Collective motion is observed in many systems, as diverse as an ensemble of droplets in a micro-channel, a group of migrating cells, a school of fish or a crowd of pedestrians. In these systems, the group level movement observed is non-trivially associated with the complex interactions between the agents that make up the collective. Hence, engineering such a system — for example, to design microfluidic systems that manipulate droplets as desired, or understanding a collective — for instance, to be able to ascertain the health of cells from movement information of the collective, becomes a challenge. The goal often in collective motion research is to identify the rules and equations governing the mechanics of collectives, bridging the gap between agent-level and the group-level descriptions. We ask: from movement information of the agents in the collective, how can one make useful inferences of the mechanics, interactions and the intrinsic characteristics of the agents in the collective?

In this talk, I will present two instances of my research where I deal with the above question.
1. First, I will talk about our work in trying to understand the emergence of ordered motion in ecological contexts, of social organisms. We use equation learning techniques and agent based models, to study the non-trivial role of noise in the emergent dynamics of the collective. We also use approaches from network science to bridge the agent—collective gap in these systems to help us understand how order emerges from interactions.
2. Next, I will present, our latest findings that demonstrate a fundamental challenge in inferring intrinsic characteristics of agents in a collective. We show that interaction effects mask the ability of an observer to infer the true properties of the agents in the collective. We develop a data-agnostic, physics-based framework that incorporates the effect of interactions in the inference problem, to make better inferences.

Finally, I will discuss how the tools developed in these studies to infer intrinsic dynamics of the agents will revolutionise both, the understanding of collective mechanics across different systems and engineering solutions for various technological applications.