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.

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.

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.

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.

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.

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.

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.

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.

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.

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.