Modelling group navigation: Transitive social structures improve navigational performance

Abstract

Collective navigation demands that group members reach consensus on which path to follow, a task that might become more challenging when the group's members have different social connections. Group decision-making mechanisms have been studied successfully in the past using individualbased modelling, although many of these studies have neglected the role of social connections between the group's interacting members. Nevertheless, empirical studies have demonstrated that individual recognition, previous shared experiences and inter-individual familiarity can influence the cohesion and the dynamics of the group as well as the relative spatial positions of specific individuals within it. Here, we use models of collective motion to study the impact of social relationships on group navigation by introducing social network structures into a model of collective motion. Our results show that groups consisting of equally informed individuals achieve the highest level of accuracy when they are hierarchically organized with the minimum number of preferred connections per individual.We also observe that the navigational accuracy of a group will depend strongly on detailed aspects of its social organization. More specifically, group navigation does not only depend on the underlying social relationships, but also on how much weight leading individuals put on following others. Also, we show that groups with certain social structures can compensate better for an increased level of navigational error. The results have broader implications for studies on collective navigation and motion because they showthat only by considering a group's social system can we fully elucidate the dynamics and advantages of joint movements.