Detecting switching leadership in collective motion

Sachit Butail, Maurizio Porfiri

Research output: Contribution to journalArticle

Abstract

Detecting causal relationships in complex systems from the time series of the individual units is a pressing area of research that has attracted the interest of a broad community. As an open area of study, this entails the development of methodologies to unravel causal relationships that evolve over time, such as switching of leader-follower roles in animal groups. Here, we augment the information theoretic measure of transfer entropy to establish a fitness function suitable for optimal partitioning of time series data to robustly detect leadership switches in collective behavior. The fitness function computes the information outflow from any agent in the group and rewards large sample sizes while normalizing with respect to available information. Our results indicate that for information-rich interactions, leadership switches within a group can be detected over relatively short time durations, with more than 90% accuracy. On a real soccer dataset, instances of leadership counted using the proposed approach are interestingly correlated with ball possession.

Original languageEnglish (US)
Article number011102
JournalChaos
Volume29
Issue number1
DOIs
StatePublished - Jan 1 2019

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leadership
Collective Motion
Leadership
Time series
Switches
Fitness Function
fitness
Large scale systems
Switch
Animals
Entropy
switches
Collective Behavior
Time Series Data
Reward
normalizing
Partitioning
pressing
Complex Systems
Sample Size

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

Cite this

Detecting switching leadership in collective motion. / Butail, Sachit; Porfiri, Maurizio.

In: Chaos, Vol. 29, No. 1, 011102, 01.01.2019.

Research output: Contribution to journalArticle

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