Qichuan (Ethan) Yin

The existing literature on trading-favor systems, or scrip systems, generally assumes a fixed set of participants. We study the design of a trading-favor system that allows participants to join and exit. We examine how the dynamics of participation create challenges to participants’ strategies and, consequently, the design of the system. We introduce a token exchange mechanism that can be implemented by simple smart contracts. Such mechanism can regulate token distribution, evaluate token value and fairly adjust the total number of tokens in circulation. We show that the mechanism can induce participants to adopt an always-trade strategy in equilibrium, resulting in robust and highly efficient resource sharing in the decentralized system.

In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post-processing techniques, namely random selection and linear combination, to generate final private models satisfying any target privacy parameter. We provide privacy accounting of these approaches from the lens of Rényi DP and privacy loss distributions on general problems, as well as on private mean estimation, where we precisely characterize the privacy/utility tradeoffs and compare the two mechanisms. Empirically, we demonstrate the effectiveness of our approaches and validate our analyses on several models and both synthetic and real-world datasets.

AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al. (2021) shows that accuracy-based payment schemes suffer from incentive collapse: as AI accuracy improves, sustaining positive human effort requires unbounded payments. We study this problem in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. We propose a sentinel-auditing payment mechanism that enforces a strictly positive and controllable level of human effort at finite cost, independent of AI accuracy. Building on this incentive-robust foundation, we develop an incentive-aware active statistical inference framework that jointly optimizes (i) the auditing rate and (ii) active sampling and budget allocation across tasks of varying difficulty to minimize the final statistical loss under a single budget. Experiments demonstrate improved cost-error tradeoffs relative to standard active learning and auditing-only baselines.

Large Language Models (LLMs) have transformed data generation by enabling scalable synthetic data augmentation. This paper studies a fundamental question in market research: how much synthetic data should be used? In the existing literature, the proportion of LLM-generated synthetic data is usually treated as a hyperparameter, tuned by ad hoc experimentation. In addition, a more important yet unexplored question is how the optimal amount of synthetic data should vary across different population segments, e.g., between younger and older respondents. In this work, we address these challenges by deriving a closed-form expression for this highly heterogeneous hyperparameter vector that governs the proportion of synthetic data. This prescriptive characterization arises from our analysis of a logit choice model that integrates real and synthetic data, under which we establish a finite-sample complexity bound for the estimation error of the resulting hybrid estimator. Specifically, our expression for the heterogeneous proportion of synthetic data corresponds to the one that minimizes this upper bound. We further demonstrate the strong empirical performance of our closed-form expression using both synthetic data and a real-world vaccine preference dataset. Overall, our results suggest that the closed-form expression serves as a practical and reliable empirical rule of thumb for determining how much LLM-generated synthetic data to incorporate into market research studies.