Managed moves: schools collaborating for collective gain

Collective translational and rotational Monte Carlo moves for attractive particles.

Virtual move Monte Carlo is a Monte Carlo (MC) cluster algorithm forming clusters via local energy gradients and approximating the collective kinetic or dynamic motion of attractive colloidal particles. We carefully describe, analyze, and test the algorithm. To formally validate the algorithm through highlighting its symmetries, we present alternative and compact ways of selecting and accepting...

Incorporating friction and collective shear moves into a lattice gas.

Here we examine a lattice-gas model that has been extended to include collective shear moves and friction interactions between particles. A shearlike field is applied by periodically translating entire subsections of the lattice with respect to one another, as opposed to biasing the individual movements of particles. Friction is introduced by forming a network of temporary bonds between particl...

The herd moves?: emergence and self-organization in collective actors

Fishing out collective memory of migratory schools.

Animals form groups for many reasons, but there are costs and benefits associated with group formation. One of the benefits is collective memory. In groups on the move, social interactions play a crucial role in the cohesion and the ability to make consensus decisions. When migrating from spawning to feeding areas, fish schools need to retain a collective memory of the destination site over tho...

Collective gradient sensing in fish schools

Throughout the animal kingdom, animals frequently benefit from living in groups. Models of collective behaviour show that simple local interactions are sufficient to generate group morphologies found in nature (swarms, flocks and mills). However, individuals also interact with the complex noisy environment in which they live. In this work, we experimentally investigate the group performance in ...