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Maximum Markovian order detection for collective behavior.

Yifan Zhang1, Ge Wu2, Xiaolu Liu3

  • 1School of Information Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces a new method to find the optimal Markov order in collective animal behavior, revealing network memory capacity. The approach effectively models interactions in animal groups like pigeons and dogs.

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Area of Science:

  • Collective behavior dynamics
  • Network science
  • Animal behavior analysis

Background:

  • Collective behavior studies have advanced significantly.
  • Markovian order is key to understanding inter-agent interactions in these systems.

Purpose of the Study:

  • To develop a method for determining the optimal maximum Markov order from collective behavior time-series data.
  • To quantify the maximum memory capacity of interacting networks in animal groups.

Main Methods:

  • Utilized a time-delayed causal inference algorithm.
  • Employed a multi-order graphical model.
  • Constructed high-order De Bruijn graphs for stochastic modeling.

Main Results:

  • Applied the method to time-series motion data from pigeon flocks, dogs, and midges.
  • Successfully determined the optimal maximum Markov order for these animal groups.
  • Validated the method's effectiveness in analyzing temporal network data of animal movements.

Conclusions:

  • The proposed method offers a practical solution for detecting the optimal maximum Markovian order in collective behavior.
  • This approach provides insights into the interacting relationships and memory capacity of animal groups.