Algorithmic Foundations of Emergent Behavior in Analog Collectives


How can an observed or desired collective behavior be reverse-engineered into local rules that individuals can embody? In this talk, I present an algorithmic framework that harnesses statistical physics to obtain stochastic distributed algorithms whose long-run collective behaviors can be formally characterized by phase transitions. To bridge this algorithmic theory to the mechanics of physical systems, I demonstrate how the nonequilibrium dynamics of active granular matter can be quantitatively predicted by a stochastic distributed algorithm at equilibrium. I conclude by highlighting how these connections between theory and physical systems suggest new approaches to characterizing emergent phenomena in complex biological and social systems, as in my ongoing work on the dynamics of political polarization.

July 14, 2021 12:15 PM
Santa Fe Institute
Santa Fe, NM, USA
Joshua J. Daymude
Joshua J. Daymude
Assistant Professor, Computer Science

I am a Christian and assistant professor in computer science studying collective emergent behavior and programmable matter through the lens of distributed computing, stochastic processes, and bio-inspired algorithms. I also love gaming and playing music.