A new study of complex systems supports a growing trend that focuses more on analyzing a system’s collective behavior rather than on trying to uncover the underlying interaction mechanisms.
When observing a flock of starlings swirling through the sky in perfect coordination—a phenomenon known as murmuration—we witness the elegant interplay of individual actions creating collective behavior. In trying to understand these mesmerizing patterns, researchers can isolate simple rules based on an individual bird’s field of vision and distance to its neighbors, but there’s always a question of whether the model is really capturing the processes behind the bird interactions (Fig. 1). The problem is a general one in complex systems research, and it comes down to distinguishing mechanisms (the rules governing interactions) from behaviors (the observable patterns that emerge).
A good way to study mechanisms versus behaviors is through representative networks of interacting individuals, or nodes. Traditionally, researchers have focused on pairwise interactions, but many systems also include higher-order interactions between multiple nodes. What impact these higher-order mechanisms have on behaviors has been unclear. Thomas Robiglio from the Central European University in Vienna and colleagues have now addressed this issue by considering networks with higher-order interactions and evaluating the resulting behaviors in terms of statistical dependencies between the node values [1]. The researchers identified higher-order behavioral signatures that—unlike their pairwise counterparts—revealed the presence of higher-order mechanisms.
