Abstract
I will discuss empirical and computational approaches my collaborators and I have been developing to build adaptive causal circuits that connect individual decision-making rules to collective patterns. This approach requires techniques that permit extraction of decision-making rules from time-series data. A goal of the research I will be discussing is to give an empirically grounded computational account of the emergence of robust aggregate features and hierarchical organization in social evolution.
Biography
Jessica Flack is Professor at the Santa Fe Institute and Co-Director of the Collective Social Computation Group. Her research program combines dynamical systems and computational perspectives in order to build a theory of how aggregate structure and hierarchy arise in social evolution. Primary goals are to understand the conditions and mechanisms supporting the emergence of slowly changing collective features that feed-down to influence component behavior, the role that conflict plays in this process, and the implications of multiple timescales and overlapping networks for robustness and adaptability in social evolution. Research foci include design principles for robust systems, conflict dynamics and control, the role of uncertainty reduction in the evolution of signaling systems, the implications of higher-order structures for social complexity and innovation, behavioral grammars and adaptive circuit construction. Flack approaches these issues using data on social process collected from animal society model systems, and through comparison of social dynamics with neural, immune, and developmental dynamics. Flack received her PhD in 2004 from Emory University in evolution, cognition and animal behavior. Flack was a Postdoctoral Fellow at SFI before joining the SFI Faculty in 2007.