Large scale coordination without dominant, consistent leadership is frequent in nature. How individuals emerge from within the group as leaders, however transitory this position may be, has become an increasingly common question asked. This question is further complicated by the fact that in many of these aggregations, differences between individuals are minor and the group is largely considered to be homogeneous. In the simulations presented here, we investigate the emergence of leadership in the extreme situation in which all individuals are initially identical. Using a mathematical model developed using observations of natural systems, we show that the addition of a simple concept of leadership tendencies which is inspired by observations of natural systems and is affected by experience can produce distinct leaders and followers using a nonlinear feedback loop. Most importantly, our results show that small differences in experience can promote the rapid emergence of stable roles for leaders and followers. Our findings have implications for our understanding of adaptive behaviors in initially homogeneous groups, the role experience can play in shaping leadership tendencies, and the use of self-assessment in adapting behavior and, ultimately, self-role-assignment.

Optimizing group success is challenging for multi-robot systems, especially for large systems such as robot swarms where even simple individual interaction rules can lead to complex group behavior. Studies of natural systems have shown that heterogeneous groups can outperform homogeneous groups, especially when individual differences lead to role or niche specialization. This happens even when the individuals are seemingly identical. Although individuals within a group may appear physically identical, they can vary widely in their personality, which significantly affects their behavior. However, determining the most effective composition of personalities in a group is particularly difficult in natural systems given the ambiguities of animal personalities and the physical challenges of repeated evaluations. Using a biologicallybased collective movement model, we evaluate different personality distributions to determine their effect on the overall success of the group. Results show that although there are distributions that are clearly more effective than others, in many cases, there is a broad range of distributions that results in high group success. Furthermore, experiments using variable, or adaptive, personalities demonstrate that successful distributions are stable equilibriums as initially extreme distributions converge to these successful distributions as personalities change.

Collective movement in autonomous systems, such as a team of robots, are frequently implemented using complex interaction rules and have significant communication requirements. These restrictions frequently relegate such systems to static, simplified environments. In contrast, collective movements in natural systems consistently occur in dynamic, complex environments in which significant communication is either impractical or impossible, and have been successfully modeled using simple, local interaction rules. In the work presented here, one such model is extended to include local communication and the spatial distribution of the group so that it can eventually be used as a guide for developing artificial systems capable of cohesive, collective movements. The extended model predicts that a reliance on local communication does not necessarily mean there will be a significant loss in the expected success of collective movement attempts if appropriate interaction rules are chosen. Furthermore, the model predicts that the addition of local communication, in conjunction with the topology of the group, results in higher expected success in attempting collective movements for individuals with central locations in the group as compared to individuals occupying edge locations.

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