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

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  • Kara Layne Johnson

    (Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717, USA)

  • Jennifer L. Walsh

    (Medical College of Wisconsin Center for AIDS Intervention Research, Milwaukee, WI 53202, USA)

  • Yuri A. Amirkhanian

    (Medical College of Wisconsin Center for AIDS Intervention Research, Milwaukee, WI 53202, USA)

  • Nicole Bohme Carnegie

    (Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717, USA)

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).

Suggested Citation

  • Kara Layne Johnson & Jennifer L. Walsh & Yuri A. Amirkhanian & Nicole Bohme Carnegie, 2021. "Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions," IJERPH, MDPI, vol. 18(24), pages 1-22, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13394-:d:706312
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    References listed on IDEAS

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    1. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Veronika Grimm & Friederike Mengel, 2020. "Experiments on Belief Formation in Networks," Journal of the European Economic Association, European Economic Association, vol. 18(1), pages 49-82.
    4. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
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