For this special mini-series covering the COVID19 pandemic, we will bring you into conversation with the scientists studying the bigger picture of this crisis, so you can learn their cutting-edge approaches and what sense they make of our evolving global situation.This week’s guest is Caroline Buckee, formerly an SFI Omidyar Fellow, one of MIT Tech Review’s 35 Innovators Under 35, and a CNN Top 10: Thinker — now Associate Director of the Center for Communicable Disease Dynamics at the Harvard School of Public Health. In this episode, we discuss the myriad challenges involved in monitoring and preventing the spread of epidemics like COVID-19, from the ethical concerns of high-resolution mobility data to an academic research ecosystem ill-equipped for rapid response, and the uneven distribution of international science funding.If you find the information in this program useful, please consider leaving a review at Apple Podcasts. Thank you for listening!Further Reading:Caroline’s Website at Harvard and Twitter Page.Find the papers we discuss in this episode at Caroline’s Google Scholar Page.Visit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

In several key respects, COVID-19 reveals how crucial timing is for human life. The lens of complex systems science helps us understand the central role of time in coordinating across scales, and how synchrony or misalignment leads to major consequences—whether it’s in how the metabolic differences between bats and humans can create an opportunity for interspecies epidemics, or in how the timing of society’s return to work could either help reboot or help destroy the world economy. Network research shows us early warning signs of an impending social crisis, the fossils of a vast collective computation as we struggle to adapt to periods of rapid change…and even the analogies we use to talk about these times bely a nested and embodied structure in how we encode the details of reality. These are complex times, indeed—and how civilization mutates to adapt to this pandemic will have everything to do with our ability to think and act at multiple timescales, simultaneously.In Transmission, SFI’s new essay series on COVID-19, our community of scientists shares a myriad of complex systems insights on this unprecedented situation. This special supplementary mini-series with SFI President David Krakauer finds the links between these articles—on everything from evolutionary theory to economics, epistemology to epidemiology—to trace the patterns of a deeper order that, until this year, was largely hidden in plain sight.You can support our research and communication efforts at santafe.edu/give.If you find the information in this program useful, please consider leaving a review at Apple Podcasts. Thank you for listening!Further Reading:005: Andrew Dobson on the Need for Disease Models which Capture Key Complexities of Transmission006: Miguel Fuentes on Using Social Media Data to Detect Signatures of Global Crises007 Danielle Allen, E. Glen Weyl, and Rajiv Sethi on How to Reduce COVID-19 Mortality While Easing Economic Decline008: Michael Hochberg on the Importance of Timing in Restrictive Confinement009: Melanie Mitchell on How the Analogies We Live by Shape our ThoughtsVisit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

The coronavirus pandemic is in one sense a kind of prism: it reveals the many interlocking systems that, until disrupted, formed the mostly invisible backdrop of modern life, challenging the economy and our models of the world at the same time that it threatens individual and social health. The virus acts on, and invites new understanding through, the complexity we only take for granted at our peril. In SFI’s new essay series on the crisis, Transmission, our international community of scientists explores a spectrum of perspectives on COVID-19 to share a myriad of complex systems insights on our unprecedented situation. In this special supplementary mini-series with SFI President David Krakauer, we discuss and find the links between these articles—on everything from evolutionary theory to economics, epistemology to epidemiology—to trace the patterns of a deeper order that, until this year, was largely hidden in plain sight.Read the Transmission series articles we discuss in this episode:000: David Krakauer on Citizen-Based Medicine001: David Kinney on Why Scientists Must Make Value Judgments in a Complex Crisis002: Luu Hoang Duc and Jürgen Jost on Making the Most of Bad Data003: John Harte on Reducing Conflicting Advice on Allowable Group Size004: Simon DeDeo on Thinking out of EquilibriumVisit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

“We should not have a strategy that involves killing a sizable percentage of the population. But, even if you were going to get over that ethical hurdle, [herd immunity for Covid-19] still isn't going to work.”- Sam ScarpinoFor this special mini-series covering the Covid-19 pandemic, we will bring you into conversation with the scientists studying the bigger picture of this crisis, so you can learn their cutting-edge approaches and what sense they make of our evolving global situation.This week we speak with Samuel V. Scarpino, who earned his PhD at UT Austin before becoming an Omidyar Fellow at The Santa Fe Institute, and now an Assistant Professor in the Network Science Institute at Northeastern University. In this episode, we glance off the surface of his extensive epidemiological research to discuss the complexity of interacting biological and behavioral contagions, analyzing Chinese mobility data to evaluate pandemic interventions, and the problem of unequal data collection due to socioeconomic inequality.Note that this episode was recorded on March 20th and we’d like to issue a blanket disclaimer that our understanding of the novel coronavirus pandemic evolves by the hour. We believe this information to be up to date at the time of publication but the findings discussed in this episode could soon be refined by more research.Sam’s Website & Twitter Page.Read the papers we discuss in this episode at Sam’s Google Scholar Page.Visit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

Chances are, if you are listening to this around the time it was released, you’re listening alone. Right now the human species is conducting one of the most sweeping synchronized experiments of all time: physical isolation, restricted travel, shuttered businesses, our social lives moved online. Many people wonder whether all of this is truly necessary to halt the spread of COVID-19—or do not understand what differences there are between closed borders and closed schools and businesses, how epidemiologists derive the interventions they advise, and why it matters that we all stay home right now.This week’s guest is Laurent Hébert-Dufresne, Assistant Professor of Computer Science at The University of Vermont’s Complex Systems Center, former SFI James S. McDonnell Foundation Postdoc and Research Fellow, and Editor of PLOS Complexity Channel. In this episode we discuss how network epidemiology studies contagions as they unfold across multiple scales, how co-infections (both biological and informational) change disease transmissibility, and how the best available research supports drastic containment measures.Note that this episode was recorded on March 17th and we’d like to issue a blanket disclaimer that our understanding of the novel coronavirus pandemic evolves by the hour. We believe this information to be up to date at the time of publication but the findings discussed in this episode could soon be refined by more research.Due to the pace at which the news is changing, we’ll ignore our normal schedule for the next few weeks and publish new episodes as quickly as we can.  Please take a moment to subscribe wherever you listen to podcasts, and feel free to suggest questions for upcoming guests on Twitter or in our Facebook group.Laurent’s Website & Twitter Page.Read the papers we discuss in this episode at Laurent’s Google Scholar Page.Visit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

Pandemics like the current novel coronavirus disease outbreak provide a powerful incentive to study the dynamics of complex adaptive systems. They also make it obvious, as new information streams in and our forecasts change in real-time, how hard emergent behaviors are to model and predict. For this special mini-series covering the COVID-19 crisis, we will bring you into conversation with scientists in the Santa Fe Institute’s global research network who study epidemics so you can learn their cutting-edge approaches and what sense they make of our evolving global situation.Due to the pace at which the news is changing, we’ll ignore our normal schedule for the next few weeks and get more, shorter conversations out more frequently.  Please take a moment to subscribe wherever you listen to podcasts, and feel free to suggest questions for upcoming guests on Twitter or in our Facebook group.This episode’s returning guest is SFI External Professor, Princeton epidemiologist Andy Dobson.  Among the questions we discuss:What are the benefits and limits of mathematical models in tracking contagious disease? How do epidemiologists make sense of the tradeoffs between a pathogen’s transmissibility and virulence with spatial and evolutionary models? When is it likely that herd immunity will and will not work as a reasonable response to COVID-19?  What happens if COVID-19 becomes an endemic seasonal infection? How are the dynamics of epidemiological and economic systems related, both at the level of disease transmission and for modeling recovery?You can support our research and communication efforts at santafe.edu/give.Visit our website for more information.Join our Facebook discussion group to meet like minds and talk about each episode.Andy’s WebsiteAndy’s Google Scholar PageAndy’s first appearance on Complexity Podcast Episode 16Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

One feature common to nonlinear phenomena is how they challenge intuitions. Maybe nowhere is this more apparent than in studying the evolutionary process, and organisms in which not just genes but learned behaviors reproduce themselves provide a fountain of reliable surprises. Teasing out the intricate dynamics of gene-culture co-evolution is no easy feat. The dance of language, tools, and rituals together with anatomy reveals a deeper hidden order in how information spreads, and offers clues to why some strategies for innovation repeat themselves across the tree of life.This week’s guest is Nicole Creanza, an Assistant Professor in the Biological Sciences department at Vanderbilt University whose research merges computational and theoretical approaches to the comparison of cultural and genetic evolution in both human languages and birdsong. In this episode, we discuss how geography, genetics, behavior, and technology collide in fascinating ways and how the study of gene-culture interactions might answer some of natural history’s greatest riddles.Nicole’s Website.Nicole’s Google Scholar Page.Nicole’s Santa Fe Institute Seminar: Cultural Evolution in Structured Populations.If you enjoy this podcast, please help us reach a wider audience by subscribing, leaving a review, and telling your friends about the show on social media. Thank you for listening!Visit our website for more information or to support our science and communication efforts.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

Since the term was coined in 1956, artificial intelligence has been a kind of mirror that tells us more about our theories of intelligence, and our hopes and fears about technology, than about whether we can make computers think. AI requires us to formulate and specify: what do we mean by computation and cognition, intelligence and thought? It is a topic rife with hype and strong opinions, driven more by funding and commercial goals than almost any other field of science...with the curious effect of making massive, world-changing technological advancements even as we lack a unifying theoretical framework to explain and guide the change. So-called machine intelligences are more and more a part of everyday human life, but we still don’t know if it is possible to make computers think, because we have no universal, satisfying definition of what thinking is. Meanwhile, we deploy technologies that we don’t fully understand to make decisions for us, sometimes with tragic consequences. To build machines with common sense, we have to answer fundamental questions such as, “How do humans learn?” “What is innate and what is taught?” “How much do sociality and evolution play a part in our intelligence, and are they necessary for AI?”This week’s guest is computer scientist Melanie Mitchell, Davis Professor of Complexity at SFI, Professor of Computer Science at Portland State University, founder of ComplexityExplorer.org, and author or editor of six books, including the acclaimed Complexity: A Guided Tour and her latest, Artificial Intelligence: A Guide for Thinking Humans. In this episode, we discuss how much left there is to learn about artificial intelligence, and how research in evolution, neuroscience, childhood development, and other disciplines might help shed light on what AI still lacks: the ability to truly think.Visit Melanie Mitchell’s Website for research papers and to buy her book, Artificial Intelligence: A Guide for Thinking Humans. Follow Melanie on Twitter.Watch Melanie's SFI Community Lecture on AI.Join our Facebook discussion group to meet like minds and talk about each episode.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedInMore discussions with Melanie:Lex FridmanEconTalkJim RuttWBUR On PointMelanie's AMA on The Next Web

Albert Kao, a Baird Scholar and researcher in animal sociality, discusses the fascinating dynamics of collective computation in nature. He delves into the concept of the 'wisdom of crowds,' unraveling the complexities that lead groups to make both wise and poor decisions. Kao emphasizes the evolutionary advantages of social behaviors, such as resource sharing and predator avoidance. He also highlights the unexpected value of disagreement in enhancing collective intelligence and the evolutionary drivers behind social group formations.

In this discussion, David B. Kinney, an Omidyar Postdoctoral Fellow at SFI and expert in the philosophy of science, sheds light on the intricate relationship between science and philosophy. He explores radical ideas like rational ignorance and the biases that influence scientific inquiry. The conversation delves into causation and probability, particularly in astrobiology, while highlighting the significance of cognitive biases shaped by evolutionary dynamics. Kinney advocates for collaboration between scientific and philosophical communities to tackle systemic inequality and the limitations of human understanding.