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Multidimensional Diffusion Processes in Dynamic Online Networks

David Easley, Eleonora Patacchini and Christopher Rojas
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Eleonora Patacchini: Cornell University and EIEF
Christopher Rojas: Cornell University

No 1912, EIEF Working Papers Series from Einaudi Institute for Economics and Finance (EIEF)

Abstract: We develop a dynamic matched sample estimation algorithm to distinguish peer influence and homophily effects on item adoption decisions in dynamic networks, with numerous items diffusing simultaneously. We infer preferences using a machine learning algorithm applied to previous adoption decisions, and we match agents using those inferred preferences. We show that ignoring previous adoption decisions leads to significantly overestimating the role of peer influence in the diffusion of information, mistakenly confounding influence-based contagion with diffusion driven by common preferences. Our matching-on-preferences algorithm with machine learning reduces the relative effect of peer influence on item adoption decisions in this network significantly more than matching on earlier adoption decisions, as well other observable characteristics. We also show significant and intuitive heterogeneity in the relative effect of peer influence.

Pages: 36
Date: 2019, Revised 2019-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Journal Article: Multidimensional diffusion processes in dynamic online networks (2020) Downloads
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