Modelling collective motion based on the principle of agency: General framework and the case of marching locusts

Collective phenomena are studied in a range of contexts—from controlling locust plagues to efficiently evacuating stadiums—but the central question remains: how can a large number of independent individuals form a seemingly perfectly coordinated whole? Previous attempts to answer this question have reduced the individuals to featureless particles, assumed particular interactions between them and studied the resulting collective dynamics. While this approach has provided useful insights, it cannot guarantee that the assumed individual-level behaviour is accurate, and, moreover, does not address its origin—that is, the question of why individuals would respond in one way or another. We propose a new approach to studying collective behaviour, based on the concept of learning agents: individuals endowed with explicitly modelled sensory capabilities, an internal mechanism for deciding how to respond to the sensory input and rules for modifying these responses based on past experience. This detailed modelling of individuals favours a more natural choice of parameters than in typical swarm models, which minimises the risk of spurious dependences or overfitting. Most notably, learning agents need not be programmed with particular responses, but can instead develop these autonomously, allowing for models with fewer implicit assumptions. We illustrate these points with the example of marching locusts, showing how learning agents can account for the phenomenon of density-dependent alignment. Our results suggest that learning agent-based models are a powerful tool for studying a broader class of problems involving collective behaviour and animal agency in general.