“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.

Kirell Benzi is a renowned data artist and lecturer with a PhD in Data Science. He shares his journey from computer enthusiast to a leader in data visualization, illustrating how emotions enhance the understanding of complex data. Benzi discusses the ethical dimensions of data use in music and art, including Generative Adversarial Networks. He emphasizes the role of engaging visuals in science communication and expresses how art can bridge the gap between science and society, making research accessible and impactful.

Why is the internal structure of Bacteria so different from the architecture of a nucleated cell? Why do some kinds of organisms stay small, whereas others grow to enormous size? What evolutionary challenges drove life’s major transitions into more and more complex varieties…and what does studying these areas reveal about the changing landscape of our global economy?New research into the science of scale — how physics operates on systems of different sizes — reveals universal speed limits imposed on biology by the energy required to make or repair component parts. It explains the varying evolutionary pressures on organisms to reallocate resources and change their body plans as they grow. It helps to resolve fierce old debates about just how much contingent history limits a creature’s future evolutionary options. And it illuminates how tradeoffs in resiliency and efficiency constrain the strategies of animals and human institutions alike, favoring self-reliance in some contexts and cooperation in others. Scale helps us prune the tree of possibilities and understand what are and are not likely futures for this planet.We have a lot to learn from germs and insects…Chris Kempes’ Website. 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

Physics usually gets the credit for grand unifying theories and the search for universal laws…but looking past the arbitrary boundaries between the sciences, it’s just as true that ecological research reveals deep patterns in the energy and information structures of our cosmos. There are profound analogies to draw from how evolving living systems organize themselves. And at the intersection of biology and physics, epidemiology and economics, new strategies for conservation and development emerge to guide us through the needle’s eye, away from global poverty and ecological catastrophe and toward a healthier and wealthier tomorrow…This week’s guest is SFI External Professor Andy Dobson of Princeton University, whose work focuses on food webs, parasites, and infectious diseases to help us understand and better manage the complexities of climate change and urban growth, human-wildlife interactions, and the spread of pathogens. In this episode we talk about how network structures can inhibit or accelerate disease transmission, the link between biodiversity and economic growth, and how complex systems thinking leads to better wildlife conservation.For transcripts, show notes, research links, and more, please visit complexity.simplecast.com.If you enjoy this podcast, please help us reach a wider audience by leaving a  review at Apple Podcasts, or by sharing 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.Andy’s WebsiteAndy’s Google Scholar PagePodcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn