Algorithms for seeding social networks can enhance the adoption of a public health intervention in urban India

Marcus Alexander, Laura Forastiere, Swati Gupta and Nicholas A. Christakis
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Marcus Alexander: a Yale Institute for Network Science, Yale University, New Haven, CT 06511;
Laura Forastiere: a Yale Institute for Network Science, Yale University, New Haven, CT 06511;; b Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510;
Swati Gupta: c Tata Consumer, Mumbai 400 053, India;
Nicholas A. Christakis: a Yale Institute for Network Science, Yale University, New Haven, CT 06511;; d Department of Sociology, Yale University, New Haven, CT 06511;; e Department of Statistics and Data Science, Yale University, New Haven, CT 06511;; f Department of Medicine, Yale School of Medicine, New Haven, CT 06510

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 30, e2120742119

Abstract: A deep understanding of social networks can be used to create an artificial tipping point, changing population behavior by fostering behavioral cascades. Here, we experimentally test this proposition. We show that network-based targeting substantially increases population-level adoption of new behaviors. In part, this works by driving indirect treatment effects among the nontargeted members of the population (among people who were not initially part of the treatment group but who were affected by treatment of others in their population). The techniques we demonstrate can be easily implemented in global health (and elsewhere), as they do not require knowledge of the whole network. The novel pair-targeting technique explored here is particularly powerful and easy to implement.

Keywords: social networks; network targeting; public health (search for similar items in EconPapers)
Date: 2022
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