Title
Reinforcement Learning for Respondent-Driven Sampling
Abstract
Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of an RDS study, the current wave of study participants are incentivized to recruit the next wave through their social connections. The success and efficiency of RDS can depend critically on attributes of incentives and the underlying (latent) network structure. We propose a reinforcement learning-based adaptive RDS design to optimize some measure of study utility, e.g., efficiency, treatment dissemination, reach, etc. Our design is based on a branching process approximation to the RDS process, however, our proposed post-study inferential procedures apply to general network models even when the network is not fully identified. Simulation experiments show that the proposed design provides substantial gains in efficiency over static and two-step RDS procedures.
Bio
Institutional page (external link)
Eric B. Laber, James B. Duke Distinguished Professor, Biostatistics and Bioinformatics
Statistical Sciences, Duke University
Practical
Eric Laber is presenting in person at the Blindern campus, Niels Henrik Abels building, 8th floor.
The Tuesdays seminars series are devoted to various topics relevant for the Integreat´s research focus. Presenters from the Integreat community and beyond have 40 minutes to present, followed by a group discussion.
Seminars are open for attendance for everybody.
For those unable to attend in person: https://uio.zoom.us/j/62535744013
Point of contacts:
- Organiser and moderator: Professor Arnoldo Frigessi, University of Oslo
- Integreat Administrative Leader Maria Dikova, University of Oslo