Simplifying Complexity

Today we're joined by Luis Bettencourt, Professor at the University of Chicago, and External Faculty at the Santa Fe Institute. Luis is going to pull apart how cities work, why they work the way they do, what's good about them, and what's bad about them. He's also going to talk specifically about slums, and the challenges that exist in raising people out of poverty. Resources and links: Simplifying Complexity - Scaling 1: Why do we live longer than mice? Simplifying Complexity - Scaling 2: You and I are fractals Simplifying Complexity - Scaling 3: Why companies die, but cities don't Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Today, we're going to return to the idea of taking concepts from complexity science and applying them to situations in the real world. In this episode, we're joined again by Melanie Moses, Professor of Computer Science at the University of New Mexico, and External Faculty at the Santa Fe Institute. She's going to share with us about her recent trip to Iceland to study active volcanoes. More specifically, Melanie is going to explain how you can program a swarm of drones to fly in formation and map the CO2 plume of a volcano. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Orit Peleg is an Associate Professor at the University of Colorado, Boulder, and External Faculty at the Santa Fe Institute. Orit has been on the show before, to discuss how bees work as a complex system. In this episode, we're staying within the animal kingdom, as Orit talks to us about fireflies. In this episode, Orit is going to explain how thousands of fireflies over very significant areas can synchronise their flashing in the night sky. She'll break down the work she has been doing to study this complex system of individual agents and share the lessons we can learn from these fireflies and use them in other applications. For example, what can we learn from these synchronised fireflies that could help us to program a swarm of small robots to work together to lift something? Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode, you heard all about economic mobility. In this episode (which is part 2 of our conversation), you're going to hear again from Matthew Jackson, William D. Eberle Professor of Economics at Stanford University, and External Faculty at the Santa Fe Institute. We finished the last episode by saying that if you want to increase a child's economic mobility, the factor that has the greatest impact is economic connectedness. In this episode, Matthew is going to talk about economic connectedness in our workplaces, our religious gatherings, and our schools. Resources and links: Social capital I: measurement and associations with economic mobility | Nature Social capital II: determinants of economic connectedness | Nature Vast New Study Shows a Key to Reducing Poverty: More Friendships Between Rich and Poor - The New York Times (nytimes.com) Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

If you're a child born into a poor family in the United States, what are the most important factors in your life that will influence whether or not you're able to rise out of poverty? To answer that question, we're joined again by Matthew Jackson, William D. Eberle Professor of Economics at Stanford University, and External Faculty at the Santa Fe Institute. This is part one of a two-part series, and in this episode, Matthew is going to introduce us to a study he was involved in that looked at the data of 21 billion friendships in the US that asked the question: what is it really that allows a child to get ahead? Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

When a system fails, how do you think about cause and effect? One way to consider this in complex systems is to imagine a pile of sand, and dropping one grain of sand at a time in random positions onto the pile. As time passes, you'll start to form little hills. Eventually, a grain of sand will hit one of these hills, and you get an avalanche. Do you believe that the avalanche was caused by the last grain of sand falling onto it, or do you believe that the avalanche happened due to the shape of the hill itself? To explore this sand pile model, we are joined today by Neil Johnson, Professor of Physics and Head of the Dynamic Online Networks Lab at George Washington University. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

When most of us think about information, we think of it as something we can possess or ‘know’. But what if it’s so much more than that? In this episode, we’re joined by Sara Walker, Deputy Director of the Beyond Center for Fundamental Concepts in Science, Associate Professor in Earth and Space Exploration and Complex Adaptive Systems at Arizona State University, and External Faculty at the Santa Fe Institute. Sara is going to examine information and the critical role it plays in complex systems. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Imagine you have a bar that comfortably seats 60 people, but every week, 100 people have to decide whether or not they're going to go to the bar on any given night. If too many people go, then the bar is too crowded, and everyone has a miserable night. But if not enough people go, then that's a missed opportunity to go out. This is the basis of the El Farol problem, which asks us to consider how people make this decision. It's a beautifully simple problem that not only makes you think but also has profound implications. To help us through this problem, we're joined again by its inventor, W. Brian Arthur, External Professor at the Santa Fe Institute and Researcher at Palo Alto Research Center. Brian's going to help us understand how this problem is more than just the story of a bar, but a problem that gives us an incredible insight into how the economy works. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode, we talked about the four conditions of complex systems: numerosity, disorder and diversity, feedback, and non-equilibrium — and we also talked about the concept of emergence. In this episode, which is part two of our two-part series on the features of complex systems, we're joined again by Karoline Wiesner, Professor of Complexity Science in the Department of Physics and Astronomy at the University of Potsdam in Germany. In this episode, Karoline explains the six emergent features of complex systems: Spontaneous order and self-organisation Non-linearity Robustness Nested structure and modularity History and memory Adaptive behaviour By the time you've finished this episode, you'll understand the underlying principles of complex systems that hold together the wide variety of topics we talk about in this series. Resources and links: Karoline’s book ‘What Is a Complex System?’ Simplifying Complexity - What makes ant colonies robust? Simplifying Complexity - The Economy and Complexity Science: Part 2 Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In most of our episodes so far, we've taken a single concept and looked at it through the context of a single example. But in this episode and the next, we're going to pull back the camera to get a bird's-eye view of complexity science, by exploring the features common to all complex systems. We're joined again by Karoline Wiesner, Professor of Complexity Science in the Department of Physics and Astronomy at the University of Potsdam in Germany. In this episode, Karoline is going to explain four conditions that we see in complexity science: numerosity, disorder and diversity, feedback, and non-equilibrium. At the end of the episode, she's going to bring them all together to explain a central concept of complex systems: emergence. Resources and links: Karoline’s book ‘What Is a Complex System?’ Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode we talked all about intelligence, specifically about what made us intelligent. In this episode we jump into artificial intelligence, and we're joined again by David Krakauer, President and William H. Miller Professor of Complex Systems at the Santa Fe Institute. This episode was recorded before the release of GPT-4, so David doesn't mention it specifically, but he does take us through the history of artificial intelligence, from Alan Turing, all the way to machine learning and neural networks. And he's going to ask the question: Are we really building something that's intelligent, or are we just building mimics and parrots? Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

With the recent release of GPT-4, now seemed like a good time for our episodes on intelligence. And not just artificial intelligence, but intelligence in general. To help us on this journey, we're joined again by David Krakauer, President and William H. Miller Professor of Complex Systems at the Santa Fe Institute. This episode is part one of our two-part conversation with David about intelligence. In part 2, David is going to cover artificial intelligence. But in this episode, we're going back to basics and David asks, what is it that makes us intelligent? Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

If there's one type of music that goes particularly well with complexity science, it's free jazz. The sort of jazz that you get when you put a group of musicians together without a conductor or any written music. But despite this, they still produce incredible music. So how does this group of musicians play so tightly together, whilst creating such dramatic changes to their music? In this episode, we're joined again by Tyler Marghetis, Assistant Professor of Cognitive and Information Sciences at the University of California, Merced. Tyler is going to return to the concept of tipping points, but this time, he's going to explore tipping points through the context of jazz music. To understand how they occur, he's going to go to one of the most unlikely places for help: the study of ecologies. Resources and links: 'Zadok the Priest' by Handel - Classical (composed) - This piece tips at 01:22 Listen on Spotify Listen on YouTube 'Implosion' by Alex Levine Quartet - Free jazz (improvised) - This piece tips between 00:10 to 00:30, and again between 02:20 to 02:40. Listen on Spotify Listen on YouTube Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Imagine you were going to Mars with a swarm of robots, and you needed to send those robots out foraging. How would you program them? A traditional top-down approach to programming would mean programming what every single robot is going to do, and that's going to get complicated fast. So in this episode, we're joined by Melanie Moses, Professor of Computer Science at the University of New Mexico, and External Faculty at the Santa Fe Institute. Melanie is going to explain how you can take lessons from complexity science, and utilise a bottom-up approach to programming a swarm. In other words, she's going to explain how you can program the robots to interact with one another. And if you thought you'd heard the end of scaling or power laws, then you're in for a surprise, because Melanie is going to share how scaling fits in with her work on getting robots to work as a team. Resources and links: Simplifying Complexity - How do bees self organise? Simplifying Complexity - Scaling 1: Why do we live longer than mice? Simplifying Complexity - Scaling 2: You and I are fractals Simplifying Complexity - Scaling 3: Why companies die, but cities don’t Complexity - Melanie Moses on Metabolic Scaling in Biology & Computation Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In the last few episodes, we learnt all about scaling laws or power laws and how they apply to mammals. In this episode, the final part of our discussion of scaling and complex systems, for now, we're looking even bigger. We're joined again by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute, who in this episode will be leaving mammals behind to look at other complex systems. In particular, Geoffrey is going to explain how scaling laws apply to cities or companies. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode, you heard all about the relationship between a mammal's weight and its metabolic rate, and how this holds true regardless of the size of the mammal. You heard other examples of so-called scaling laws, and how these laws seem to be guided by the number four. In this episode, we're joined again by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute. Geoffrey is going to explain why when we double the size of an animal, we only increase its metabolic rate by three quarters. He's going to explain why the number four is behind these curious laws, and he's going to reveal how you and I are fractals. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Have you ever thought about why the average human lifespan is 80 years? Or why smaller animals, like mice, live for much shorter periods compared to large animals like blue whales? To help answer these questions, we're joined by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute. Geoffrey will introduce us to the concept of scaling in complex systems, and how it helps explain not just lifespan, but a whole range of physiological characteristics in mammals. In Parts 2 and 3, he will explain what creates these laws, and how they apply not just to mammals, but to companies and cities as well. Resources and links: Scaling Graphs Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

One of the things that make complexity science so fascinating is the diversity of the systems that it applies to. In this series so far, you've learnt about everything from ecologies to economies, tipping points in ecologies and economies, to power and influence in the 1400s, and even the spread of coronavirus in the lungs and the thing that brings all of these different topics together is complexity. This means that we can study one system to help us understand other systems — including bees. In today's episode, Orit Peleg, Faculty at the University of Colorado, Boulder, and External Faculty at the Santa Fe Institute, explains how bees self-organise and produce sophisticated behaviour. In this case, you'll hear how thousands of bees can work out where their queen is at any given point. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode, we heard from W. Brian Arthur, who shared his journey in economics as he studied increasing returns. Now, Brian's going to take us to 1987, to a small meeting in the Rockies in Santa Fe. At this time, he was struggling to gain recognition for his work within the economics community, but it was when Brian went to what would become the Santa Fe Institute that things really kicked off. In this episode, you're going to hear again from W. Brain Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he remembers the early days of the Santa Fe Institute. From the early meetings of economists, physicists, and a biologist that started it all, to an early model Brian built of a stock market that was unique to any models before it — because this model included booms and busts. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

Mitchell Waldrop's 'Complexity' brought complexity science into the limelight with an account of the early days of the Santa Fe Institute. One of the people who appear in this book is W. Brian Arthur, the engineer turned economist who found economics unsatisfactory — because it treated the economy purely as a system in equilibrium when he knew it very obviously wasn't. In this episode, you'll hear from W. Brian Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he explains his journey to understanding the economy as a non-equilibrium system, and his work on increasing returns. But what are increasing returns? Well in complexity terms, it's how positive feedback affects the economy. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

How do you model a complex system? Traditionally we would observe how the system is behaving and create equations to mimic this behaviour, but this doesn't work for complex systems. This is because the interactions between agents in a complex system can significantly impact the system's overall behaviour. In today's episode, Melanie Moses, Professor of Computer Science at the University of New Mexico, will answer this question. She'll introduce us to agent-based models, which are very different to how we traditionally model systems. More specifically, Melanie will explain how she used agent-based models to understand the spread of coronavirus in the lungs. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

A key part of complexity science is understanding the behaviour of networks. Networks are groups of interacting agents, and they're all around us; our friendship groups, our colleagues, and even interactions online are all examples of networks. But what role does influence and power play in these networks? In today's episode, we're joined by Matthew Jackson, William D. Eberle Professor of Economics at Stanford University, and External Faculty of the Santa Fe Institute. Matthew is going to break down the key factors of a network, with an example from all the way back in the 1400s, featuring the Medici family. He'll explain how Cosimo de’ Medici used his network to wield power, and what about his network made it so successful. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

In our last episode with Tyler Marghetis, we learnt about how a complex system can tip from one state into another. But what happens when systems don't tip or fail? What makes a system robust? In today's episode, we're talking with Karoline Wiesner, a Professor of Complexity Science in the Department of Physics and Astronomy at the University of Potsdam, Germany. She breaks down the characteristics of a robust system, through the context of an incredibly robust complex system — the ant colony. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

A fascinating property of a system's behaviour is its ability to change, and change quickly. For example, how does an economy go from boom to bust so suddenly and unpredictably? That is to say, how does it 'tip' from one behaviour to another? What are these tipping points, and are they really as unpredictable as they seem? In today's episode, we speak to Tyler Marghetis, Assistant Professor of Cognitive and Information Sciences at the University of California Merced. He pulls apart the underlying reasons why the behaviour of a complex system can radically change. He also poses the question, can you tell when a system is about to tip? Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.

What is complexity science, and why should you care about it? Well, complexity science is all about understanding the systems that are all around us — systems like the economy, your body, cities, companies, and the environment. To properly understand how these systems work, and how they fail, you need to understand complexity science. Because complexity science provides us with the underlying principles that govern these systems. In today's episode, we speak to David Krakauer, President and William H. Miller Professor of Complex Systems at the Santa Fe Institute. He explains the underlying principles of complex systems and what they have in common — even if they all seem completely unrelated. He also talks through the history of complexity science and provides his top three takeaways on how you can start to think about complex systems. Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.See omnystudio.com/listener for privacy information.