Differentiation of Collective Behavior Based on Automated Discovery of Dynamical Kinds

Abstract

In this paper, we explore a model of collective behavior using EUGENE, an algorithm for automated discovery of so-called “dynamical kinds”. Two systems are of the same dynamical kind if their underlying causal dynamics are similar, as defined using dynamical symmetry. We apply EUGENE to simulation data from a model capable of generating a range of qualitatively different collective behaviors, from aligned motion to circular milling. These behaviors are measured using both global and local order parameters, and this data is analyzed with EUGENE. We find that EUGENE is capable of differentiating between these systems when global order parameters are used, and can only identify more coarse characteristics when local order parameters are considered.