Dynamical maximum entropy approach to flocking.

Area of Science:

• Collective animal behavior
• Active matter physics
• Statistical mechanics

Background:

• Understanding how individual interactions lead to coordinated group movement is a key challenge in collective behavior.
• Existing models often assume equilibrium conditions, which may not apply to rapidly changing animal group dynamics.
• Inferring these dynamics from observational data is crucial for validating theoretical models.

Purpose of the Study:

• To develop a novel method for inferring out-of-equilibrium alignment dynamics in collectively moving animal groups.
• To differentiate between dynamic and static inference methods based on the rate of neighborhood change.
• To establish the applicability of the method to various active matter systems.

Main Methods:

• Derivation of a new method based on maximum entropy model distributions.
• Incorporation of temporal and spatial correlations in flight direction.
• Comparison of dynamical inference with static inference under varying neighborhood evolution rates.
• Validation using simulated data.

Main Results:

• The new dynamical inference method accurately learns model parameters when animal neighborhoods evolve rapidly.
• Static inference methods fail when neighborhood evolution is fast.
• The dynamical method converges to the static procedure when detailed balance is satisfied and neighbors change slowly.
• The method's validity was confirmed on simulated datasets.

Conclusions:

• A robust method for inferring out-of-equilibrium alignment dynamics in animal groups has been established.
• The method's performance is dependent on the rate of change in group interactions.
• This approach offers a powerful tool for analyzing collective motion in various active matter systems.