economics and psychology for further research, with stability checks showing sensitivity to wording and presentation. I independently developed the research framework, behavioral tasks, evaluation workflow, and website.
An open-source workflow that helps researchers conduct reproducible AI-assisted meta-analyses and systematic reviews. It provides a downloadable skill for AI assistants, reusable templates, and tools for auditing decisions and reproducing results from saved records. It accompanies the RAES methods paper.
Student evaluations (Summer 2026): 4.7/5 for instructor preparation and course organization, and above the Economics department average on all 14 items (6 of 21 students responded).