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This study introduces a new opinion formation model where individuals can connect with dissimilar opinions. The research demonstrates that consensus can be reached, even with diverse viewpoints, through a bounded confidence plus random selection approach.

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Area of Science:

  • Social dynamics
  • Computational social science
  • Opinion formation modeling

Background:

  • Traditional opinion formation models assume limited interaction based on opinion similarity.
  • Real-world social networks often include connections with individuals holding diverse opinions.
  • Existing models do not fully capture the complexity of heterogeneous social interactions.

Purpose of the Study:

  • To propose a novel opinion formation model incorporating both bounded confidence and random selection of dissimilar opinions.
  • To investigate the conditions under which consensus can be achieved in a group with diverse viewpoints.
  • To analyze the impact of similarity-based probability rules on opinion convergence.

Main Methods:

  • Development of a bounded confidence plus random selection model for opinion dynamics.
  • Simulation of agent interactions with long-range neighbors outside the confidence bound.
  • Mathematical proof for consensus achievement under different selection rules.

Main Results:

  • The proposed model demonstrates that consensus can be reached within a finite time, even with connections to dissimilar opinions.
  • When agents ignore the confidence bound and select neighbors randomly based on similarity, consensus is achieved in a probabilistic sense.
  • The similarity-based probability rule plays a crucial role in facilitating opinion convergence.

Conclusions:

  • The bounded confidence plus random selection model offers a more realistic representation of opinion formation in social networks.
  • Consensus is achievable in groups with diverse opinions, challenging previous assumptions in opinion dynamics.
  • The findings have implications for understanding information diffusion and social cohesion in complex systems.