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Environmental Path-Entropy and Collective Motion.

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Animal flocking behavior emerges from agents maximizing future path entropy. Surprisingly, low noise levels increase order, before higher noise causes a transition to disorder, mimicking natural systems.

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Area of Science:

  • Complex Systems
  • Collective Behavior
  • Theoretical Ecology

Background:

  • Animal swarming and flocking are complex emergent phenomena observed across diverse species.
  • Understanding the individual-level rules governing collective motion is crucial for ecological and evolutionary insights.
  • Previous models often focus on direct neighbor interactions, overlooking individual decision-making strategies.

Purpose of the Study:

  • To investigate emergent collective motion in 2D space based on individual agent principles.
  • To explore how maximizing future path entropy influences group dynamics and emergent states.
  • To analyze the impact of different noise types on collective order and transitions.

Main Methods:

  • Agent-based modeling of individuals in unbounded 2D space.
  • Individual reorientation strategy based on maximizing future path entropy (keeping options open).
  • Introduction of two noise types: standard orientational noise and cognitive noise affecting future path prediction.

Main Results:

  • Emergence of ordered (coaligned), disordered, and rotating cluster states, analogous to natural systems.
  • The ordered state exhibits an unusual order-disorder transition with increasing noise.
  • Order initially increases at low noise levels before decreasing as noise further increases.

Conclusions:

  • Maximizing future path entropy is a viable 'bottom-up' principle for generating collective animal behaviors.
  • The 'keeping options open' strategy can lead to robust emergent order and complex transitions.
  • Noise plays a critical role, with non-monotonic effects on collective order, highlighting the complexity of real-world systems.