3 Takeaways - The Hidden Pattern in Fires, Earthquakes, Stock Market Crashes, and Even Wars (#242)

Episode Date: March 25, 2025

Catastrophes seem to be the new normal. There’s a stunning new scientific belief that although catastrophes are unpredictable, there’s a hidden pattern that explains them all. In other words, fires, avalanches, wars and even stock market crashes aren’t a glitch in the system, they are the system itself. Listen as noted physicist Mark Buchanan reveals more.

Fires, earthquakes, stock market crashes, and even wars. They seem totally unrelated. But what if all these catastrophes follow the same hidden pattern? Is there a natural structure of instability which explains both natural and human catastrophes and why they happen. Hi everyone, I'm Lynn Toman and this is Three Takeaways. On Three Takeaways I talk with some of the world's best thinkers, business leaders, writers, politicians, newsmakers, and scientists.

Each episode ends with three key takeaways to help us understand the world and maybe even ourselves a little better. Today I'm excited to be with Mark Buchanan. He's a prize-winning physicist and author. He was formerly editor with the international science journal Nature, and he's written for many other journals and newspapers, including the New York Times. His books and articles explore ideas of physics to better understand patterns in other fields. His wonderful book, Ubiquity, tells the fascinating story of the discovery that there is a natural

structure of instability woven into the fabric of our world. I'm excited to find out more about the pervasiveness of instability and why both natural and human catastrophes happen. Welcome, Mark, and thanks so much for joining Three Takeaways today. Well, thanks for having me on. It is my pleasure. Mark, can you explain the new math that underlies our world? So the new math is an embrace of irregularity. I think the mathematics of antiquity, we started with simple things like squares and cubes and spheres and naturally smooth surfaces.

All of our mathematics for hundreds of years was about those things. Only in the past century have we started trying to describe the mathematics of things like rough surfaces, like the break a brick and look at the fractured interior. That surface has a mathematical pattern to it. It's highly irregular and in the the old mathematics, we couldn't even describe that. There's a new mathematics using fractals, chaos, and things like that that does understand that kind of irregular patterns. And those irregular patterns are the natural patterns that you actually find in the real world all

around us. And so the new maths is something that embraces erratic unpredictability, rough edges, great inequalities in patterns over time that show wild upheavals, periods of quiescence suddenly punctuated by bursts of activity. That's normal. That is the normal pattern in our world. We used to think of that as strange behavior. That's not strange behavior. That's normal behavior. That's kind of erratic, highly unpredictable processes are the norm in our world. The simple patterns of linear change in cycles, those are what is unusual. And we just came to those first as we were learning. So the erratic mathematics is the more modern math.

And that is at the heart of all modern physics. You found that fires, earthquakes, avalanches, stock market crashes, wars, and other catastrophes follow the same hidden patterns. What are those hidden patterns? So the hidden patterns are, first of all, a mathematical propensity for many small events and only a few large events. The second pattern is that those few large events really dominate the system in terms of the consequences. So if you look at the total number of acres that get burned in forest fires, then you

can sum up all the many, many smaller forest fires, sum up all the total of all the acres that have been burned, and you'll find it's a small fraction. Whereas you may look to the two or three largest forest fires, put them together, and they will account for maybe 90% of all of the acreage that gets burned. So the second pattern in this particular critical state organization is that the few largest events actually dominate in terms of their consequences for the system as a whole. Even though there's so many more small ones, they are so much smaller than

the large ones, the large ones end up dominating the consequences for the system. And so in the context of things like stock market crashes, you'll find that, you know, the number of people who get their portfolios wiped out is dominated by the few big crashes that occur rather than the small movements that are happening all the time. And if economists can avoid the worst crashes and avoid the worst problems, then you're way ahead in terms of protecting people and protecting the system. So that's the second pattern. Those are the two, I think, particular patterns. The power law, distribution, and then this thing about how the biggest events carry almost all the weight. You believe that catastrophes aren't random, that they follow

essentially rules. Can you explain more? The pattern isn't something that is necessarily predictable, so it would be great. We'd all like to be able to say catastrophes are predictable., therefore I can predict the next earthquake, we can warn everybody about it when it comes, know it'll be injured, or we can predict the next market crash, or we can predict the next great Sun storm that's going to wipe out the Internet. The evidence suggests after many years of trying that people have generally failed to be able to predict these systems. And now we believe we know why.

And the reason is that all of these systems are systems that have evolved into this critical state so that the dynamics are extremely unpredictable. A catastrophe is always possible at any moment, but is still very unlikely. And also that they are essentially beyond predictability. You cannot predict the timing of the next particular catastrophe. However, that doesn't mean we can't predict anything. So understanding the statistics of these systems means we can predict the likelihood of the largest events. We can predict the likelihood of the largest events. We can predict the time scales on which they're likely to happen. We can predict how much damage they're going to cause when they do happen. And we can try then to engineer our systems and our support systems to be prepared for

those large-scale events when they do strike, because we know they're going to strike. Does that mean that instability isn't a glitch in the system, whether it's avalanches or earthquakes or pandemics or wars, but it's actually the system itself? Precisely. That's the idea. The instability is coming from the overall collective organization of the system itself. And the world just seems to have a natural dynamic by which stresses and strains build up over time to put the system into this condition where it's maximally unpredictable and prone to large-scale events.

Mark, can you summarize the underlying pattern of instability and where you see it? The underlying pattern of instability is a complex web of cause and effect by which stresses and strains get distributed through a system that can be a pattern of stresses and strains get distributed through a system. That can be a pattern of stresses and strains in the earth's crust. It can be a pattern of interlocking regions of burnable wood in a forest. It can be patterns of interlocking hopes and expectations and beliefs in an ecology of investors in a stock stock market, and within those systems there are these webs of instability that are very difficult to see because lots of this information isn't accessible to us at any moment, but they're there.

And the critical state suggests that all of these systems, these networks of instability, organize themselves into a state such that the system is always prone to wild unpredictability. So the next small cause that triggers a change always has the potential to create a huge upheaval across the entire system. And more frequently than we would expect, it will cause a large-scale rearrangement and response in the system and a large upheaval that will seem to everyone totally out of the blue, unpredictable. How could that have happened? I didn't see anything that suggested that was about to happen. And that's just really the case. All

of these systems do not have any precursors or warning signs that give away the idea and show you that there's just about to be a big upheaval just before it happens. This is just the world as we live in it. I see these power laws and catastrophes arising in some of the most important physical phenomena that affect our lives, such as earthquakes, in forest fires, in storms, hurricanes, even things coming from space, such as solar wings, but also in human activities themselves. So in the way stock markets work and fluctuate from day to day, the way prices move around so erratically and apparently with no cause, the way epidemics break out occasionally,

sporadically and unpredictably. So in all these different systems, they both show the mathematical pattern of the power law, which is the kind of classic archetypal signature of the critical state organization, where large-scale upheavals happen much more frequently than we would expect on basis on our intuition. And those large-scale upheavals are the act through which most of the change occurs in these systems over time. So this pattern occurs both in human systems and in natural systems. Understanding the critical state just gives us a new

conceptual framework for helping to understand why the system is like this, why it's unpredictable, why we're perpetually mystified by these systems and trying to explain them and often without much success. And this idea of instability underlying so many natural and human systems really was discovered through experiments with grains of sand and building sand piles and then applying the math to other types of systems. Can you briefly summarize? Some physicists were really interested in trying to see if they could create a really interesting system, because we're used to the idea that big events have big, equally

big causes. So they started playing around with mathematical models. They wanted to see if they could make a system where the cause at every moment was the same, just one small triggering event. And they wanted to see if they could create a system where the output would be wildly unpredictable, not only in detail but in scale, so that if you recorded the outcomes over many millions of times, you'd find that some events that were triggered by the same trigger were literally hundreds of millions of times bigger than others. Can you make such a system? Seems unlikely, but they found one. And so first they found it in a mathematical model, but it turned out to be logically equivalent to the sandpile model, where you drop a grain on a table, drop another grain, gradually

the pile build up into a steady state, and then you drop a next grain and you measure how big is the avalanche, drop another grain, measure the avalanche. And what you find is that even though every time you drop a single grain, it's always just a single grain every time, it couldn't be more predictable. The outcomes are wildly unpredictable. Frequently, the outcome is an avalanche where two or three grains trickle down the hill. But occasionally, you have an avalanche where 10 million grains go down the hill. And in fact, you find that those avalanches are far more likely than you would expect

if the statistics of the system were anything like the normal statistics we're used to in normal life. Some of the avalanches are 100 million times as big as the typical avalanches, the small ones that happen more frequently. And if I can summarize, it's really based on this idea of sand piles where you're adding an additional grain of sand and where you have increasing fingers of instability and that additional grain of sand

could cause a small avalanche or a big one that it's completely random. Is that right? Precisely. Is there anything we can do to mitigate these catastrophes? One thing is we can be prepared with an awareness that they are going to happen, that they're going to happen at moments we didn't prepare for them for. And so we need to build resilience into our systems. We need to realize that we don't understand the overall system, and we're probably not going to be able to predict it. So we shouldn't rely on having an immensely powerful predictive capacity. Maybe you should try to predict, but also realize that our predictive capacity is limited

and we should invest more in building resilience into our systems, our ability to regenerate and adapt after capacity should be enhanced and strengthened. In a way, it's just living and accepting that the world is wilder and more unpredictable than we would like and predict. And that's okay. If we just accept it, we can build more resilience and still survive. And what are the three takeaways you'd like to leave the audience with today? The first takeaway is the world is simpler than you think. Even though it seems hugely complicated and you look at the stock market every day

and it's in tumult and crazy things are happening and earthquakes, people have been trying to predict them for centuries and gotten essentially nowhere in being able to do so. It's not because those systems are inherently extremely complicated and their causes involve 2,000 factors that we'll never get our hands on. The processes are pretty simple, but the dynamics to which they lead just happen to be intricate. They involve this critical state. And so the world, even though it's complex in its outcomes and its heart, it does seem to be dynamically simpler than we think. The second lesson, I think, historians are still a couple centuries out of date in terms of the mathematics they use. Historians haven't been studying

a lot of modern physics and mathematics, probably for good reason. And so the metaphors that they use for how change occurs in our world are things like gradual trends and reversals and cycles that go up and down. That's all inspired by the mathematics of planets and astronomy. But the mathematics of today's physics involves much more an embrace of erratic processes, wild unpredictability. Even though the dynamics is simple, the outcomes are very complicated. Update your conceptual ideas about how change comes in the world. And then the third takeaway, I guess, is that you have more power than you think because the world is poised on the edge of this unstable state. And in a sense, it doesn't know what it wants to do.

And if you give it a push in one direction or another, you may be able to take it further than you think. Everyone potentially has the power to initiate great change through their own activities. I love those three takeaways. Thank you, Mark. This has been great. I loved your book, Ubiquiti. Mark Bailey It's been a pleasure talking to you. Dr. Sara McNeil If you're enjoying the podcast, and I really hope you are, please review us on Apple podcasts or Spotify or wherever you get your podcasts. It really helps get the word out. If you're interested, you can also sign up for the Three Takeaways newsletter at three takeaways.com

where you can also listen to previous episodes. You can also follow us on LinkedIn, X, Instagram and Facebook. I'm Lynn Toman and this is Three Takeaways. Thanks for listening.