#517： What Big-Time Catastrophes Can Teach Us About How to Improve the Systems of Our Lives
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Summary
Chris Clearfield and Andras Tiltchik discuss their new book, Meltdown: Why Our Systems Fail and What We Can Do About It, and how to prevent catastrophes from happening in the future. They discuss how they came to the idea of catastrophic failures and what they can teach us about how to be successful in life.
Transcript
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Brett McKay here and welcome to another edition of the Art of Manliness podcast.
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Whenever a financial or technological disaster takes place, people wonder if it could have
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possibly been averted. I guess they say that the answer is often yes, and that the lessons
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around why big disasters happen can teach us something about preventing catastrophes
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in our businesses and in our personal lives. Their names are Chris Clearfield and Andras
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Tilcek. They're the authors of Meltdown, Why Our Systems Fail and What We Can Do About
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It. We begin our discussion getting into how they got interested in exploring how everything
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from plane crashes to nuclear meltdowns to flash stock market crashes actually share common
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causes. We then discuss the difference between complicated and complex systems, why complex
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systems have weaknesses that make them vulnerable to failure, and how such complexity is on the
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rise in our modern technological era. Along the way, Chris and Andras provide examples
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of complex systems that have crashed and burned, from the Three Mile Island nuclear reactor meltdown
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to a Starbucks social media campaign gone awry. We enter a conversation digging into specific
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tactics engineers and organizations use to create stronger, more catastrophe-proof systems,
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and how regular folks can use these insights to help make their lives run a bit more smoothly.
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After the show's over, check out our show notes at aom.is slash meltdown.
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Chris Clearfield, Andras Tiltchik, welcome to the show.
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So you two co-authored a book, Meltdown, what plane crashes, oil spills, and dumb business decisions
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can teach us about how to succeed at work and at home. And I was telling you all before we started,
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I was actually, I read this book while I was on an airplane, reading about catastrophic plane
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crashes, which made me feel great. I'm curious, how did you two start working together on this
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idea of exploring catastrophic failures and what they can teach us about how to be successful in
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Well, I'll tell maybe kind of my half of how I came to this work, and then Andras, you can maybe pick
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up with sort of how we came together with it. I studied physics and biochemistry as an undergrad,
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so I was always kind of a science geek. But then I got intercepted and ended up working on Wall
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Street, and I ended up working there kind of during the heart of the 2007-2008 financial crisis.
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And so all around me, the whole system of finance was struggling and collapsing. And
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I had this observation that, oh, I think that Bank X is going to do better than Bank Y in this
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process. And then I kind of took a step back and thought, well, that's really interesting.
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I don't work at either of these places. How could I possibly say something about the ability of these
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firms to manage risk just as an outsider, just based on kind of a really bare understanding of
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their culture? And so that made me just really interested in this question, sort of how organizations
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learn to manage failure and manage complexity. And at the same time, I was actually learning to fly
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airplanes. I'm a pilot. I'm an instructor now. But at the time, I was just starting to learn.
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And I was kind of reading obsessively about why pilots crash planes, like why well-trained flight
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crews make mistakes that mean that they make bad decisions or they have these errors in judgment that
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caused them to, despite their best efforts, to kind of crash and do the wrong thing.
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And that to me was also the same kind of thing. It's like the culture of a team mattered and the
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culture of an airline or the kind of way people approach these challenging situations really
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mattered. And then the third piece of the puzzle that made me realize what a widespread issue this
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was, was when BP's Deepwater Horizon oil rig exploded in the Gulf of Mexico. And just because I was
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interested, I started reading about that accident and realized that there was this kind of same set of
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sort of system and organizational factors at play in that accident. It was a complex system with a
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lot of different aspects to it, a lot of different things that had to be done correctly. And there
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wasn't a great culture to support that. And so that made me realize what a widespread problem it was.
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But I was still thinking about it mostly from the systems angle. And then Andras and I came together
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at some point and started thinking kind of more broadly about it together.
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Yeah. And I came from a social science angle. And so I was more interested in the organizational
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aspects of this. But in some ways, we started to converge around this idea that, you know, if you
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look at these different failures, the financial crisis, Deepwater Horizon, a plane crash, if you read
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enough of the of the literature and the accident reports on these, you start to see some of the same
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themes come up over and over again. And I thought that was that was fascinating that there is a sort of dark
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side to organizations, but it's not unique to each failure, there's some kind of fundamentals. And in some
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ways, we started to see that over time as this very positive message potentially in that, while it implies
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that we are making the same dumb mistakes over and over again, across different industries and in different life
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situations, if it's the same things that get us into trouble across these situations, it also means
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that if we figure out a solution in some fields, we can try to apply those in other fields, and we can all
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learn from other people from other industries. And so that was, it wasn't just gloom and doom. And I think
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that that really helped move this whole project forward.
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Well, let's talk about, I think it's interesting, you guys highlight this, the study, the scientific
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study of catastrophe is relatively new. And it started with this guy named Charles Perrault. How do you say
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his last name? Perrault? Yeah, Perrault. Perrault. Andres, I think this would be your in your wheelhouse
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since he was a social psychologist. What was his contribution? What did what was his insight about
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catastrophic failures and like why they occur on an organizational level? Yeah, so Charles Perrault or
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Chick Perrault, as people more affectionately know him, he's actually a sociologist, fascinating
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character. He's in his early 90s. Now, he got into this whole field back in the in the late 70s, in the
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wake of the Three Mile Island partial meltdown. And, you know, his whole entry into this field was just
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just so interesting to Chris and me when we were studying it. He really, you know, for an outsider,
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he was studying very obscure, organizational, sociological topics. And then he ends up looking
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at Three Mile Island from this very organizational, sociological, social scientific perspective,
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rather than the standard kind of hardcore engineering perspective. And in that process,
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he develops this big insight that, you know, one of the most fascinating things about Three Mile
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Island is that, you know, it's this big meltdown. And yet, the causes of it are just so trivial,
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or, you know, it's really a combination of a bunch of small things, kind of like a plumbing error
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combined with some human oversight or problems with kind of human attention, and sort of nothing out of
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the ordinary. It's not like a big earthquake or a terrorist attack. And those those little things
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instead just combine and bring down this whole system. And that really inspired Perrault at the time
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to kind of figure out what are the systems where we would see these types of failures,
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these types of meltdowns. And that's when he develops this interesting typology of systems,
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which really has two parts. One is complexity, the other is tight coupling.
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But complexity just means that the system has a lot of elaborately connected parts.
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So it's like an intricate web, less like a linear assembly line type of process. And it's not a
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very visible system, you can just, you know, you can just send a guy to the nuclear core and tell
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them, hey, take a look, look at what's going on in there and come back, report back to me,
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you have to instead rely on indirect indicators. And the other part of the of this typology that he
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comes up with is is what he calls tight coupling, which really just means the lack of slack in a
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system that the system is unforgiving, it doesn't really have a lot of margin for error. And he says
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that when when a system has both of those components, then it's especially ripe for these
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types of big surprising meltdowns that come from not from a big external shock, but from little things
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coming together inside the system in surprising ways. And and he says that's because complexity tends
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to confuse us, it tends to produce symptoms in a system that are hard to understand how to diagnosis
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as they are happening. And if the system also doesn't have a lot of slack in it, if it's tightly
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coupled, if it's unforgiving, then then it's just going to run away with it, it's just going to run
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away with us, we will get confused, but it will be and we'll be surprised. And it's going to be a
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particularly nasty kind of surprise, you can't easily control it. And in some ways, you know, what we do in
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this book is, is that we build on pearls inside that came from this, you know, one particular
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accident at Three Mile Island. And we try to extend and apply that insight to modern systems,
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basically 40 years later. And we still see a lot of resonance of that very simple, yet deeply
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insightful framework. Let's take this idea of complexity, because you guys highlight in the book
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that something can be complicated, but not complex. What's the difference between the two
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things? You know, I think that the complexity is a very specific way of thinking about the amount of
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connectedness in the system, the amount of kind of the way that the parts come together. So a complicated
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system might be a system that has lots of different pieces or has lots of moving parts,
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like the kind of hardware in our phone. But most of those things, they interact in very stable and
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predictable ways. Whereas a complex system, it has these interactions that are sort of important
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either by design or important kind of by accident. You know, the Three Mile Island example is just one
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example of this, but where you have these kind of parts of the system that end up being
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physically or kind of informationally close to each other that's not intended by the designers.
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Delta, for example, had a fire in a data center, which brought down their whole fleet of planes.
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I don't mean made crash, but I mean, you know, grounded their whole fleet of planes. They couldn't
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dispatch new flights. And so you have this really unintentional connection between these different
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parts of the system that come from the way it's built, that come from the complexity. So I think
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complexity refers to that, the kind of the connections being more important than the components themselves.
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Okay. So like a complicated system or a complicated system would be like the Rube Goldberg machine,
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right? Where there's like different little parts, but it all works in a linear fashion.
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And if one part's messed up, like you can figure out, you can replace that part and it'll keep going.
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You can keep things moving. With a complex system, it would be something like, there's like a network
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effect, right? Where parts that you don't think are connected linearly start interacting and it affects
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the whole system. Exactly. That's a great way to put it. Okay. You should come with us on other
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podcasts so you can help us with that. All right. So that's complexity. And so the other issue,
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the other part of what causes catastrophic meltdowns is the tightly coupled. So what would be an
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example of a tightly coupled system, right? Where there's very little slack.
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I think one neat way to think about it is just take one system and sort of think about when it is more
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or less tightly coupled. So take an airplane and you're at the airport, you're just leaving the gate.
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It's a relatively loosely coupled system at that point in the sense that there's a lot of slack in
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it. There's a lot of buffer. There's a lot of time to address problems and figure out what's going on
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if something bad or concerning happens, right? So say you are the pilot, you notice something going on,
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you can go back to the gate, you can bring in other people, you can have a repair crew,
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you can go out, have a cup of coffee, think about what's happening. Once you are in the air,
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it's a lot more tightly coupled. And once you are, say, over the Atlantic, very far from any other
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alternative option, such as another airport where you could land, it's extremely tightly coupled.
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There's no time to think, there's no time to get new parts, there's no other resources you can
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lean on and you can just say, hey, I'm going to, this is a very complex, confusing situation,
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I'm going to step outside, have my coffee, come back after I had some fresh air, and then solve it.
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Things just happen fast in real time and you just have to work with what you got.
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And I got another example I think you give in the book, sort of like just a run-of-the-mill
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tightly coupled system is like if you're cooking a meal, right? And you have to time things just right
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so that the meal is all done at the same time. And if you miss one thing up, then like you're going
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to be 20, 30 minutes late with, you know, serving your meal.
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Right. Yeah, that's a great one. And the other example that I like to think about that, you know,
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just crops up in my personal life when I have to travel for work is, you know, what does my flight
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look like? Like, do I have a direct flight from, you know, Seattle to San Francisco? That's pretty simple.
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And, you know, there might be problems in the network, but by and large, if I show up for
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that flight on time, I'm going to get on the plane, it's going to fly to San Francisco and
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I'm going to be fine. But if I'm going to, you know, like if I'm going to someplace on the East
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coast from Seattle where I live, then do I have to change planes in Chicago? Well, that adds a bit
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of complexity. Now there's kind of more parts of my trip where things can go wrong. And, you know,
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if I have two hours to change planes, well, that's kind of a, that's sort of a buffer.
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That's relatively loosely coupled. If I have four hours, it's super loosely coupled. But if I have
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45 minutes, suddenly I'm in a really tightly coupled system where any delay caused by complexity
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is likely to be very disruptive to my whole trip, be very disruptive to my whole system.
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How has digital technology and the internet added more complexity and more coupling to our systems?
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There's so many good examples of this. And, and I think this is one of the fascinating things for
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us about working on this book that when we started, we really expected to be working on,
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you know, the big, like writing about big disasters, writing about Deepwater Horizon and,
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and, and, you know, Three Mile Island. And we, we definitely write about those things,
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but what struck us was how many more systems have moved into, into Perot's danger zone that are
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complex and tightly coupled. And there's so many examples, but you know, one of the ones that I
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love is our cars these days. So I have a 2016 Subaru Outback. This is not, I mean, it's a, it's a
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lovely car, but it's not a fancy car. And yet it, it has the ability to look up stock prices on the
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infotainment system in the dashboard. And so, you know, suddenly you've taken this,
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this piece of technology that used to be relatively simple and relatively standalone. And now many,
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many parts of the car are controlled by computers themselves. And that has very good reasons. That's
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to, you know, add features and, and increase the efficiency of how the engine works and, and all of
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this good stuff. But now suddenly manufacturers are connecting these cars to the internet and they
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are part of this global system. And there's this fascinating story in the book that was covered
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originally by a Wired reporter called Andy Greenberg. And it is about these hackers that
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figure out that they can through the cellular network, hack into and remotely control Jeep
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Cherokees. And so they, they've done all this research. And fortunately, the guys who did that
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were, you know, white hat hackers. They were hackers kind of whose role was to find these security
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flaws and help manufacturers fix them. But that didn't necessarily have to be the case. So you suddenly
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have this example of complexity and, and tight coupling coming because manufacturers are now
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connecting cars to the internet. I mean, Tesla's can be remotely updated overnight while it's in
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your garage. And, you know, that adds great capabilities, but it also adds this real ability
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for something to go wrong, either by accident or, or nefariously.
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But you also give other examples of the internet creating more complexity and sort of benign,
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well, not benign ways, but ways you typically don't think of. So you gave the example of
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Starbucks doing this whole Twitter campaign that had a social media aspect to it and it
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Yeah, totally. I think that, you know, that was one of the early examples that actually caught
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our attention. This is a Starbucks marketing campaign, a global marketing campaign around the,
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around the holiday season where they created this hashtag, hashtag spread the cheer.
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And they wanted people to post photos and warm and fuzzy holiday messages with that hashtag.
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And then they would post those messages on their, on their social media, as well as in some physical
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locations on, in, on large screens. And in some ways they created a very complex system,
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right? There were all these potential participants who could, who could use the, the, the hashtag.
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They would be retweeting what they were saying. And then they were, there was another part of the
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system, which was also connected is that the, the Starbucks tweets with the hashtag would be
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these retweeted tweets and individual tweets would be appearing in the physical locations.
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And, and it was an, it was an ice skating rink, right? They showed up at a screen at an ice skating
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Yeah. So, you know, very prominent location. There's a great Starbucks store there.
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Lots of people are watching it and, and they start seeing these messages come up that are,
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that are very positive initially. And people are tweeting about their favorite lattes and their
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gingerbread cookies and things like that. And then all of a sudden it turns out that some people
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decide to, to, to essentially hijack the campaign and start posting negative messages about Starbucks,
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critical messages about their labor practices, about their kind of tax avoidance scandals,
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that they were caught up in at the time, especially in the, in the, in the UK.
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And Starbucks of course thought about this possibility. They had a moderation filter put
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in place, but the moderation filter didn't function for, for a little while and talk about a tightly
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coupled system, right? Once the genie was out of the bottle, people, when, once people realize
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that anything they say with that hashtag will be shared both online and, and on this,
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in these physical locations, then it was, it was impossible to put the, put the genie back into
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the bottle. And, and, and again, even after they, they fixed the moderation filter, it was just,
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it was just too late. It was by that point, the, the, the hashtag was trending with all these negative
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messages. Then traditional media started to pay attention to this funny thing happening on social
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media. So there was another layer to that. Social media is connected to traditional media,
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which then fed back into people tweeting, uh, and Facebook sharing more and more about this.
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So very quickly, essentially a small oversight and, and a little glitch in the system that,
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that only lasted for a relatively short period of time, turning to this, this big, embarrassing PR
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fiasco that you couldn't just undo that, that, that, that was just spiraling, spiraling out of control.
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All right. So the complexity was the social network. People act in unexpected ways. You can't predict
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that. And the tight coupling was, it was happening in a real time. There's nothing you can do about it.
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Really. We're going to take a quick break for your word from our sponsors. And now back to the show.
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Well, you also give examples of how technology has increased complexity and tight coupling in our
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economy. And you give examples of these flash crashes that happened in the stock market where,
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you know, the, basically there'll be like this giant crash of billions and billions of dollars.
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And then, you know, a second later, a minute later, it goes back to normal.
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Yeah. It's totally, it's totally fascinating. And I think what's fascinating about it is there are all
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these different, I mean, it's very, very similar to social media in a way. There are all these
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different participants looking to see what everybody else is doing and kind of using the output of other
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people as, as the input to their own models and their own ways of behaviors. And so you sometimes get
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these, these kind of critical events where the, you know, the whole system starts moving in lockstep
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together and, and moves down very quickly and then kind of bounces back. And what, I mean, one of the
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things that I think is interesting about it is that regulators and, and policymakers, they sort of,
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their mental model, generally speaking of, of the stock market is it's kind of like it used to be just
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faster. In other words, like, you know, it used to be that you had a bunch of guys on the floor of the
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New York Stock Exchange, sort of shouting at each other. And, and that's how trading happened. And
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the sort of basic mental model, and it's changing a little bit, but the basic mental model of, of
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regulators has been like, well, that's still what's happening, except computers are doing the kind of
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interaction and, and therefore everything is faster. But, but really the truth is that it's a
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totally different system. It's like the character of, of finance has totally changed. You also saw that
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in the, in the financial crisis in 07, 08, the way that the, the decisions that one bank or
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institution made because of all the inner linkages in finance, the way that those decisions really
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cascaded throughout the system. So if, if one participant, you know, started looking shaky,
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then the next question was, well, who, who has exposure to these people and, and kind of,
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how is that going to propagate through, through this system? You know, so that was the big,
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the big worry with AIG that it wasn't so much the, the failure of AIG that was critical.
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That was hugely important. It was all of the people that had dependent on AIG to kind of,
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not only in the sort of insurance as we think about it traditionally, but all the financial
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institutions that had made these big trades with AIG. It did totally fascinating stuff.
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And it sounds like things are just going to get more complex as, you know, the whole thing
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people are talking about now is the introduction of 5G, which would allow us to connect cars,
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health devices, connect homes with like smart things, like things are going to just get more
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and more complex. Yeah. And I think that kind of gets to, gets to the fundamental,
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I think the fundamental message of the book, which, which for us, from our perspective is that
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this complexity, we can't turn back the clock, right? I mean, I, I, I do tend to be skeptical of
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the cost and benefits of, of some of these trade-offs, like my car being, you know, internet
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connected, but the truth is these systems in general, our world in general is going to get
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more complex and more tightly coupled. The thing that we do have some control over is the things
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we can do to manage this, the things we can do to operate better in these systems. And, and I think
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for, for us, the key question was at the, at the heart of the book that we try to answer is, you know,
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why can some organizations build teams that can thrive in these complex and uncertain environments
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while, while others really, really struggle? And so that's a lot of what the focus of the book is
00:23:58.180
on. It's on this kind of the upside in a sense, how can you get the capabilities while being better
00:24:02.680
able to, to manage the challenges? And it sounds like everyone needs to start becoming, you know,
00:24:08.140
somewhat of an expert in this because systems that they interact with on a day-to-day basis are going
00:24:12.760
to become more complex. And so they need to understand how they can get the upside with,
00:24:16.740
while mitigating the downside. So let's talk about that. What, some of the things you all uncovered.
00:24:21.120
So you start off talking about how trying to prevent melt, meltdowns can often backfire and
00:24:28.200
actually increase the chances of meltdowns happening. And you use the deep water horizon
00:24:33.580
explosion as an example of that. So how can adding, you know, safety measures actually backfire on you?
00:24:39.780
Yeah. So we see this at deep water, we see this in hospitals, we see this in, I think we have seen
00:24:45.960
this in aviation. We see this in, in all kinds of systems. It's a very understandable basic human
00:24:52.020
tendency when, that when we encounter a problem or something seems to be failing, we want to add one
00:24:58.780
more layer of, of safety. We add some more redundancy, we add a warning system, we add one more alarm
00:25:04.720
system, more bells and whistles to the, to the whole thing in the hopes that we'll be able to
00:25:12.060
prevent these things in the future. Often, however, especially in the systems that are already so
00:25:17.500
complex to begin with, what ends up happening is that those, those alarms and those warning systems
00:25:23.660
that we put in place themselves add to the complexity of the system in part because they become
00:25:29.440
confusing and overwhelming at the moment. So you mentioned deep water. I mean, one of the things
00:25:35.600
on the, one of the problems on the rig that day was that they had an extremely detailed protocol for
00:25:43.740
how to deal with every single emergency that they could, that they could envision. But it was just,
00:25:49.840
it was, it was so detailed. It was, it was so specific, so rigid that it was just in the heat of the
00:25:55.920
moment. It was pretty much impossible for, for people to figure out what is it that they, what is
00:26:01.840
it that they should be, should be, should be doing. In hospitals, a huge problem that we see all the time
00:26:08.760
now is, is that there are all sorts of alarms and warning systems built into hospitals to, to warn
00:26:15.180
doctors and nurses about things that might be happening with patients or with, with medication doses
00:26:20.760
or, or, or, or what have you. And at some point people just start to, just start to tune out. If,
00:26:26.940
if you hear an alarm or if you get an alarm on your computer screen where you're ordering medication
00:26:32.240
every, every minute, or in some cases, every, every few seconds, at some point you just, you just have
00:26:38.700
to, you just have to ignore them. And then it becomes very hard to separate the signal from the noise.
00:26:45.060
And, and in our really kind of well-intentioned efforts to, to, to add warning systems, we, we often
00:26:52.540
end up just adding a ton of noise to these systems. And that was part of the issue that was going on
00:26:57.740
with the, with the airplane crashes we've had recently. I think Air France, there was issues
00:27:02.780
where, you know, alarms are going off and they were being ignored or even on the least recent Boeing
00:27:07.340
ones, there was, you know, sort of false positives of what was going on in the computer system
00:27:12.140
overrode and caused the crash. Yeah. So the, I mean the, the 737 max crashes that we've tragically
00:27:18.260
seen recently, I mean, are, are exactly this problem. It is Boeing, you know, very well-intentioned,
00:27:24.600
very thoughtful engineers at Boeing added a safety system to their airplane. And, you know,
00:27:31.880
that makes sense, right? To first order when you, when you just kind of think about it in isolation,
00:27:36.620
adding a safety system should make things safer, but they didn't properly account for the complexity
00:27:44.960
that that safety system introduced. And so what we see is we see they're now dealing with the,
00:27:51.160
the tragic and unintended consequences of that. So what can organizations, individuals do to cut
00:27:58.740
through all that complexity as much as they can to see, so they can figure out what they need to focus on
00:28:04.700
that can really make a difference in preventing meltdowns.
00:28:08.840
One of the things that we find to be extremely important is, is to make an effort. And I think
00:28:15.720
this applies to our individual lives as well as to, to running a big organization or a big system as
00:28:22.160
well is, is to make an effort to pick up on what we call weak signals of failure on, on kind of near
00:28:29.820
misses or, or, or, or just strange things that you, you see in your data or your experience in your
00:28:36.400
daily life as you are going around or running a system or trying to, to manage a team, something
00:28:43.500
that's unexplained anomalies and try to learn from that. This relates to complexity because
00:28:49.180
well, one of the hallmarks of, of, I think a highly complex system is that you can just sit down in
00:28:55.220
your armchair and imagine all the, the, the, the crazy unexpected ways in which it can fail. That's
00:29:01.760
just not possible almost by definition. What you can do on the other hand is you can, before these
00:29:09.080
little things all come together into one big error chain, often what you see is, is a part of that
00:29:14.640
error chain playing out, not all the way, but just a little part of it. So the system almost fails,
00:29:20.120
but doesn't fail or something strange starts to happen that could lead to something much worse.
00:29:25.600
If you catch it on time, you can learn from that. And, and, and, but I think what we see time and
00:29:31.940
time again is that we have this tendency to treat those incidents, these small weak signals of failure
00:29:38.700
as kind of confirmations that things are actually going pretty well, right? The system, the system
00:29:45.240
didn't fail. Maybe it got close to it. Maybe it was just starting to fail, but, but it eventually
00:29:50.960
didn't fail. So, so it's safe. So we have this temptation to conclude that while what, what we
00:29:56.980
argue is that you, you'll be in a much better position if you start to treat those incidents
00:30:01.660
and those observations as data. Yeah. I think it's an important point. The idea, don't focus on
00:30:06.760
outcomes, right? Cause everything can go right and be successful because of just dumb luck.
00:30:12.000
Right. But there's that one time when that, those little anomalies that popped up can actually,
00:30:17.620
I mean, that's the example, I guess, Columbia, the space shuttle Columbia is a perfect example of
00:30:21.060
this where the engineers knew that those, those tiles were coming off and it happened a lot. And,
00:30:27.360
you know, the shuttles were able to successfully land safely. So they figured, oh, it's just something
00:30:31.080
that happens. It's par for the course. But then that one time in 2003, I mean, it ended up
00:30:35.880
disintegrating the shuttle. Yeah, exactly. And I, well, you know, we, we see this in our own
00:30:41.500
lives too. I mean, I think my, like there's, there's sort of two examples that I love. I think
00:30:47.980
everybody who has a car has had the experience of the check engine light coming on and the response
00:30:53.440
being like, God, I hope that thing goes off. And then it goes off later and you're like, okay,
00:30:57.700
good. I don't have to worry about that anymore. And yet there is something that, that caused that
00:31:01.900
light to, to come on in the first place. And you're sort of kicking the can down the road a
00:31:06.160
little bit. And not that that's in, you know, with your car, not that that's necessarily a bad
00:31:11.020
strategy, but the more complex the system gets, the kind of the, the, the more, I guess the more
00:31:17.420
you're giving up by not attending to those kinds of things. You know, one example that actually happened
00:31:22.800
to me right before, right, right. As we were working on the book was, you know, I had a toilet in my
00:31:28.260
house that started running slowly and, you know, I just kind of dealt with it for a while. Like,
00:31:32.840
oh yeah, that toilet's running slowly. It always does that. Or, and then, you know, eventually it
00:31:37.400
clogged and it, and it, and it overflowed and, you know, was quite literally a shit show, but
00:31:42.300
that's because I didn't attend to that, that kind of weak cue that, that there's something here I
00:31:48.020
should pay attention to. And how do you overcome that tendency? Is there anything from social psychology
00:31:52.700
or psychology that we can, that we've, you know, gotten insights on how to overcome that tendency to
00:31:57.060
ignore small anomalies? Yeah, I think there's, I mean, there's one thing is you can just write them
00:32:01.020
down, right. Write them down and share them with other people. And the other thing is you, you've
00:32:05.300
got to be part of an organization sort of thinking about it more from a, like a team culture perspective
00:32:09.600
and a, and a sort of work perspective. You've got to be part of an organization that's willing to talk
00:32:14.760
about mistakes and errors because so many of, of the way that these things happen comes out of
00:32:20.980
mistakes and, and comes out of, you know, people who miss something maybe as a, as a function of the
00:32:26.860
system, but to learn from it, you, you've got to be willing to talk about it. So we're, we've been
00:32:32.540
working with, uh, doing consulting work with a company. And one of the things we're helping them
00:32:36.500
do is we're helping them sort of reimagine the way this is a pretty big tech company and we're helping
00:32:42.320
them reimagine the way that they learn from bugs that they've introduced in their software. They have
00:32:47.920
this, they have this postmortem process that they've been running for a long time and they have a good
00:32:52.900
culture around it. And we're just helping them figure out how to get the, push the learning out
00:32:58.060
further in the organization and kind of help them make sure it, it is reinforcing a culture of
00:33:03.080
blamelessness rather than being, you know, blaming the person who introduced a bug or brought down the
00:33:08.400
system or, or whatever it is. So I think pushing the learning out as much as possible and, and
00:33:13.060
reinforcing the culture of everybody's expected to make mistakes. The important thing is you raise your
00:33:18.020
hand and say, I messed this up and then everybody can learn from it. Well, I think you all highlight
00:33:22.340
organizations where people get rewarded for reporting their own mistakes. Like they'll get,
00:33:28.600
there'll be some sort of reward ceremony, like, Hey, this guy messed up and, but he figured out
00:33:33.000
something we can do with that, that mess up. Yeah. The, the, the company Etsy, who, you know,
00:33:38.220
many of us know is this kind of great place that sells these handmade crafts is a really super
00:33:43.300
sophisticated engineering organization behind it. You know, they have 300, 400 engineers that,
00:33:48.980
that are really doing cutting edge software stuff. And every year they give out a, what they call the
00:33:54.600
three arms sweater award, which is an award for the engineer who broke their website in the most
00:34:00.700
creative and unexpected way possible. So that's really an example of a culture kind of celebrating
00:34:06.680
these kinds of, these kinds of problems through, through the learning and, and saying, yes,
00:34:11.900
we know mistakes are going to happen. The important thing is we learn from them.
00:34:15.200
Okay. So you can pay attention to anomalies, even if they're small. So if the check engine light
00:34:19.020
comes on and goes off, take it to the dealership because there might be something bigger looming
00:34:23.640
there, create a culture where anomalies are, you know, people bring those up willingly. They don't
00:34:29.400
try to hide it. But then another part of, you know, preventing meltdowns is giving, getting an idea
00:34:34.900
of how likely it's going, they're going to occur. Because a lot of these meltdowns that happen,
00:34:39.860
people are just like, I just, we didn't see it coming. Like it's, I don't, I, we, we had no idea
00:34:44.200
this could happen. Is it even possible when, as systems get more and more complex to even predict,
00:34:51.280
like, are these all going to be black swan events, as Nassim Taleb says, or are there techniques we can
00:34:56.620
use where we can hone in a bit and get an idea of how likely some meltdown is going to occur?
00:35:03.460
Yeah. So I think that's a great question. I think, again, prediction from your armchair is not,
00:35:08.100
is not possible. But there are two things you can do. One is, as we just discussed,
00:35:12.820
you can start paying attention to these, to these signals that are emerging and treat those as data
00:35:17.560
rather than as confirmations that things are going just fine. And the other thing you can do is that
00:35:23.920
there are now a bunch of techniques that, that social psychologists and others have developed to,
00:35:29.600
to really try to get at the, the, the, the sort of negging concerns that people are not necessarily
00:35:37.120
bringing up, not necessarily articulating, maybe not even to themselves and certainly not to their
00:35:43.600
team and to their manager. And we talk about a bunch of these in the book, but I'll just highlight
00:35:48.260
one that, that we, we both really like. It's, it's the primordial technique, which essentially
00:35:54.100
entails imagining that your project or your team or, or whatever high stakes thing you might be
00:36:02.800
working on has failed miserably in, in six months from now or a year from now, and then getting
00:36:09.280
people in your team to, to sit down and kind of write a short little history of that failure.
00:36:16.000
And, and when you do this, you, you have to use a lot of past tense. You have to tell people,
00:36:20.820
Hey, this failure has happened. Our team has failed. Our project was a miserable failure without
00:36:26.940
giving them any specifics and then turning things around and asking them to, to give you their kind
00:36:32.640
of best set of reasons for, for why that might happen. And, and there is some fascinating research
00:36:38.660
behind this technique that shows that when you ask this question in this way, people come up with
00:36:44.760
much more specific reasons for, for why something might actually fail. They come up with a much broader
00:36:51.500
set of reasons for why that, that failure might come about. And, and it also helps a lot with the,
00:36:57.540
the social dynamics in a group. Often, you know, in an organization, we are rewarded for coming up with
00:37:03.940
solutions. Here now we are rewarding people, at least in this, at least by allowing them to be creative
00:37:10.140
and talk about these things for, for, for articulating reasons why a project or a team or, or, or,
00:37:18.340
or a business unit might fail. And, and it really has this interesting cognitive and kind of social
00:37:24.580
effect that really liberates people to, to talk about these issues. And it's been shown to be vastly
00:37:30.580
superior to the normal way that we usually do this, which is just purely brainstorming about risks,
00:37:37.500
right? We, we do that all the time when we are running a project, we sit down and we think,
00:37:41.520
okay, what could go wrong? What are the risks here? Let's think about that. It turns out that's not
00:37:46.780
the right way to do it. And, and, and there is a right way to, there is a right way to do it.
00:37:51.400
And as I was listening to this, I can see this being applied just on people's personal lives,
00:37:55.420
right? Bringing it back. If you're planning a vacation with your family, you're sitting down
00:37:59.140
with the missus and be like, all right, our vacation was a complete failure. Like, why was it a complete
00:38:04.400
failure? Right. Or totally. Or you're planning, or you're like, you're planning a class reunion,
00:38:08.680
right? And it's, there's all these moving parts. It's okay. My class reunion was a complete bust.
00:38:12.840
What happened? Totally. Yeah. Or say you are sitting down with your family and thinking about
00:38:19.100
a home renovation project and you know, it's going to take about two months and you say, okay,
00:38:23.960
now let's imagine it's three or four months from now. The project was a disaster. We never want to
00:38:29.660
see that contractor ever again. We, we really regret that we even started this whole thing.
00:38:34.580
Now everybody talk, write down, you know, what, what went wrong. Again, use this, this,
00:38:40.720
the past tense rather than asking what could go wrong. And, and, and yeah, and I think it would
00:38:46.600
really help people, people, Chris and I actually collected some data on this and it really seems
00:38:52.700
like people volunteer information that, that sort of, that sort of butters them, but they never really
00:39:01.060
had an opportunity to, to bring up. And I think using that to collect data about risks is one of
00:39:07.880
the, one of the most powerful predictive tools we have. And, and to your question, Brett, I think,
00:39:13.380
you know, the vacation one is a great example, right? It's not like the output of this, it's not
00:39:18.040
like we're not going to take the vacation, right? But maybe we are going to figure out like,
00:39:22.380
oh, like, you know, in this part of the trip, there's no activities for the kids to do. Like,
00:39:26.980
maybe we need to figure out like, is there a playground near where we're staying? Right. Or,
00:39:31.320
you know, is there, is there something fun for them to do? Or maybe we need to, you know,
00:39:36.020
whatever, make sure the iPad is charged or, or bring them, you know, bring a little like,
00:39:41.080
you know, nature kit so that they can go exploring with a magnifying glass or, you know, whatever it
00:39:46.020
is. It's like, the key is that this raises, this lets us raise issues, which we can then resolve in a
00:39:52.300
creative way. It's not like, well, we're not going to take the vacation or we're not going to have
00:39:55.860
a class reunion. So one of my favorite chapters in your book about how to mitigate the downsides
00:40:01.900
of complexity and tightly coupled systems is by increasing diversity on your team, your group,
00:40:08.820
and there's diversity of viewpoints. Studies have shown that outcomes are better, like more,
00:40:13.320
more creative solutions are developed, et cetera. And, you know, I've, I've read that literature.
00:40:18.020
I've had people on the podcast, you know, they talk about the same thing. And I asked them like,
00:40:21.560
what, why, what's going on? Like, why? And the answer they give is like, well, we don't,
00:40:24.980
it just does. Right. And like, I never understood it because like, I always thought, well, you can
00:40:29.740
have a diverse group of people, but they all give equally bad ideas. But I think your guys'
00:40:35.760
explanation of why diversity works is interesting because it makes counterintuitive sense. And it's
00:40:41.540
that diversity breeds distrust. And that actually helps us get better answers. Can you talk about
00:40:47.100
that a bit? Yeah, I think, you know, this is coming from some pretty recent research done in really
00:40:53.560
in the past sort of three to five years, where, where psychologists are increasingly discovering
00:40:58.940
this effect that in, in diverse groups, people tend to be a bit more skeptical. They tend to feel a
00:41:04.140
little less comfortable. And if you compare those groups to homogeneous groups, you know, there seems
00:41:10.580
to be this effect that when you're surrounded by people who look like you, you also tend to assume
00:41:16.700
that they think like you. And as a result, you don't work as hard to question them. You don't
00:41:22.880
work as hard to get them to explain their assumptions. And that just doesn't happen as
00:41:28.780
much in diverse teams. In diverse teams, people don't tend to give each other the benefit of the
00:41:33.600
doubt, at least in a cognitive sense, quite as much. So you mentioned distrust, and I think that's
00:41:38.800
sort of an interesting way to think about it. But I would say that it's distrust in a pretty
00:41:44.600
specific sense. It's not distrust in the, in a kind of interpersonal sense. It's not that I don't
00:41:50.840
trust you or that I don't trust Chris to do the right thing or to, to want to have the same good
00:41:56.160
outcomes that I want to have. It's more that I, I don't, I might be skeptical, more skeptical about
00:42:03.560
your interpretation of the world, or at least I have some skepticism as to whether you buy my
00:42:09.400
interpretation of the, of the world. And this of course creates some friction and, and, and friction
00:42:15.240
is, is, can be healthy in, in a, in a complex system. Of course, you don't want people to
00:42:20.740
disagree too easily. You want people to, to unearth assumptions and question each other.
00:42:27.160
While in a, you know, if you're running a very simple kind of operation and it's very execution
00:42:32.260
oriented, there's not a lot of strategic thinking. There's not a lot of, a lot of these
00:42:36.600
black swan type of events you need to worry about, you know, diversity is, is probably not
00:42:41.240
particularly important, at least from a, a kind of effectiveness point of view. But if you're
00:42:46.200
running something that's complex and then you really can't afford people to fall in line too
00:42:53.000
easily, and you really want some level of dissent, what data show across types of diversity, whether
00:42:59.780
it's race or gender, or even something like professional background is, is, is that, is that diversity
00:43:06.200
tends to, tends to make a very positive difference in, in, in those systems if it's managed well.
00:43:12.940
And yeah, like, like you said, the diversity doesn't have to be based on race or gender
00:43:16.260
or whatever. It can be profession. You give the example of the Challenger explosion. All the
00:43:22.500
engineers thought it, you know, nothing was a problem with the O-rings, right? Which would
00:43:25.720
end up causing the, the failure. But there was some guy, some accountant basically, who saw
00:43:31.620
that there was an issue and he got ignored because all the engineers are like, well, you're not an
00:43:37.140
engineer like us. You're not wearing the white shirt and tie and got the slide ruler. Go back
00:43:42.660
to wherever you are and ended up being the O-ring causing the problem. Yeah. And we, we see the
00:43:48.440
same kind of effect in, in all kinds of, uh, in all kinds of areas. One, one, um, set of results
00:43:54.680
we write about in a book is, is about small banks in the U S community banks, right? Your credit
00:43:59.840
unions, your, your local banks. And we look at their boards and it turns out that the, when these
00:44:05.440
banks have boards that have a good amount of professional diversity, so not just bankers,
00:44:10.620
but also lawyers and journalists and doctors and sort of people from the community, there tends
00:44:16.720
to be a lot more skepticism and disagreement on those boards, uh, which doesn't seem to make
00:44:24.340
a big difference. If these banks are doing very simple, straightforward things, just running a
00:44:29.820
couple of branches in, in, in a small town, but once they start to do more interesting, bigger,
00:44:35.460
more complex things like expanding out of a, out of a County or getting engaged in, in sort of more
00:44:42.300
complex high stakes lending markets, then all of a sudden having that kind of disagreement and
00:44:48.500
skepticism and diversity on the board becomes really, really helpful. And it turns out that it,
00:44:54.340
it, it measurably increases the chances that those banks will, will survive. And even, even
00:45:00.740
doing something like the financial crisis. And how do you manage that skepticism so that it's
00:45:05.260
productive and not, it doesn't get in the way of getting things done? So, so I think to, to a large
00:45:11.000
extent, it's, it's a leadership challenge, sort of how you, you, you run that group. And, and I think
00:45:16.760
instilling a culture where people understand that there's a distinction between interpersonal
00:45:22.340
conflict and, and, and task conflict. So conflict about, you know, I, I don't like, you know, the
00:45:29.020
way Chris dresses and drinks his coffee and all that kind of stuff. I drink my coffee just fine.
00:45:35.260
Thank you. I think your coffee is just fine versus task conflict, which is, you know, I, I disagree
00:45:41.200
with Chris's interpretation of this particular event and, and sort of treating the first thing,
00:45:46.660
the, the interpersonal stuff as something that we need to hammer out and get rid of, but making sure
00:45:52.980
that our team and organizational culture treats the, the second thing, the task conflict as something
00:45:58.560
that we need to celebrate, right? It's great that we disagree because it's data, because we have these
00:46:04.500
different perspectives and these different little data points about this particular thing.
00:46:09.220
And that forces us to state our assumptions. And, and I think as a, as a manager or a director on one
00:46:16.760
of these boards or, or, or, you know, even, even at home, if you are kind of thinking about this in,
00:46:21.900
in a family context, managing disagreement in, in this way is, is really critical and, and making sure
00:46:29.240
that the, that the, the task conflict, this sort of cognitive conflict that we do want to have
00:46:34.260
doesn't turn into interpersonal conflict, but rather gets treated basically as data about this
00:46:40.520
complex system that we have to navigate is, is, is really the, is really the way to go.
00:46:45.460
And then I'll, I'll, I'll add something to that. Cause when we work with leaders, you know,
00:46:49.720
when, especially when we work with senior leaders, I mean, that there's always a tension between,
00:46:54.660
right. I mean, senior leaders got to be senior leaders because they were, they were, you know,
00:46:59.440
good at making decisions, good at their, their judgment, but there becomes a time, particularly
00:47:04.560
in a complex system where they have to start to delegate more and otherwise they will be
00:47:10.640
overwhelmed by the amount of data that they have to process. They will be overwhelmed by the,
00:47:14.880
and they will not be able to predict the interactions between different parts of the
00:47:19.080
system, right. That that's only something that somebody who is working lower down, who is,
00:47:23.660
if they're a programmer programming every day, if they're an engineer, you know,
00:47:27.120
working directly on the engineering problem, only they will be able to, to kind of make the right
00:47:32.800
call. And so I actually think this ties into this, this decision-making and diversity question
00:47:37.960
in a, in a real tangible way. One of the things we help leaders do is sort of set up the kind of
00:47:45.760
output parameters of like, okay, what risks are they comfortable with their teams taking? What
00:47:51.560
mistakes are they comfortable with people making? And then any decision that seems like it's
00:47:56.960
kind of in, you know, in that ring fence really should be pushed down as low in the chain as
00:48:03.040
possible. And anything that, any disagreement that seems that can't be resolved at a lower level
00:48:08.840
should get escalated. But one of the things that does is that means that you don't have a leader who
00:48:14.380
is kind of stopping the decision-making process when people actually have enough information. And even
00:48:20.740
when people make a decision to make a mistake, you have a process in place that says, okay, well,
00:48:25.940
you know, this isn't the call I would have made. Here's why my thinking is different in this case,
00:48:30.740
but because you've kind of sort of put a ring around the types of risks that you're willing
00:48:36.140
to let people take, you can end up with having really effective decision-making that doesn't put
00:48:40.800
the firm or doesn't put, you know, different parts of reputation or your reputation or a big
00:48:45.640
project at risk. And so I think that part of the question about decision-making, diversity just adds
00:48:51.180
data, but many organizations struggle with how to make better decisions faster. And that comes from
00:48:57.440
thinking about things a little more systematically from the get-go.
00:49:02.380
I'm bringing this back to how people can apply this to their just home life, the personal life.
00:49:06.640
I imagine if you're trying to make a decision that where there's a lot of complexity involved,
00:49:10.900
like those really tough decisions that people encounter every now and then with their life,
00:49:15.120
talk to a whole variety of people, like have like a sounding board you can go to,
00:49:19.880
not just your friends who are going to say, oh, you know, you're great. Whatever you want to do is
00:49:23.860
fine. Like actually look for those people who are going to tell you what's what and give you
00:49:28.900
contradictory feedback and have them explain their thinking.
00:49:33.120
Yeah, totally. And I think one of the ways to do it too is to just structure your question
00:49:38.940
to your friends very carefully. You know, you can ask them in the form of a premortem. You can say,
00:49:44.840
hey, I want your help thinking through whether or not I should take this new job, whether or not,
00:49:48.540
you know, I should move with my family to St. Louis or whatever it is. Like, let's imagine we do this
00:49:54.580
and it turns out, you know, six months from now, I'm calling you and telling you this is a total
00:49:59.080
disaster. You know, what are the things that led to that? So you can really use that technique to
00:50:03.720
to create skeptics in your in your community and create skeptics in your network. And as you said,
00:50:09.360
go to outsiders, go to people who are going to say, no, this isn't the right decision. And here's
00:50:13.420
why that can be really helpful. Another top and sort of bias that we have that can cause us problems
00:50:19.300
with complex, tightly coupled systems is idea of get their itis. This is what you guys call it.
00:50:25.140
What is that? And how does that contribute to disasters?
00:50:29.420
Yeah, get their itis. We're not the ones who came up with that name. It's actually like,
00:50:33.340
I feel like an official term in the in the aviation accident literature. It is basically
00:50:39.120
this phenomenon that happens to really, you know, highly trained pilots, as they get closer and
00:50:44.600
closer to their destination, they get more and more fixated on, you know, landing at the intended
00:50:51.000
point of landing. And so they start to take information that sort of suggests that maybe
00:50:56.840
there's a problem or maybe they should do something else and they start to discard it. And this is kind of
00:51:01.740
this natural human tendency that we all have, you know, we look for data that that confirms our
00:51:06.780
opinion rather than what we should be doing, which is look for data that that sort of disagrees with our
00:51:11.660
opinion. But the get their itis, you know, it happens when flight crews are, you know, flying to their
00:51:19.100
destination airport. But it also happens to all of us, you know, when we're working on a project, right? So the
00:51:26.680
kind of once a project has started, boy, is it hard to take a step back and say, like, actually,
00:51:32.200
things have changed. This is no longer, you know, the right decision. This is no longer the right
00:51:37.820
way to run our marketing campaign. Or this is no longer, you know, the right piece of technology to
00:51:43.640
include in this car. Or this is no longer, you know, we don't even think there's a market for this
00:51:49.300
craft beer that we're brewing anymore. People tend to, as the kind of pressure gets ramped up,
00:51:55.300
people tend to just want to do more of the same thing and want to push forward more and more and
00:52:00.960
more. And sometimes that works out. But and that kind of helps make it even more dangerous. But when
00:52:08.580
it doesn't work out, it can really blow up in our faces and in really big ways from, you know, from
00:52:13.620
actual airplane crashes to things like you can look at a target's expansion into Canada, which kind of
00:52:20.360
ended in, you know, thousands of people being laid off and I think seven billion dollars being written
00:52:26.140
down from from target. So that that get their itis is really a part of pushing forward when we should be
00:52:33.640
It sounds like there's some sunk cost fallacy going on there.
00:52:36.260
A lot of ego protection, you know, people like, well, I started this thing. I'm a smart person. So when I
00:52:42.220
decided it must have been a good idea. If I say I'm not doing anymore, it means I'm a dummy. That's going to
00:52:47.240
look bad. Someone's going to keep going. Yeah, totally. And I think that there's this,
00:52:51.380
you know, there's so much there. And there's a great story from I think it's in one of Andy
00:52:56.160
Grove's books when he was at Intel and they were sort of in the process of looking at the way that
00:53:03.060
the market had really shifted around them. And he and the CEO at the time, I think, sort of asked
00:53:09.280
themselves this question, you know, what decision would we make if we had just been fired and then they
00:53:15.080
brought you and I back in from the outside and we were looking at this with fresh eyes, what decision
00:53:20.160
we would make. And so being able to kind of shock yourself out of that sunk cost fallacy, being able
00:53:26.340
to shock yourself out of that status quo bias is huge. And you mentioned ego. And I think one of the
00:53:33.120
things that the research shows and we see over and over again in our work is that the most effective
00:53:39.320
organizations are the ones that start with a theory that they want to test and then test that
00:53:45.780
theory and use that to kind of build what they're going to do rather than starting with a theory that
00:53:52.440
is kind of an implicit theory, but that turns into this is what we're going to do without really having
00:54:00.800
Well, Andras, Chris, this has been a great conversation. Where can people go to learn more
00:54:04.780
So you can find us at a bunch of places. We have a book website, rethinkrisk.net, which also has a
00:54:09.880
short two, three-minute quiz that you can take to find out if you are heading for a meltdown in one
00:54:16.240
of your projects or your situations. Twitter for our book is at rethinkrisk. I'm on Twitter at
00:54:22.700
Chris Clearfield. And then my personal website is chrisclearfield.com.
00:54:27.220
Well, guys, thanks for your time. It's been a pleasure.
00:54:30.860
My guests, they were Chris Clearfield and Andras Tilchuk. They're the author of the book,
00:54:34.020
Meltdown, Why Our Systems Fail and What We Can Do About It. It's available on amazon.com and
00:54:38.340
bookstores everywhere. You can find out more information about their work at rethinkrisk.net.
00:54:42.680
Also, check out our show notes at aom.is slash meltdown, where you can find links to resources,
00:54:56.360
Well, that wraps up another edition of the AOM podcast. Check out our website at
00:54:59.840
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00:55:04.160
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