When the selfish herd is too crowded to enter

The selfish herd hypothesis highlights the importance of individual short-term fitness to the collective behavior. Previous agent-based models have demonstrated how selfish prey agents evolve into cohesive groups where individuals attempt to enter the central positions. However, these simulations either treated an agent as a point or allowed overlaps between agent bodies. Hence, the condition when a herd is too crowded to enter has long been neglected. In this paper, an agent-based model is built to simulate the behavioral evolution of a prey population in two-dimensional open space. These prey agents are specifically assigned rigid bodies so that overlapping is forbidden in the model. By introducing a genetic algorithm that evolves neural networks with incremental complexity, adaptive strategies can be developed automatically in evolution. The simulation output stresses the significant impact of the overlap-free condition on the behavioral evolution of gregarious prey. It is shown that given agents able to squeeze into a group by pushing away others, evolution will drive selfish prey agents to leave smaller heaps and assemble larger ones. In contrast, given that agents cannot squeeze into the crowd, selfish prey agents will evolve to exhibit various appearances of coordinated movement. This collective motion is due to malignant competition, which decreases the group benefit compared with the transitional states. These findings reveal a novel perspective on the collective behavior of group-living animals in nature.