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Updated: Dec 13, 2025
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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Noise-induced effects in collective dynamics and inferring local interactions from data
Jitesh Jhawar1, Vishwesha Guttal1
1Centre for Ecological Sciences, Indian Institute of Science, Bengaluru 560012, India.
Summary
We present a new method to analyze how random variations, or intrinsic noise, in small animal groups can surprisingly lead to collective order. This technique connects empirical data to theoretical models of state-dependent noise.
Area of Science:
- Collective behavior
- Theoretical biology
- Statistical physics
Background:
- Individual decisions in animal groups often follow probabilistic rules.
- Small group sizes in many species lead to intrinsic noise at the group level.
- This noise can be state-dependent or multiplicative, influencing collective dynamics.
Purpose of the Study:
- To demonstrate a method for characterizing the role of stochasticity in collective dynamics.
- To connect empirical data with theoretical predictions on noise-induced states.
- To bridge the gap between theoretical and empirical studies of collective behavior.
Main Methods:
- Utilizing high-resolution time-series data of collective dynamics.
- Employing two well-studied individual-based toy models of collective behavior.
- Developing a method to directly analyze stochasticity from data.
Main Results:
- The developed method allows for the characterization of intrinsic noise from empirical data.
- Group-level noise can encode significant information about individual-scale processes.
- The study validates the method using established models.
Conclusions:
- A novel method is described to link empirical collective behavior data with noise-induced phenomena.
- This approach facilitates the study of state-dependent noise in biological systems.
- The findings open new avenues for empirical research in collective behavior and noise.
Keywords:
collective behaviourfinite-size effectsfishmesoscopic dynamicsnoise-induced transitionsstochastic differential equationsMore Related Videos
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