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Transfer entropy dependent on distance among agents in quantifying leader-follower relationships
Udoy S Basak1,2, Sulimon Sattari3, Motaleb Hossain3,4
1Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Sapporo, Hokkaido 060-0812, Japan.
Biophysics and Physicobiology
|June 28, 2021
Summary
Identifying leaders in collective motion is key. Using interaction domain data significantly improves leader-follower classification in synchronized systems.
Area of Science:
- Collective motion
- Systems biology
- Mathematical modeling
Background:
- Synchronized movement is prevalent in biological systems, from unicellular to multicellular organisms.
- Understanding the regulation of synchronized behavior in collective motion is a significant scientific challenge.
- The concept of leader agents influencing group dynamics is a key hypothesis in collective motion.
Purpose of the Study:
- To review mathematical models of leadership in collective motion.
- To enhance the leader-follower classification problem.
- To investigate methods for identifying influential agents within a group.
Main Methods:
- Review of various mathematical models representing different leadership types.
- Simulation modeling to test classification strategies.
- Analysis of linear and information-theoretic schemes for quantifying influence.
- Exploration of methods to identify agent interaction domains from motion data.
Main Results:
- The use of interaction domain information significantly improves leader-follower classification.
- Both linear and information-theoretic schemes benefit from interaction domain data.
- Effective schemes for identifying interaction domains from motion data were reviewed.
Conclusions:
- Interaction domain information is crucial for accurately classifying leaders in collective motion.
- Improved leader identification can be achieved through advanced modeling and data analysis.
- This work provides a foundation for further research into the dynamics of synchronized systems.
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