The Art of Manliness - #517： What Big-Time Catastrophes Can Teach Us About How to Improve the Systems of Our Lives

00:00:00.000 Brett McKay here and welcome to another edition of the Art of Manliness podcast.

00:00:11.220 Whenever a financial or technological disaster takes place, people wonder if it could have

00:00:15.240 possibly been averted. I guess they say that the answer is often yes, and that the lessons

00:00:19.340 around why big disasters happen can teach us something about preventing catastrophes

00:00:23.020 in our businesses and in our personal lives. Their names are Chris Clearfield and Andras

00:00:26.460 Tilcek. They're the authors of Meltdown, Why Our Systems Fail and What We Can Do About

00:00:30.440 It. We begin our discussion getting into how they got interested in exploring how everything

00:00:33.980 from plane crashes to nuclear meltdowns to flash stock market crashes actually share common

00:00:38.600 causes. We then discuss the difference between complicated and complex systems, why complex

00:00:42.920 systems have weaknesses that make them vulnerable to failure, and how such complexity is on the

00:00:46.720 rise in our modern technological era. Along the way, Chris and Andras provide examples

00:00:50.680 of complex systems that have crashed and burned, from the Three Mile Island nuclear reactor meltdown

00:00:55.060 to a Starbucks social media campaign gone awry. We enter a conversation digging into specific

00:00:59.240 tactics engineers and organizations use to create stronger, more catastrophe-proof systems,

00:01:03.580 and how regular folks can use these insights to help make their lives run a bit more smoothly.

00:01:07.960 After the show's over, check out our show notes at aom.is slash meltdown.

00:01:12.180 Chris and Andras, join me now by Skype.

00:01:13.960 Chris Clearfield, Andras Tiltchik, welcome to the show.

00:01:25.720 Thank you so much.

00:01:26.740 Thanks.

00:01:27.320 So you two co-authored a book, Meltdown, what plane crashes, oil spills, and dumb business decisions

00:01:33.280 can teach us about how to succeed at work and at home. And I was telling you all before we started,

00:01:38.980 I was actually, I read this book while I was on an airplane, reading about catastrophic plane

00:01:45.240 crashes, which made me feel great. I'm curious, how did you two start working together on this

00:01:50.860 idea of exploring catastrophic failures and what they can teach us about how to be successful in

00:01:56.700 life?

00:01:57.380 Well, I'll tell maybe kind of my half of how I came to this work, and then Andras, you can maybe pick

00:02:02.280 up with sort of how we came together with it. I studied physics and biochemistry as an undergrad,

00:02:08.440 so I was always kind of a science geek. But then I got intercepted and ended up working on Wall

00:02:14.080 Street, and I ended up working there kind of during the heart of the 2007-2008 financial crisis.

00:02:20.800 And so all around me, the whole system of finance was struggling and collapsing. And

00:02:26.300 I had this observation that, oh, I think that Bank X is going to do better than Bank Y in this

00:02:32.840 process. And then I kind of took a step back and thought, well, that's really interesting.

00:02:37.020 I don't work at either of these places. How could I possibly say something about the ability of these

00:02:43.040 firms to manage risk just as an outsider, just based on kind of a really bare understanding of

00:02:48.800 their culture? And so that made me just really interested in this question, sort of how organizations

00:02:55.440 learn to manage failure and manage complexity. And at the same time, I was actually learning to fly

00:03:03.600 airplanes. I'm a pilot. I'm an instructor now. But at the time, I was just starting to learn.

00:03:08.560 And I was kind of reading obsessively about why pilots crash planes, like why well-trained flight

00:03:15.140 crews make mistakes that mean that they make bad decisions or they have these errors in judgment that

00:03:21.040 caused them to, despite their best efforts, to kind of crash and do the wrong thing.

00:03:26.260 And that to me was also the same kind of thing. It's like the culture of a team mattered and the

00:03:31.860 culture of an airline or the kind of way people approach these challenging situations really

00:03:37.320 mattered. And then the third piece of the puzzle that made me realize what a widespread issue this

00:03:42.340 was, was when BP's Deepwater Horizon oil rig exploded in the Gulf of Mexico. And just because I was

00:03:49.220 interested, I started reading about that accident and realized that there was this kind of same set of

00:03:53.860 sort of system and organizational factors at play in that accident. It was a complex system with a

00:04:01.640 lot of different aspects to it, a lot of different things that had to be done correctly. And there

00:04:06.260 wasn't a great culture to support that. And so that made me realize what a widespread problem it was.

00:04:12.420 But I was still thinking about it mostly from the systems angle. And then Andras and I came together

00:04:18.340 at some point and started thinking kind of more broadly about it together.

00:04:22.120 Yeah. And I came from a social science angle. And so I was more interested in the organizational

00:04:27.960 aspects of this. But in some ways, we started to converge around this idea that, you know, if you

00:04:33.780 look at these different failures, the financial crisis, Deepwater Horizon, a plane crash, if you read

00:04:40.780 enough of the of the literature and the accident reports on these, you start to see some of the same

00:04:46.640 themes come up over and over again. And I thought that was that was fascinating that there is a sort of dark

00:04:53.440 side to organizations, but it's not unique to each failure, there's some kind of fundamentals. And in some

00:05:00.380 ways, we started to see that over time as this very positive message potentially in that, while it implies

00:05:07.760 that we are making the same dumb mistakes over and over again, across different industries and in different life

00:05:14.080 situations, if it's the same things that get us into trouble across these situations, it also means

00:05:19.980 that if we figure out a solution in some fields, we can try to apply those in other fields, and we can all

00:05:25.840 learn from other people from other industries. And so that was, it wasn't just gloom and doom. And I think

00:05:32.860 that that really helped move this whole project forward.

00:05:36.220 Well, let's talk about, I think it's interesting, you guys highlight this, the study, the scientific

00:05:41.740 study of catastrophe is relatively new. And it started with this guy named Charles Perrault. How do you say

00:05:48.700 his last name? Perrault? Yeah, Perrault. Perrault. Andres, I think this would be your in your wheelhouse

00:05:53.560 since he was a social psychologist. What was his contribution? What did what was his insight about

00:06:00.180 catastrophic failures and like why they occur on an organizational level? Yeah, so Charles Perrault or

00:06:06.600 Chick Perrault, as people more affectionately know him, he's actually a sociologist, fascinating

00:06:11.680 character. He's in his early 90s. Now, he got into this whole field back in the in the late 70s, in the

00:06:17.980 wake of the Three Mile Island partial meltdown. And, you know, his whole entry into this field was just

00:06:23.900 just so interesting to Chris and me when we were studying it. He really, you know, for an outsider,

00:06:29.960 he was studying very obscure, organizational, sociological topics. And then he ends up looking

00:06:36.680 at Three Mile Island from this very organizational, sociological, social scientific perspective,

00:06:42.340 rather than the standard kind of hardcore engineering perspective. And in that process,

00:06:48.180 he develops this big insight that, you know, one of the most fascinating things about Three Mile

00:06:54.200 Island is that, you know, it's this big meltdown. And yet, the causes of it are just so trivial,

00:07:01.420 or, you know, it's really a combination of a bunch of small things, kind of like a plumbing error

00:07:06.700 combined with some human oversight or problems with kind of human attention, and sort of nothing out of

00:07:14.220 the ordinary. It's not like a big earthquake or a terrorist attack. And those those little things

00:07:18.680 instead just combine and bring down this whole system. And that really inspired Perrault at the time

00:07:23.360 to kind of figure out what are the systems where we would see these types of failures,

00:07:29.300 these types of meltdowns. And that's when he develops this interesting typology of systems,

00:07:35.340 which really has two parts. One is complexity, the other is tight coupling.

00:07:40.420 But complexity just means that the system has a lot of elaborately connected parts.

00:07:45.560 So it's like an intricate web, less like a linear assembly line type of process. And it's not a

00:07:51.580 very visible system, you can just, you know, you can just send a guy to the nuclear core and tell

00:07:56.400 them, hey, take a look, look at what's going on in there and come back, report back to me,

00:08:00.640 you have to instead rely on indirect indicators. And the other part of the of this typology that he

00:08:07.320 comes up with is is what he calls tight coupling, which really just means the lack of slack in a

00:08:12.140 system that the system is unforgiving, it doesn't really have a lot of margin for error. And he says

00:08:17.300 that when when a system has both of those components, then it's especially ripe for these

00:08:22.860 types of big surprising meltdowns that come from not from a big external shock, but from little things

00:08:30.740 coming together inside the system in surprising ways. And and he says that's because complexity tends

00:08:36.720 to confuse us, it tends to produce symptoms in a system that are hard to understand how to diagnosis

00:08:42.480 as they are happening. And if the system also doesn't have a lot of slack in it, if it's tightly

00:08:47.620 coupled, if it's unforgiving, then then it's just going to run away with it, it's just going to run

00:08:53.260 away with us, we will get confused, but it will be and we'll be surprised. And it's going to be a

00:08:58.460 particularly nasty kind of surprise, you can't easily control it. And in some ways, you know, what we do in

00:09:04.820 this book is, is that we build on pearls inside that came from this, you know, one particular

00:09:11.280 accident at Three Mile Island. And we try to extend and apply that insight to modern systems,

00:09:18.060 basically 40 years later. And we still see a lot of resonance of that very simple, yet deeply

00:09:24.600 insightful framework. Let's take this idea of complexity, because you guys highlight in the book

00:09:30.060 that something can be complicated, but not complex. What's the difference between the two

00:09:36.020 things? You know, I think that the complexity is a very specific way of thinking about the amount of

00:09:44.880 connectedness in the system, the amount of kind of the way that the parts come together. So a complicated

00:09:53.540 system might be a system that has lots of different pieces or has lots of moving parts,

00:09:58.400 like the kind of hardware in our phone. But most of those things, they interact in very stable and

00:10:06.420 predictable ways. Whereas a complex system, it has these interactions that are sort of important

00:10:14.300 either by design or important kind of by accident. You know, the Three Mile Island example is just one

00:10:22.400 example of this, but where you have these kind of parts of the system that end up being

00:10:26.920 physically or kind of informationally close to each other that's not intended by the designers.

00:10:35.140 Delta, for example, had a fire in a data center, which brought down their whole fleet of planes.

00:10:41.340 I don't mean made crash, but I mean, you know, grounded their whole fleet of planes. They couldn't

00:10:45.400 dispatch new flights. And so you have this really unintentional connection between these different

00:10:51.100 parts of the system that come from the way it's built, that come from the complexity. So I think

00:10:58.860 complexity refers to that, the kind of the connections being more important than the components themselves.

00:11:05.520 Okay. So like a complicated system or a complicated system would be like the Rube Goldberg machine,

00:11:10.620 right? Where there's like different little parts, but it all works in a linear fashion.

00:11:14.940 And if one part's messed up, like you can figure out, you can replace that part and it'll keep going.

00:11:21.180 You can keep things moving. With a complex system, it would be something like, there's like a network

00:11:26.020 effect, right? Where parts that you don't think are connected linearly start interacting and it affects

00:11:30.980 the whole system. Exactly. That's a great way to put it. Okay. You should come with us on other

00:11:36.160 podcasts so you can help us with that. All right. So that's complexity. And so the other issue,

00:11:44.720 the other part of what causes catastrophic meltdowns is the tightly coupled. So what would be an

00:11:51.100 example of a tightly coupled system, right? Where there's very little slack.

00:11:56.120 I think one neat way to think about it is just take one system and sort of think about when it is more

00:12:02.380 or less tightly coupled. So take an airplane and you're at the airport, you're just leaving the gate.

00:12:09.080 It's a relatively loosely coupled system at that point in the sense that there's a lot of slack in

00:12:15.740 it. There's a lot of buffer. There's a lot of time to address problems and figure out what's going on

00:12:21.780 if something bad or concerning happens, right? So say you are the pilot, you notice something going on,

00:12:28.960 you can go back to the gate, you can bring in other people, you can have a repair crew,

00:12:33.240 you can go out, have a cup of coffee, think about what's happening. Once you are in the air,

00:12:39.760 it's a lot more tightly coupled. And once you are, say, over the Atlantic, very far from any other

00:12:47.340 alternative option, such as another airport where you could land, it's extremely tightly coupled.

00:12:53.540 There's no time to think, there's no time to get new parts, there's no other resources you can

00:13:00.240 lean on and you can just say, hey, I'm going to, this is a very complex, confusing situation,

00:13:05.560 I'm going to step outside, have my coffee, come back after I had some fresh air, and then solve it.

00:13:13.060 Things just happen fast in real time and you just have to work with what you got.

00:13:19.220 And I got another example I think you give in the book, sort of like just a run-of-the-mill

00:13:23.200 tightly coupled system is like if you're cooking a meal, right? And you have to time things just right

00:13:28.980 so that the meal is all done at the same time. And if you miss one thing up, then like you're going

00:13:34.060 to be 20, 30 minutes late with, you know, serving your meal.

00:13:37.840 Right. Yeah, that's a great one. And the other example that I like to think about that, you know,

00:13:43.960 just crops up in my personal life when I have to travel for work is, you know, what does my flight

00:13:50.960 look like? Like, do I have a direct flight from, you know, Seattle to San Francisco? That's pretty simple.

00:13:57.320 And, you know, there might be problems in the network, but by and large, if I show up for

00:14:01.980 that flight on time, I'm going to get on the plane, it's going to fly to San Francisco and

00:14:05.400 I'm going to be fine. But if I'm going to, you know, like if I'm going to someplace on the East

00:14:10.380 coast from Seattle where I live, then do I have to change planes in Chicago? Well, that adds a bit

00:14:15.820 of complexity. Now there's kind of more parts of my trip where things can go wrong. And, you know,

00:14:21.700 if I have two hours to change planes, well, that's kind of a, that's sort of a buffer.

00:14:26.140 That's relatively loosely coupled. If I have four hours, it's super loosely coupled. But if I have

00:14:30.640 45 minutes, suddenly I'm in a really tightly coupled system where any delay caused by complexity

00:14:36.140 is likely to be very disruptive to my whole trip, be very disruptive to my whole system.

00:14:42.120 How has digital technology and the internet added more complexity and more coupling to our systems?

00:14:49.760 There's so many good examples of this. And, and I think this is one of the fascinating things for

00:14:54.260 us about working on this book that when we started, we really expected to be working on,

00:14:59.520 you know, the big, like writing about big disasters, writing about Deepwater Horizon and,

00:15:04.640 and, and, you know, Three Mile Island. And we, we definitely write about those things,

00:15:08.640 but what struck us was how many more systems have moved into, into Perot's danger zone that are

00:15:16.040 complex and tightly coupled. And there's so many examples, but you know, one of the ones that I

00:15:21.220 love is our cars these days. So I have a 2016 Subaru Outback. This is not, I mean, it's a, it's a

00:15:30.220 lovely car, but it's not a fancy car. And yet it, it has the ability to look up stock prices on the

00:15:37.380 infotainment system in the dashboard. And so, you know, suddenly you've taken this,

00:15:42.400 this piece of technology that used to be relatively simple and relatively standalone. And now many,

00:15:50.080 many parts of the car are controlled by computers themselves. And that has very good reasons. That's

00:15:54.620 to, you know, add features and, and increase the efficiency of how the engine works and, and all of

00:15:59.360 this good stuff. But now suddenly manufacturers are connecting these cars to the internet and they

00:16:04.400 are part of this global system. And there's this fascinating story in the book that was covered

00:16:10.420 originally by a Wired reporter called Andy Greenberg. And it is about these hackers that

00:16:15.120 figure out that they can through the cellular network, hack into and remotely control Jeep

00:16:21.540 Cherokees. And so they, they've done all this research. And fortunately, the guys who did that

00:16:26.340 were, you know, white hat hackers. They were hackers kind of whose role was to find these security

00:16:31.120 flaws and help manufacturers fix them. But that didn't necessarily have to be the case. So you suddenly

00:16:36.480 have this example of complexity and, and tight coupling coming because manufacturers are now

00:16:42.780 connecting cars to the internet. I mean, Tesla's can be remotely updated overnight while it's in

00:16:48.660 your garage. And, you know, that adds great capabilities, but it also adds this real ability

00:16:54.180 for something to go wrong, either by accident or, or nefariously.

00:16:59.260 But you also give other examples of the internet creating more complexity and sort of benign,

00:17:03.420 well, not benign ways, but ways you typically don't think of. So you gave the example of

00:17:07.560 Starbucks doing this whole Twitter campaign that had a social media aspect to it and it 0.99

00:17:13.580 completely backfired and bit them in the butt. 0.98

00:17:16.160 Yeah, totally. I think that, you know, that was one of the early examples that actually caught 0.99

00:17:19.920 our attention. This is a Starbucks marketing campaign, a global marketing campaign around the,

00:17:26.540 around the holiday season where they created this hashtag, hashtag spread the cheer.

00:17:30.420 And they wanted people to post photos and warm and fuzzy holiday messages with that hashtag.

00:17:36.080 And then they would post those messages on their, on their social media, as well as in some physical

00:17:41.920 locations on, in, on large screens. And in some ways they created a very complex system,

00:17:49.120 right? There were all these potential participants who could, who could use the, the, the hashtag.

00:17:55.220 They would be retweeting what they were saying. And then they were, there was another part of the

00:18:00.460 system, which was also connected is that the, the Starbucks tweets with the hashtag would be

00:18:05.040 these retweeted tweets and individual tweets would be appearing in the physical locations.

00:18:10.680 And, and it was an, it was an ice skating rink, right? They showed up at a screen at an ice skating

00:18:15.080 rink in, in London.

00:18:16.880 Yeah. So, you know, very prominent location. There's a great Starbucks store there.

00:18:21.360 Lots of people are watching it and, and they start seeing these messages come up that are,

00:18:27.040 that are very positive initially. And people are tweeting about their favorite lattes and their

00:18:32.900 gingerbread cookies and things like that. And then all of a sudden it turns out that some people

00:18:37.960 decide to, to, to essentially hijack the campaign and start posting negative messages about Starbucks,

00:18:45.220 critical messages about their labor practices, about their kind of tax avoidance scandals,

00:18:51.160 that they were caught up in at the time, especially in the, in the, in the UK.

00:18:55.760 And Starbucks of course thought about this possibility. They had a moderation filter put

00:19:00.700 in place, but the moderation filter didn't function for, for a little while and talk about a tightly

00:19:06.880 coupled system, right? Once the genie was out of the bottle, people, when, once people realize

00:19:12.020 that anything they say with that hashtag will be shared both online and, and on this,

00:19:18.440 in these physical locations, then it was, it was impossible to put the, put the genie back into

00:19:23.660 the bottle. And, and, and again, even after they, they fixed the moderation filter, it was just,

00:19:29.300 it was just too late. It was by that point, the, the, the hashtag was trending with all these negative

00:19:35.040 messages. Then traditional media started to pay attention to this funny thing happening on social

00:19:40.700 media. So there was another layer to that. Social media is connected to traditional media,

00:19:45.940 which then fed back into people tweeting, uh, and Facebook sharing more and more about this.

00:19:51.620 So very quickly, essentially a small oversight and, and a little glitch in the system that,

00:19:58.700 that only lasted for a relatively short period of time, turning to this, this big, embarrassing PR

00:20:05.380 fiasco that you couldn't just undo that, that, that, that was just spiraling, spiraling out of control.

00:20:11.480 All right. So the complexity was the social network. People act in unexpected ways. You can't predict

00:20:15.920 that. And the tight coupling was, it was happening in a real time. There's nothing you can do about it.

00:20:20.400 Really. We're going to take a quick break for your word from our sponsors. And now back to the show.

00:20:26.040 Well, you also give examples of how technology has increased complexity and tight coupling in our

00:20:31.960 economy. And you give examples of these flash crashes that happened in the stock market where,

00:20:36.500 you know, the, basically there'll be like this giant crash of billions and billions of dollars.

00:20:41.060 And then, you know, a second later, a minute later, it goes back to normal.

00:20:44.140 Yeah. It's totally, it's totally fascinating. And I think what's fascinating about it is there are all

00:20:49.720 these different, I mean, it's very, very similar to social media in a way. There are all these

00:20:53.340 different participants looking to see what everybody else is doing and kind of using the output of other

00:20:59.320 people as, as the input to their own models and their own ways of behaviors. And so you sometimes get

00:21:05.120 these, these kind of critical events where the, you know, the whole system starts moving in lockstep

00:21:10.780 together and, and moves down very quickly and then kind of bounces back. And what, I mean, one of the

00:21:16.840 things that I think is interesting about it is that regulators and, and policymakers, they sort of,

00:21:23.540 their mental model, generally speaking of, of the stock market is it's kind of like it used to be just

00:21:28.900 faster. In other words, like, you know, it used to be that you had a bunch of guys on the floor of the

00:21:33.680 New York Stock Exchange, sort of shouting at each other. And, and that's how trading happened. And

00:21:38.360 the sort of basic mental model, and it's changing a little bit, but the basic mental model of, of

00:21:43.260 regulators has been like, well, that's still what's happening, except computers are doing the kind of

00:21:47.520 interaction and, and therefore everything is faster. But, but really the truth is that it's a

00:21:52.980 totally different system. It's like the character of, of finance has totally changed. You also saw that

00:21:59.100 in the, in the financial crisis in 07, 08, the way that the, the decisions that one bank or

00:22:06.200 institution made because of all the inner linkages in finance, the way that those decisions really

00:22:12.000 cascaded throughout the system. So if, if one participant, you know, started looking shaky,

00:22:18.160 then the next question was, well, who, who has exposure to these people and, and kind of,

00:22:22.600 how is that going to propagate through, through this system? You know, so that was the big,

00:22:26.680 the big worry with AIG that it wasn't so much the, the failure of AIG that was critical.

00:22:32.640 That was hugely important. It was all of the people that had dependent on AIG to kind of,

00:22:38.300 not only in the sort of insurance as we think about it traditionally, but all the financial

00:22:42.260 institutions that had made these big trades with AIG. It did totally fascinating stuff.

00:22:47.380 And it sounds like things are just going to get more complex as, you know, the whole thing

00:22:52.340 people are talking about now is the introduction of 5G, which would allow us to connect cars,

00:22:56.380 health devices, connect homes with like smart things, like things are going to just get more

00:23:02.340 and more complex. Yeah. And I think that kind of gets to, gets to the fundamental,

00:23:08.080 I think the fundamental message of the book, which, which for us, from our perspective is that

00:23:12.320 this complexity, we can't turn back the clock, right? I mean, I, I, I do tend to be skeptical of

00:23:18.940 the cost and benefits of, of some of these trade-offs, like my car being, you know, internet

00:23:24.260 connected, but the truth is these systems in general, our world in general is going to get

00:23:29.660 more complex and more tightly coupled. The thing that we do have some control over is the things

00:23:35.820 we can do to manage this, the things we can do to operate better in these systems. And, and I think

00:23:40.840 for, for us, the key question was at the, at the heart of the book that we try to answer is, you know,

00:23:47.060 why can some organizations build teams that can thrive in these complex and uncertain environments

00:23:52.820 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. 0.86

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 0.97

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 0.99

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 0.57

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 1.00

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 0.97

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

00:52:32.000 able to take a step back.

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

00:53:57.420 any great data on how much that works.

00:54:00.800 Well, Andras, Chris, this has been a great conversation. Where can people go to learn more

00:54:03.620 about the book and your work?

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.

00:54:28.960 Brad, this has been awesome. Thank you.

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,

00:54:47.680 where you can delve deeper into this topic.

00:54:56.360 Well, that wraps up another edition of the AOM podcast. Check out our website at

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00:55:04.160 as well as thousands of articles we've written over the years about personal finances, health and

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The Art of Manliness - June 17, 2019

#517： What Big-Time Catastrophes Can Teach Us About How to Improve the Systems of Our Lives

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