Abstract

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred.

Highlights

  • The collective behavior exhibited by interacting individuals in a population has recently attracted interest in scientific and engineering communities

  • We show that our models follow the source dynamics well, and from them we are able to infer that significant collective behavior occurs in pigeon flights

  • Using the methodology we presented, several models were retrieved for each dataset

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Summary

Introduction

The collective behavior exhibited by interacting individuals in a population has recently attracted interest in scientific and engineering communities. The movement of groups of animals is a common and well studied example involving the emergence of collective behavior in an interacting population. It is well known that animals tend to work in groups to achieve goals; simple examples that can come to mind are ant colonies, herds, fish schools, and bird flocks. The ability to gain accurate positional data from GPS devices on pigeons, has opened the door to more advanced and meaningful analysis of flocking [1]. Photographic data has lead to deeper analysis of the interaction properties of flocking [2]. With accurate 3D positional data, it is possible to perform statistical analysis which leads to the understanding of the structural and behavioral properties of flocking

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