Physics Study Shows Sheep Flocks Alternate Their Leader and Achieve Collective Intelligence

The collective movement of animals in a group is a fascinating research topic for many scientists. Understanding these collective behaviors can sometimes inspire the development of strategies to promote positive social change, as well as technologies that mimic nature.

Many studies describe flocking behavior as a self-organizing process, with individuals in a group continually adapting their direction and speed to ultimately achieve “collective” movement. This perspective, however, fails to take into account the hierarchical structure exhibited by many groups of animals and the possible benefits of having a “leader” leading the way.

Luis Gómez-Nava, Richard Bon and Fernando Peruani, three researchers at Université Côte d’Azur, Université de Toulouse and CY Cergy Paris Université have recently used physical theory to examine the collective behavior of small flocks of sheep. Their findings, published in Natural Physicsshow that by alternating the role of leader and follower, the herd finally achieves a certain form of “collective intelligence”.

“In most gregarious animal systems, collective movement is not a continuous process, but occurs in episodes: phases of collective movement are interrupted, for example, to rest or feed,” Peruani told Phys .org. “Nevertheless, most studies of collective movement, including experimental and theoretical, consider groups that remain, from beginning to end, in motion. “

The key goal of recent work by Peruani and colleagues was to study the collective motion of an animal system in a way that explicitly considers the temporal aspect of the observed self-organized process, in particular that the phases of motion collective have a beginning and an end. . Furthermore, the team wished to adopt an alternative and holistic perspective, which considers the movement of the animal group as a set of “collective phases”.

“From this perspective, questions about mechanisms for information sharing and consensus decision-making take on a new dimension,” Peruani explained.

In their experiment, Peruani and his colleagues closely studied the spontaneous behavior of small groups of sheep over varying time intervals. They analyzed the trajectories of individual herd members and calculated the animals’ overall spatial order and orientation, while assessing correlations between the speed at which individual animals moved.

“We first showed that none of the existing flocking models, or their extensions, are consistent with our observations,” Peruani said. “Next, we analyzed how the information travels through the group, identifying a consistent network of interaction with the data, and studying what information is transmitted through this network.”

Interestingly, Peruani and his colleagues found that the interaction network representing herd behavior that they observed was highly hierarchical. Moreover, they showed that the only information propagated through this network is that related to the position of the sheep within the group.

Using their findings, the researchers built a model of collective animal movement that focuses on two key cognitive processes. These processes are the selection of a leader who will lead the herd for a specific duration and the mechanism behind herd navigation.

“It is important to note that each phase of collective movement has a temporal leader,” explained Peruani, “We studied the mathematical properties of the resulting model to identify the benefits of the collective strategy unveiled. I believe that the main contribution is the following: animals, through the use of a hierarchical interaction network to move together for a period of time gives full control of the group to the temporal leader, but there is also a rapid turnover of temporal leaders.

Essentially, the researchers’ findings suggest that when moving in herds, sheep alternate between the role of leader and follower. Leaders therefore only lead the group for a certain amount of time, before control of the group is transferred to another sheep.

“If a temporal leader has knowledge relevant to the group (e.g., the exit from a maze or the location of a food source), then the temporal leader will be able to effectively guide the group,” said said Peruani. “In this way, all members of the group benefit from this knowledge. It should be noted that this only works if all individuals follow the temporal leader without question.

The findings collected by Peruani and his colleagues shed new light on the dynamics underlying the collective movement of small flocks of sheep. However, to investigate to what extent these results can be generalized, further experiments with larger herds and different animals will need to be conducted.

“We asked ourselves: if there is a temporal leader at every moment, how does the group share and process the information that each individual member of the group may have? pooling information to improve its ability to navigate with precision to a distant location? In short, does the group demonstrate collective intelligence? said Peruani. “We have proven that by regularly changing time leaders, the group is able to demonstrate the pooling of information and collective intelligence.”

Overall, the recent work of this team of researchers highlights the possibility that certain natural collective animal strategies take advantage of both hierarchical and democratic organizational patterns. In the future, their observations could inspire new studies of the physics and biology that underlie these intriguing collective animal behaviors.

“We are now studying collective movement using groups of different agents,” Peruani added. “Specifically, we are comparing the spontaneous behavior of groups of lambs, young sheep, and adult sheep, to determine whether sheep learn to follow temporal leaders and act as one over time. We are also investigating how groups behave in complex environments such as mazes or arenas with different food patches that can trigger a conflict of interest within group members.And more generally, we study how collectives distribute and process information, to using several tools of statistical mechanics.

© 2022 Science X Network