Childhood in social learning models with changing environments: Implications for human evolution.

Childhood is a time of learning, both individually and through social interactions. But the vulnerability inherent in childhood represents a fitness cost: individuals do not have progeny during childhood, and childhood is a net drain of familial or group resources. In this study we model childhood in resource acquisition scenarios where social learning through observation competes with learning through innovation across multiple generations in a variety of environments. In general, observing others allows useful knowledge to be gained more efficiently than self-exploration and may result in significantly greater resource acquisition. However, social learning needing a lengthy childhood to develop advanced cognitive ability may offset the net fitness advantage that might otherwise be gained. Through simulations we demonstrate that individuals with a substantial childhood burden acquire more lifetime resources by observing others than do individuals with negligible childhood costs using self-exploration, as long as the environment is fairly stable. This advantage decreases as the environment becomes less predictable, and reverses in rapidly changing environments where knowledge is unreliable. These results suggest that hominid evolution, with vastly growing cognitive abilities and a longer, more vulnerable childhood, may have been facilitated in similarly stable environments. On the other hand, hominid populations may have been particularly vulnerable to environmental instability. We apply this insight to the Out of Africa Homo sapiens migration roughly 50,000 years ago and show consistency with the serial founder model that best fits current archeological and genetic evidence. Our findings are important for models of social learning, especially those that describe the emergence and spread of Homo sapiens.

Effects of random motion in traveling and grazing herds.

We examine the role that randomness or noise in individual motion may play in forming effective grazing strategies for herd members as they collectively move toward a destination. Through a model where animals are attracted to Voronoi neighbors as well as a destination endpoint, we show that including a significant random motion component can speed up the movement of a herd toward this destination, increase the efficiency that food is acquired during the travel, and facilitate a natural herd shape that mitigates predation risk. Specifically, if the influence of the Voronoi neighbors on individual motion is equal to the pull toward the destination, we find that optimal travel time and food consumption efficiency occurs for noise approximately twice as strong as the influence of herd members to each other, in a range of herd sizes from 10 to 100. We find that reducing the destination influence lowers this optimal noise only slightly, with random motion still exceeding the influence of neighbors. For a destination influence exceeding that of the Voronoi neighbors, an additional travel mode appears with minimal noise and aligned velocities in which the herd marches directly toward the endpoint. Our results are consistent with observational evidence of random motion in several animal groups, and motivate its generalization to traveling and grazing herds.

The selfish herd: Noise effects in Local Crowded Horizon and Voronoi models.

Randomness or noise in the motion of herding animals is more important than commonly assumed. We show through simulations that noise promotes the compactness of a herd by facilitating the breakup of small clusters in favor of a more compact whole, reducing the mean median distances to center (MMDC) for the herd. Adding noise to movement models can also decrease risk from predators emerging locally by reducing Domains of Danger (DODs); we specifically show that adding noise to the Voronoi (V) movement model gives favorable DOD results comparable to that of the Local Crowded Horizon (LCH) model. In addition, we show that adding noise to these models can significantly reduce risk from predators emerging outside the herding area through head-to-head competition in mixed herds, with effects that can be larger than the choice of model itself. Our results are consistent with recent observational studies in several different animal populations that suggest random motion plays a significant role in the movement of individuals within these groups. Because of its significance, noise must be considered whenever model effectiveness is discussed or comparisons between movement models are made.

Density-independent model of self-propelled particles.

We examine a density-independent modification of the Vicsek model in which a particle interacts with neighbors defined by Delaunay triangulation. To feasibly simulate the model, an algorithm for repairing the triangulation over time was developed. This algorithm may also be applied to any time varying two-dimensional Delaunay triangulation. This model exhibits a continuous phase transition with noise, and a distinct set of critical exponents were measured which satisfy a hyperscaling relationship. The critical exponents are found to vary between a low and high velocity regime, but they are robust under the inclusion of a repulsive interaction. We present evidence that the correlation length approximately scales with the size of the system even in the ordered phase.