Reinforcement Learning for Respondent-Driven Sampling

Justin Weltz, PhD

Is an听Emerging Political Economies and Applied Complexity Postdoctoral Fellow at the , Duke University

NOTE: Meet & Greet Justin Weltz from 3-3:30pm听in Room 1140

WHEN: Wednesday, October 8, 2025, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 91黑料网 College Avenue, Rm 1140;
NOTE: Justin Weltz will be presenting in-person at SPGH

Abstract

Respondent-driven sampling (RDS) is widely used to study hidden or hard-to-reach populations by incentivizing study participants to recruit their social connections. The success and efficiency of RDS can depend critically on the nature of the incentives, including their number, value, call to action, etc. Standard RDS uses an incentive structure that is set a priori and held fixed throughout the study. Thus, it does not make use of accumulating information on which incentives are effective and for whom. We propose a reinforcement learning (RL) based adaptive RDS study design in which the incentives are tailored over time to maximize cumulative utility during the study. We show that these designs are more efficient, cost-effective, and can generate new insights into the social structure of hidden populations. In addition, we develop methods for valid post-study inference which are non-trivial due to the adaptive sampling induced by RL as well as the complex dependencies among subjects due to latent (unobserved) social network structure. We provide asymptotic regret bounds and illustrate its finite sample behavior through a suite of simulation experiments.

Speaker Bio

I am an Emerging Political Economies and Applied Complexity Postdoctoral Fellow at the , where I work with , , and on statistical inference for complex network sampling techniques. I recently completed my Ph.D. at Duke University advised by and . My dissertation research focused on creating methods for the study and assistance of hard-to-reach populations.

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