We will deonstrate the use of (more or less!) Bayesian methods for inferring interaction rules between individuals in a system of collective animal motion. We examine a group of prawns moving in an effectively one-dimensional environment, which we reduce to a binary classification problem, aiming to infer the factors that predict whether an individual will change its direction of motion. Our results show that interactions are primarily driven by spatial proximity, that prawns tend to align with other individuals travelling in the opposite direction and that the effect of interactions persist over time to create a non-Markovian system. This extended introduction provides technical details of the models we examine and some preliminary findings. The full results of this analysis will be published when complete. © 2012 American Institute of Physics.
Prawns and probability
Perna A.;
2012-01-01
Abstract
We will deonstrate the use of (more or less!) Bayesian methods for inferring interaction rules between individuals in a system of collective animal motion. We examine a group of prawns moving in an effectively one-dimensional environment, which we reduce to a binary classification problem, aiming to infer the factors that predict whether an individual will change its direction of motion. Our results show that interactions are primarily driven by spatial proximity, that prawns tend to align with other individuals travelling in the opposite direction and that the effect of interactions persist over time to create a non-Markovian system. This extended introduction provides technical details of the models we examine and some preliminary findings. The full results of this analysis will be published when complete. © 2012 American Institute of Physics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
