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Bounded-confidence opinion models with random-time interactions.

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Summary
This summary is machine-generated.

This study introduces random-time interactions into bounded-confidence models (BCMs) for opinion dynamics. Findings show that inter-event-time distributions significantly impact model behavior, especially for non-Markovian processes.

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Area of Science:

  • Social dynamics
  • Complex systems
  • Statistical physics

Background:

  • Opinion dynamics models simulate how agents change beliefs through interaction.
  • Bounded-confidence models (BCMs) assume agents compromise opinions if sufficiently similar.
  • Existing BCM research often assumes deterministic interaction times, ignoring social randomness.

Purpose of the Study:

  • To incorporate random-time interactions into BCMs using renewal processes.
  • To analyze the impact of inter-event-time distributions (ITDs) on BCM dynamics.
  • To compare random-time BCMs with deterministic-time BCMs.

Main Methods:

  • Utilized renewal processes to model random social interaction event times.
  • Derived approximate governing equations for time-dependent BCM expectations.
  • Numerically examined transient and steady-state dynamics on various networks.

Main Results:

  • BCMs with Markovian ITDs exhibit consistent statistical properties with the same mean ITD.
  • BCMs with non-Markovian ITDs show behavior dependent on ITD type, even with identical means.
  • Identified quantitative impacts of ITDs on transient dynamics and convergence.

Conclusions:

  • Random-time interactions are crucial for realistic opinion dynamics modeling.
  • The choice of inter-event-time distribution significantly influences opinion convergence and stability.
  • Future research should consider diverse ITDs for comprehensive BCM analysis.