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

Since the first Industrial Revolution, most people have responded in one of two ways to the threat of technological unemployment: either a general blanket fear that the machines are coming for us all, or an equally uncritical dismissal of the issue. But history shows otherwise: the labor market changes over time in adaptation to the complex and nonlinear ways automation eats economies. Some jobs are easier to lose but teach skills that translate to other more secure jobs; other kinds of work elude mechanization but are comparably easier for humans, and thus don’t provide the kind of job security one might suppose. By analyzing labor networks — studying the landscapes of how skillsets intersect with labor markets and these systems mutate under pressure from a changing technological milieu — researchers can make deeper and more practical quantitative models for how our world will shift along with evolutions in robotics and AI. Dispelling Chicken Little fears and challenging the sanguine techno-optimists, these models start to tell a story of a future not unlike the past: one in which Big Changes will disrupt the world we know, arrive unevenly, reshape terrains of privilege and hardship, and reward those who can dedicate themselves to lifelong learning.This week’s guest is R. Maria del Rio-Chanona, a Mathematics PhD student supervised by SFI External Professor Doyne Farmer at the University of Oxford. Before starting her PhD, Maria did her BSc in Physics at Universidad Nacional Autónoma de México and was a research intern at the International Monetary Fund, where she studied global financial contagion in multilayer networks. We met at the 2019 New Complexity Economics Symposium to discuss the use of agent-based models in economics, how the labor market changes in response to technological disruption, and the future of work.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.Maria’s Website & Links to Papers.Maria’s Google Scholar Page.Andrew McAfee & Erik Brynjolfsson on Technological Unemployment.Carl Benedikt Frey & Michael A. Osborne on Technological Unemployment.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

If the economy is better understood as an evolving system, an out-of-equilibrium ecology composed of agents that adapt to one another’s strategies, how does this change the way we think about our future? By drawing new analogies between technology and life, and studying how tools evolve by building on and recombining what has come before, what does this tell us about economics as a sub-process of our self-organizing biosphere? Over the last forty years, previously siloed scientific disciplines have come together with new data-driven methods to trace the outlines of a unifying economic theory, and allow us to design new human systems that anticipate the planet-wide disruptions of our rapidly accelerating age. New stories need to be articulated, ones that start earlier than human history, and in which societies work better when engineered in service to the laws of physics and biology they ultimately follow…This week’s guest is W. Brian Arthur, External Professor at the Santa Fe Institute, Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford, and Visiting Researcher at Xerox PARC.  In this second part of our two-episode conversation, we discuss technology as seen through the lens of evolutionary biology, and how he foresees the future of the economy as our labor market and financial systems are increasingly devoured by artificial intelligence.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.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedInBrian’s Website.Brian’s Google Scholar page.“Where is technology taking the economy?” in McKinsey, 2017.The Nature of Technology: What It Is and How It Evolves.“Punctuated equilibria: the tempo and mode of evolution reconsidered” by Gould & Eldredge."A natural bias for simplicity" by Mark Buchanan in Nature Physics."Economic Possibilities for our Grandchildren" by John Maynard Keynes.

W. Brian Arthur, an economics pioneer and complexity theory expert, discusses the shift from traditional economics to complexity economics, where markets are seen as systems out of balance. They talk about how complex systems evolve, the impact of technology on the economy, and the need to understand non-equilibrium conditions for dynamic strategies and innovations.

It may be a cliché, but it’s a timeless truth regardless: who you know matters. The connectedness of actors in a network tells us not just who wields the power in societies and markets, but also how new information spreads through a community and how resilient economic systems are to major shocks. One of the pillars of a complex systems understanding is the network science that reveals how structural differences lead to (or help counter) inequality and why a good idea alone can’t change the world. As human beings, who we are is shaped by those around us — not just our relationships to them but their relationships to one another. And the topology of human networks governs everything from the diffusion of fake news to cascading bank failures to the popularity of social influencers and their habits to the potency of economic interventions. To learn about your place amidst the networks of your life is to awaken to the hidden seams of human culture and the flows of energy that organize our world.This week’s guest is SFI External Professor Matthew O. Jackson, William D. Eberle Professor of Economics at Stanford University and senior fellow of CIFAR, also a Member of the National Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences. In this episode, we discuss key insights from his book, The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors.For transcripts, show notes, research links, and more, please visit complexity.simplecast.com.And note that we’re taking a short break over the winter holiday. COMPLEXITY will be back with new episodes in January 2020.If you enjoy this show, please help us reach a wider audience by leaving a review at Apple Podcasts, or by telling your friends on social media…after this episode’s discussion, we know you’ll understand how crucial this can be. 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.Matthew Jackson’s Stanford Homepage.WSJ reviews The Human Network.Jackson’s Coursera MOOCs on Game Theory I, Game Theory II, and Social & Economic Networks.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

In this show’s first episode, David Krakauer explained how art and science live along an axis of explanatory depth: science strives to find the simplest adequate abstractions to explain the world we observe, where art’s devotion is to the incompressible — the one-offs that resist abstraction and attempts to write a unifying framework. Between the random and the regular, amidst the ligaments that bind our scientific and artistic inquiries, we find a huge swath of the world that we struggle to articulate in formal quantitative terms, but that rewards our curiosity and offers us profound insights regardless. Here lives the open question of what we can learn from history — specifically, the histories of other people’s lives.  Why do we love biographies?  What can the stories of the lives of others teach us about both situational and common truths of being?  This is a different kind of episode and conversation, one living at the intersection of philosophy and history and science…This week’s episode features guest interviewer, SFI President David Krakauer, in conversation with philosopher and biographer Ray Monk.  Monk teaches at the University of Southhampton and was SFI’s 2017 Miller Scholar, a position that he earned for his biographies of Ludwig Wittgenstein, Bertrand Russell, and J. Robert Oppenheimer — three mavericks whose legacies are lessons for contemporary leaders.If you enjoy this podcast, please help us reach a wider audience by leaving a five-star 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.Ray Monk on Twitter.Ray Monk’s SFI Miller Scholar Profile Page.Ray Monk on Hidden Forces Podcast.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

What is the difference between 100 kilograms of human being and 100 kilograms of algae? One answer to this question is the veins and arteries that carry nutrients throughout the human body, allowing for the intricate coordination needed in a complex organism. Energy requirements determine how the evolutionary process settles on the body plans appropriate to an environment — one way to tell the story of life’s major innovations is in terms of how a living system solves the problems of increasing body size with internal transport networks and more extensive regulation. And the same is true in our invented information systems, every bit as subject to the laws of physics as we are. Computers, just like living tissue, seek effective tradeoffs between their scale and energy efficiency. A physics of metabolic scaling — one that finds deep commonalities and crucial differences between ant hives and robot swarms, between the physiology of elephants and server farms — can help explain some of the biggest puzzles of the fossil record and sketch out the likely future evolution of technology. It is already revolutionizing how we understand search algorithms and the genius of eusocial organisms. And just maybe, it can also help us solve the challenge of sustainability for planetary culture.This week’s guest is Melanie Moses, External Professor at the Santa Fe Institute, Professor of Computer Science and Biology at the University of New Mexico, and Principal Investigator for the NASA Swarmathon. In this episode, we talk about her highly interdisciplinary work on metabolic scaling in biology and computer information-processing, and how complex systems made and born alike have found ingenious ways to balance the demands of growth and maintenance — with implications for space exploration, economics, computer chip design, and more.If you enjoy this podcast, please help us reach a wider audience by leaving a five-star 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.Melanie’s UNM Webpage & full list of publications.“Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms” by Joshua Hecker & Melanie Moses.“Energy and time determine scaling in biological and computer designs” by Moses, et al.“Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life” by DeLong, Moses, et al.“Distributed adaptive search in T cells: lessons from ants” by Melanie Moses, et al.“Curvature in metabolic scaling” by Kolokotrones, et al.The NASA Swarmathon.Podcast Theme Music by Mitch Mignano.Follow us on social media:Twitter • YouTube • Facebook • Instagram • LinkedIn

The podcast explores social learning and decision-making, including the influence of social networks and political ideologies on our understanding of climate change. It delves into biases in social judgments and the impact of biased social networks on accurate estimates of the general population. The trade-off between computational cost and efficiency in understanding social networks is discussed, as well as contradictory findings in problem-solving networks. The podcast also explores the influence of social and semantic networks on scientific beliefs, and techniques to respond to hate speech online.