Abstract. A critical aspect of human and animal interactions is modularity. Individuals that surround each other tend to be more densely
interconnected and more similar than would be expected from pure chance. This effect takes multiple forms: one can think of shared
ideologies and tastes in the subgroups of a human population, or more generally, one can think of behaviors, strategies and environments.
We will tackle two problems: (i) how knowledge of modularity affects behavior, and (ii) how other structural features might bias our ability to identify modularity in the first place.
Our results will highlight how modularity affects optimal strategies, for instance for survival given heterogeneous environmental conditions or for spread of ideas given our divided political climate. We will also quantify how heterogeneous social structure, such as one with a well-defined hierarchy, affects both our tools to identify modularity as well as the consequences of modularity.
Our working group is composed of researchers with expertise's in nonlinear dynamics, random matrix theory as well as SFI's local expertise in complex network (Laurent Hébert-Dufresne) and evolutionary dynamics (Eric Libby).