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. 2021 Dec 20;18(24):13394.
doi: 10.3390/ijerph182413394.

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

Kara Layne Johnson  1 , Jennifer L Walsh  2 , Yuri A Amirkhanian  2 , Nicole Bohme Carnegie  1
Affiliations

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

Kara Layne Johnson et al. Int J Environ Res Public Health. .

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).

Keywords: DeGroot model; genetic algorithm; opinion diffusion; parameter estimation; pre-exposure prophylaxis (PrEP); social influence; social network intervention.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Boxplots and violin plots for root-mean-square error for recovery, modeling, and prediction by adjacency matrix type with number of time steps (horizontal) and performance metric (vertical) across facets.
Figure 2
Figure 2
Boxplots and violin plots for root-mean-square error for recovery, modeling, and prediction by ordinal scale with number of time steps (horizontal) and performance metric (vertical) across facets for the remove matrix.
Figure 3
Figure 3
Boxplots and violin plots for root-mean-square error for recovery, modeling, and prediction by decay parameter with bounded confidence parameter (horizontal) and performance metric (vertical) across facets for the remove matrix.
Figure 4
Figure 4
Root-mean-square error for recovery, modeling, and prediction by log-transformed RMSE for ordinal fit with shift of 0.0001 and number of time steps with number of items in ordinal scale (horizontal) and performance metric (vertical) across facets for the remove matrix.
Figure 5
Figure 5
Difference between observed and modeled opinions measured in number of bins by adjacency matrix variety and measure across all networks and runs of the algorithm, originally published in Johnson et al. [2].
Figure 6
Figure 6
Representations of network 4 using build and remove adjacency matrices with the seed identified in yellow and the agent who attended leadership training in green.
See this image and copyright information in PMC

References

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