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Transition in collective motion decision making.

Biao Wang1, Yue Wu1, Guangwei Wang1

  • 1School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

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Summary

This study introduces a new model for collective motion, revealing how groups make decisions when faced with conflicting leaders. It shows a shift from averaging to leader-following in group behavior.

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Area of Science:

  • Physics
  • Biology
  • Complex Systems

Background:

  • Collective decision-making in biological groups relies on rapid information transfer.
  • Traditional models like Vicsek and Couzin do not capture ultrafast synchronous directional changes.

Purpose of the Study:

  • To propose a novel multiparticle model for collective motion with fast directional switching.
  • To elucidate the mechanism of group decision-making when leaders have divergent opinions.

Main Methods:

  • A multiparticle model based on inertial spin self-propulsion and collision avoidance was developed.
  • Analysis of the order parameter for group-velocity synchronization was performed.

Main Results:

  • The model successfully describes collective motion with rapid directional changes.
  • A decision-making mechanism was revealed, involving two divergent leader clusters.
  • A transition from compromising (group average) to preferred (leader cluster alignment) phases was observed.

Conclusions:

  • The proposed self-propelled particle spin model offers a more adequate description of group motion.
  • Findings provide new insights into follower decision-making when confronted with divergent leaders.