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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: May 21, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Topological analysis of complexity in multiagent systems.

Nicole Abaid1, Erik Bollt, Maurizio Porfiri

  • 1Department of Mechanical and Aerospace Engineering, Polytechnic Institute of New York University, Brooklyn, New York 11201, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|June 12, 2012
PubMed
Summary
This summary is machine-generated.

Researchers defined collective behavior as low-dimensional manifolds in group motion. Using the ISOMAP algorithm, they quantified this complexity in simulated particle systems and live fish schools, revealing inherent order in animal group dynamics.

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

  • Complex Systems
  • Biophysics
  • Computational Biology

Background:

  • Social organisms exhibit collective behaviors like schooling and flocking.
  • Understanding the underlying order in these group dynamics is challenging.
  • A quantitative definition for collective behavior is lacking.

Purpose of the Study:

  • To define and quantify collective behavior in social organisms.
  • To introduce a data-driven method for measuring order in group motion.
  • To validate the approach using computational models and empirical data.

Main Methods:

  • Defined collective behavior as low-dimensional manifolds in group motion.
  • Employed the ISOMAP algorithm for dimensionality reduction.
  • Applied the method to simulated self-propelled particle systems and fish school video data.

Main Results:

  • Increasing noise in particle simulations correlated with higher dimensionality.
  • Low-dimensional structures were absent in non-interacting particle simulations.
  • Similar low-dimensional structures were identified in live fish school data.

Conclusions:

  • Collective behavior is characterized by low-dimensional structures in group motion.
  • The ISOMAP algorithm provides a quantitative measure of order in biological systems.
  • Mathematical models can effectively capture emergent collective behaviors observed in animal groups.