Reinforcement Learning for Respondent-Driven Sampling

TL;DR

This paper introduces a reinforcement learning approach to adaptively optimize incentives in respondent-driven sampling, improving efficiency and insights in studying hidden populations.

Contribution

It develops an RL-based adaptive RDS design that tailors incentives over time, along with methods for valid post-study inference under adaptive sampling.

Findings

RL-based incentives improve sampling efficiency

The method reduces study costs

Provides insights into social network structures

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.