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Most real world collectives, including active particles, living cells, andgrains, are heterogeneous, where individuals with differing propertiesinteract. The differences among individuals in their intrinsic properties haveemergent effects at the group level. It is often of interest to infer how theintrinsic properties differ among the individuals, based on their observedmovement patterns. However, the true individual properties may be masked byemergent effects in the collective. We investigate the inference problem in thecontext of a bidisperse collective with two types of agents, where the goal isto observe the motion of the collective and classify the agents according totheir types. Since collective effects such as jamming and clustering affectindividual motion, an agent's own movement does not have sufficient informationto perform the classification well: a simple observer algorithm, based only onindividual velocities cannot accurately estimate the level of heterogeneity ofthe system, and often misclassifies agents. We propose a novel approach to theclassification problem, where collective effects on an agent's motion isexplicitly accounted for. We use insights about the physics of collectivemotion to quantify the effect of the neighbourhood on an agent using aneighbourhood parameter. Such an approach can distinguish between agents of twotypes, even when their observed motion is identical. This approach estimatesthe level of heterogeneity much more accurately, and achieves significantimprovements in classification. Our results demonstrate that explicitlyaccounting for neighbourhood effects is often necessary to correctly inferintrinsic properties of individuals.

Disentangling intrinsic motion from neighbourhood effects in heterogeneous collective motion