COMPLEXITY

Over one hundred years ago, Sir Francis Galton asked 787 villagers to guess an ox’s weight. None of them got it right, but averaging the answers led to a near-perfect estimate. This is a textbook case of the so-called “wisdom of crowds,” in which we’re smarter as collectives than we are as individuals. But the story of why evolution sometimes favors sociality is not so simple — everyone can call up cases in which larger groups make worse decisions. More nuanced scientific research is required for a deeper understanding of the origins and fitness benefits of collective computation — how the complexity of an environment or problem, or the structure of a group, provides the evolutionary pressures that have shaped the landscape of wild and civilized societies alike. Not every group deploys the same rules for decision-making; some decide by a majority, some by consensus. Some groups break up into smaller sub-groups and evaluate things in a hierarchy of modular decisions. Some crowds are wise and some are dumber than their parts, and understanding how and when and why the living world adopts a vast diversity of different strategies for sociality yields potent insights into how to tackle the most wicked problems of our time.

This week’s guest is Albert Kao, a Baird Scholar and Omidyar Fellow here at SFI. Kao came to Santa Fe after receiving his PhD in Ecology and Evolutionary Biology at Princeton and spending three years as a James S. McDonnell fellow at Harvard. In this episode, we talk about his research into social animals and collective decision-making, just one of several reasons why a species might evolve to live in groups. What do the features of these groups, or the environments they live in, have to do with how they process information and act in the world?

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Albert’s Website

Albert’s Google Scholar Page

Discussed:
Quanta Magazine’s “Smarter Parts Make Collective Systems Too Stubborn”

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Science is often seen as a pure, objective discipline — as if it all rests neatly on cause and effect. As if the universe acknowledges a difference between ideal categories like “biology” and “physics.” But lately, the authority of science has had to reckon with critiques that it is practiced by flawed human actors inside social institutions. How much can its methods really disclose? Somewhere between the two extremes of scientism and the assertion that all knowledge is a social construct, real scientists continue to explore the world under conditions of uncertainty, ready to revise it all with deeper rigor.

For this great project to continue in spite of our known biases, it’s helpful to step back and ask some crucial questions about the nature, limits, and reliability of science. To answer the most fundamental questions of our cosmos, it is time to bring back the philosophers to articulate a better understanding of how it is that we know what we know in the first place. Some questions — like the nature of causation, where we should look for aliens, and why we might rationally choose not to know important information — might not be answerable without bringing science and philosophy back into conversation with each other.

This week’s guest is David Kinney, an Omidyar Postdoctoral Fellow here at SFI whose research focuses on the philosophy of science and formal epistemology. We talk about his work on rational ignorance, explanatory depth, causation, and more on a tour of a philosophy unlike what most of us may be familiar with from school — one thriving in collaboration with the sciences.

DavidBKinney.com

On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations.Philosophy of Science. 86(1): 145-167.

Is Causation Scientific?

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Science has always been about improving human understanding of our universe…but scientists have not always prioritized accessibility of their hard-won results. The deeper research digs into specialized sub-fields and daunting data sets, the greater the divide a team must cross to help communicate their findings not just to the public, but to other scientists.

It is cliché: “A picture’s worth a thousand words.” But it’s the truth: strong visual communication helps readers make the choice to dig into dense manuscripts, and helps journal editors decide whose work gets published in the first place. Good dataviz can get complexity across in less time and with less effort, help public audiences grasp science better and appreciate the beauty that inspired the research to start with.

Deciding how to represent research in graphic form is both a little science and a little art: it takes developing an understanding of what information matters and what doesn’t, and how other people will absorb it. Thus it should come as no surprise that in our noisy era, the data artist rises as a hero of both fields: empowered by technology to bridge dissociated disciplines and help us all learn more and better.

This week’s episode is with Kirell Benzi, a data artist and data visualization lecturer who holds a PhD in Data Science from EPFL (Ecole Polytechnique Fédérale de Lausanne). Kirell’s work has been shown in outlets as diverse as the Swiss National Museum, Gizmodo, VICE, and Phys.org. In this recording, we discuss his projects mapping the Montreaux Jazz Festival and the Star Wars Extended Universe, the future of neural-network assisted data visualization, and how data art helps with the technical and ethical challenges facing science communication in the 21st Century.

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Kirell Benzi’s Website

Kirell’s Instagram

Kirell’s SFI seminar on Data Art (video)

“Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts” by Scott Bateman, Regan L. Mandryk, Carl Gutwin, Aaron Genest, & David McDine, University of Saskatchewan

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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.

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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!

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Andy’s Website

Andy’s Google Scholar Page

Podcast Theme Music by Mitch Mignano.

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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.

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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.

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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!

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Brian’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.

From its beginnings as a discipline nearly 150 years ago, economics rested on assumptions that don’t hold up when studied in the present day. The notion that our economic systems are in equilibrium, that they’re made of actors making simple rational and self-interested decisions with perfect knowledge of society— these ideas prove about as useful in the Information Age as Newton’s laws of motion are to quantum physicists. A novel paradigm for economics, borrowing insights from ecology and evolutionary biology, started to emerge at SFI in the late 1980s — one that treats our markets and technologies as systems out of balance, serving metabolic forces, made of agents with imperfect information and acting on fundamental uncertainty. This new complexity economics uses new tools and data sets to shed light on puzzles standard economics couldn’t answer — like why the economy grows, how sudden and cascading crashes happen, why some companies and cities lock in permanent competitive advantages, and how technology evolves. And complexity economics offers insights back to biology, providing a new lens through which to understand the vastly intricate exchanges on which human life depends.

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 first part of a two-episode conversation, we discuss the heady early days when complex systems science took on economics, and how biology provided a new paradigm for understanding our financial and technological systems.  Tune in next week for part two...

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!

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Podcast Theme Music by Mitch Mignano.

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For more information:

Brian’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.

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!

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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.

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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.

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Ray Monk on Twitter.

Ray Monk’s SFI Miller Scholar Profile Page.

Ray Monk on Hidden Forces Podcast.

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