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

Kara Layne Johnson1, Jennifer L Walsh2, Yuri A Amirkhanian2

  • 1Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717, USA.

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|December 24, 2021
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Summary

This study validates a genetic algorithm for modeling opinion diffusion in social networks, finding it effective even with imperfect data. The method aids in predicting the impact of public health interventions like pre-exposure prophylaxis (PrEP) uptake.

Keywords:
DeGroot modelgenetic algorithmopinion diffusionparameter estimationpre-exposure prophylaxis (PrEP)social influencesocial network intervention

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Area of Science:

  • Computational Social Science
  • Network Science
  • Public Health Interventions

Background:

  • Social influence is key for public health interventions, but effectiveness assessment requires opinion diffusion modeling.
  • Previous work developed a genetic algorithm for the DeGroot model in small networks.
  • Practical applications demand assessing algorithm performance under realistic, imperfect conditions.

Purpose of the Study:

  • Evaluate a genetic algorithm's performance for opinion diffusion modeling under challenging, real-world conditions.
  • Assess the impact of ordinal opinion data, network sampling, and model misspecification on algorithm accuracy.
  • Apply findings to understand opinion dynamics for social network interventions, specifically pre-exposure prophylaxis (PrEP) uptake.

Main Methods:

  • Simulation study to test algorithm performance with ordinal opinion measurements.
  • Investigated algorithm robustness against network sampling and model misspecification.
  • Applied simulation insights to analyze opinion diffusion for a social network intervention targeting PrEP uptake.

Main Results:

  • The genetic algorithm demonstrates robustness across various non-ideal conditions, including ordinal data and model misspecification.
  • Algorithm performance is sensitive to the precision of the ordinal opinion scale.
  • Complete network sampling is not essential for the method's effective application.

Conclusions:

  • The validated genetic algorithm is a reliable tool for modeling opinion diffusion in complex social networks.
  • The findings support the use of this method for projecting the effectiveness of public health interventions.
  • Insights gained can inform the design of social network strategies to increase PrEP uptake among key populations.