Using Ego Network Data to Inform Agent-based Models of Diffusion

Authors

Jeffrey A. Smith, University of Nebraska-LincolnFollow
Jessica Burow, The Hartford

Date of this Version

4-2018

Citation

Smith, Jeffrey A. and Jessica Burow. "Using Ego Network Data to Inform Agent-Based Models of Diffusion." Sociological Methods & Research (2018), 46 pp.

Advance online publication April 2018 @ http://journals.sagepub.com/doi/pdf/10.1177/0049124118769100

doi: 10.1177/0049124118769100.

Comments

Copyright © 2018 Jeffrey A. Smith and Jessica Burow. Published by SAGE Publications. Used by permission.

Abstract

Agent-based modeling holds great potential as an analytical tool. Agent-based models (ABMs) are, however, also vulnerable to critique, as they often employ stylized social worlds, with little connection to the actual environment in question. Given these concerns, there has been a recent call to more fully incorporate empirical data into ABMs. This article falls in this tradition, exploring the benefits of using sampled ego network data in ABMs of cultural diffusion. Thus, instead of relying on full network data, which can be difficult and costly to collect, or no empirical network data, which is convenient but not empirically grounded, we offer a middle-ground, one combining ABMs with recent work on network sampling. The main question is whether this approach is effective. We provide a test of the approach using six complete networks; the test also includes a range of diffusion models (where actors follow different rules of adoption). For each network, we take a random ego network sample and use that sample to infer the full network structure. We then run a diffusion model through the known, complete networks, as well as the inferred networks, and compare the results. The results, on the whole, are quite strong: Across all analyses, the diffusion curves based on the sampled data are very similar to the curves based on the true, complete network. This suggests that ego network sampling can, in fact, offer a practical means of incorporating empirical data into an agent-based model.