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Detecting switching leadership in collective motion.

Sachit Butail1, Maurizio Porfiri2

  • 1Department of Mechanical Engineering, Northern Illinois University, DeKalb, Illinois 60115, USA.

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This study introduces a new method to detect leadership changes in group behavior using transfer entropy. The approach accurately identifies leader shifts in collective dynamics, even with limited data.

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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Collective Behavior Analysis

Background:

  • Detecting causal relationships in complex systems from time series data is challenging.
  • Understanding evolving causal links, like leadership in groups, requires advanced methodologies.

Purpose of the Study:

  • To develop a robust method for detecting leadership switches in collective behavior.
  • To augment transfer entropy with a fitness function for time series partitioning.

Main Methods:

  • Augmented transfer entropy with a novel fitness function.
  • The fitness function optimizes time series partitioning by measuring information outflow.
  • The method rewards large sample sizes and normalizes information.

Main Results:

  • Successfully detected leadership switches in collective behavior with over 90% accuracy.
  • Effective detection achieved even with short time durations in information-rich interactions.
  • Identified leadership instances correlated with ball possession in a soccer dataset.

Conclusions:

  • The proposed method robustly detects leadership switches in collective behavior.
  • This approach offers a powerful tool for analyzing dynamic causal relationships in complex systems.
  • Findings have implications for understanding group dynamics in various fields.