Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions

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Date

2021-12

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Journal ISSN

Volume Title

Publisher

MDPI AG

Abstract

Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM).

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Keywords

DeGroot model, opinion diffusion, ocial influence, genetic algorithm, parameter estimation, social network intervention, pre-exposure prophylaxis

Citation

Johnson, K.L.; Walsh, J.L.; Amirkhanian, Y.A.; Carnegie, N.B. Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions. Int. J. Environ. Res. Public Health 2021, 18, 13394. https://doi.org/10.3390/ ijerph182413394
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