Currents 015: Jessica Flack & Melanie Mitchell on Complexity



In this Currents episode, Jim talks to Melanie Mitchell & Jessica Flack about the complexity of COVID, randomness, robustness, collective intelligence, misinfo, and much more…

In this Currents episode, Jim talks to Melanie Mitchell & Jessica Flack about their recent Aeon article, Uncertain times. Why R(0) is not a good measure for COVID contagion, network contagion & super spreaders, global non-linear causes & effects, feedback dynamics in complex systems, some hopeful views on COVID-19 impact, the importance of noise & randomness in complex systems, understanding & planning for fat-tailed distributions, designing for robustness, emergent engineering, funding robustness, collective intelligence, science distrust, misinformation, humility, authority, trust, and more.

Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Co-Chair of the Science Board at the Santa Fe Institute. Mitchell has also held faculty or professional positions at the University of Michigan, Los Alamos National Laboratory, and the OGI School of Science and Engineering. She is the author or editor of seven books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems, including her latest, Artificial Intelligence: A Guide for Thinking Humans.

Jessica Flack is a professor at the Santa Fe Institute. Flack directs SFI’s Collective Computation Group (C4). Flack was formerly founding director of the Center for Complexity and Collective Computation in the Wisconsin Institute for Discovery at the University of Wisconsin, Madison. Flack received her Ph.D. from Emory in 2003, studying cognitive science, animal behavior and evolutionary theory, and B.A. with honors from Cornell in 1996. Flack’s work has been covered by scientists and science journalists in many publications and media outlets, including Quanta Magazine, the BBC, NPR, NatureScienceThe EconomistNew Scientist, and Current Biology.


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