7 December 2018
Nina Fefferman
Department of Ecology & Evolutionary Biology
University of Tennessee, Knoxville
Social organization in animal groups is an emergent property of self-organizing individual behaviors that can confer significant fitness benefits, but also incur significant costs. How can evolutionary forces shape the algorithms actors use to guide their own individual behaviors, even when the global outcome cannot be individually observed? We will discuss an abstracted hypothetical system that captures organizational efficiency for tasks like group-decision making and balances those against constraints from costs such as the risk from infectious disease spread in emergent social networks – this simplified model abstraction will allow us to discuss how these types of multiscale feedback controls can shape individual choices in ways that can provide successful group-level outcomes. The talk itself will focus primarily on computational simulation and the choices that went into forming the abstraction to provide broad insight into any multi-scale, multiobjective feedback control system on self-organizing networks (not just social behaviors), but we will also spend a few minutes discussing which types of questions about these systems are accessible by analytic solutions to approximations of the ongoing dynamics.