Modeling Group Behavior
Our guest in this episode is Sebastien Motsch, an assistant professor at Arizona State University, working in the School of Mathematical and Statistical Science. He works on modeling self-organized biological systems to understand how complex patterns emerge.
Sebastien discussed two approaches to modeling the group behavior of animals, for instance, a flock of birds. He discussed how Boltzmann's questions and kinetic theory help them understand birds' interactions with respect to velocity changes. Sebastian also discussed the computational challenge of dealing with non-linear models.
Sebastien shared how the particular behavior of a group leads to a pattern. He also discussed how the flock determines the direction to go using the Vicsek model and Swarmalator. He shared how they collect data to study ants as well.
Sebastien talked about the dynamics of consensus, particularly explaining heterophilous. He shared some assumptions made during modeling and why they were necessary. He also discussed the emerging behaviors in slime molds.
Rounding up, he gave some advice for students who wish to get into the field.
Papers discussed
A new model for self-organized dynamics and its flocking behavior
Sebastien Motsch
Sebastien Motsch' research interests focus in the mathematical modeling of biological systems and especially those which exhibit self-organization such as bacterial colonies or flock of birds. His work aims at connecting two levels of description for these systems: a microscopic viewpoint (describing each individual) and a macroscopic description (using partial differential equations). One of the many questions addressed by biological systems is to understand how local interactions among individuals lead to the formation of large structures. The derivation and analysis of macroscopic models give new insights to understand these phenomenons. Motsch’ research can be divided in three themes: 1) derivation of macroscopic models from microscopic dynamics 2) numerical and analytically study of the macroscopic models derived 3) modeling of complex systems based on experimental data
