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Network diffusion of competing behaviors.

Yuan Hsiao1

  • 1Department of Sociology, Department of Statistics, University of Washington.

Journal of Computational Social Science
|October 21, 2024
PubMed
Summary

In competitive behavior diffusion, initial adopter size creates a "head-start advantage." This advantage is influenced by network transitivity, adoption thresholds, and early adopter connectedness, affecting diffusion outcomes.

Area of Science:

  • Social network analysis
  • Computational social science
  • Behavioral economics

Background:

  • Single-behavior diffusion models are well-established.
  • Competitive diffusion scenarios, involving multiple competing behaviors, present unique challenges.
  • Existing models do not fully capture the complexities of competing behavior adoption.

Purpose of the Study:

  • To investigate how key factors from single-behavior diffusion influence competitive diffusion.
  • To analyze the impact of network transitivity, adoption threshold, and early adopter connectedness in competitive diffusion.
  • To identify conditions under which countervailing ties affect behavioral diffusion inequality.

Main Methods:

  • Agent-based modeling simulations were employed to explore competitive diffusion dynamics.
Keywords:
Competitive diffusionbehavioral adoptionsimulationssocial networks

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  • The study systematically varied network transitivity, adoption thresholds, and initial adopter group sizes.
  • The influence of early adopter connectedness on diffusion outcomes was assessed.

    • Main Results:

    • Minor initial differences in adopter size significantly magnify, creating a pronounced "head-start advantage."
    • The extent of the head-start advantage is contingent upon the interplay between network structure and adoption parameters.
    • Countervailing ties can lead to unequal diffusion, depending on specific network and adoption conditions.

    Conclusions:

    • Network structure and adoption dynamics critically shape competitive diffusion outcomes.
    • Understanding the interaction between network transitivity, adoption thresholds, and early adopter characteristics is crucial for predicting diffusion success.
    • The findings offer insights into the spread of competing ideas, innovations, or social movements.