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Social contagion induced by uncertain information.

Teruyoshi Kobayashi1

  • 1Department of Economics, Center for Computational Social Science, Kobe University, Kobe 657-8501, Japan.

Physical Review. E
|July 19, 2023
PubMed
Summary

Social contagion dynamics were studied under uncertainty. Misperceptions of neighbor activity can trigger global cascades, even when certainty would prevent them, highlighting the role of probabilistic threshold rules.

Area of Science:

  • Network Science
  • Sociophysics
  • Computational Social Science

Background:

  • Social contagion and information diffusion are key phenomena studied via threshold models.
  • Existing models often assume perfect observation of neighbors' states, a potentially unrealistic assumption.

Purpose of the Study:

  • To analyze global cascade dynamics within a standard threshold model incorporating uncertainty.
  • To investigate the impact of imperfect state observation on social contagion.

Main Methods:

  • Introduced a probabilistic threshold rule where individuals use statistical inference to estimate neighbor activity.
  • Analyzed the dynamics of global cascades under this uncertainty-infused threshold model.

Main Results:

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  • The spreading process becomes nonmonotonic due to updated state inferences.
  • Misperceptions of neighbor states can act as self-fulfilling prophecies, triggering cascades.
  • Cascades can emerge in scenarios where they would be impossible under deterministic conditions.

Conclusions:

  • Uncertainty in observing social networks significantly alters contagion dynamics.
  • Probabilistic threshold rules reveal how misperceptions can drive widespread social phenomena.
  • The findings challenge the assumptions of deterministic models and highlight the importance of information uncertainty in social spread.