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Polarizing crowds: Consensus and bipolarization in a persuasive arguments model.

Federico Barrera Lemarchand1, Viktoriya Semeshenko2, Joaquín Navajas1

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Summary
This summary is machine-generated.

This study models opinion formation through arguments, finding consensus in symmetrical interactions. However, homophily and confirmation bias lead to polarization, especially when agents struggle to accept opposing views.

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Area of Science:

  • Social dynamics modeling
  • Computational social science
  • Opinion formation

Background:

  • Understanding opinion formation is crucial for political and group decision-making.
  • Existing models often lack explicit argumentation mechanisms.

Purpose of the Study:

  • To present a novel model of opinion change driven by explicit argument exchange.
  • To analyze emergent collective states based on dynamic rules.

Main Methods:

  • Developed a computational model simulating opinion dynamics.
  • Introduced parameters for homophily and confirmation bias.
  • Analyzed model outcomes under varying agent and argument counts.

Main Results:

  • Symmetrical interactions lead to consensus.
  • Homophily and confirmation bias promote polarization by hindering acceptance of opposing arguments.
  • Stable states depend on the ratio of agents to available arguments.

Conclusions:

  • The model demonstrates how social dynamics can lead to polarized societies.
  • Argument exchange, influenced by biases, is a key driver of opinion divergence.
  • Conditions for consensus versus polarization are identified based on interaction rules and network properties.