402. Why Quitters Often Win w/ Annie Duke (Refreshed Episode)

Welcome to episode 402 of the Brainy Business, Understanding the Psychology of why People buy. Today's episode is a conversation with Annie Duke about her book, Quit. Ready? Let's get started.

You are listening to the Brainy Business podcast, where we dig into the psychology of why people buy and help you incorporate behavioral economics into your business, making it more brain friendly. Now, here's your host, Melissa Melina Palmer.

Hello. Hello, everyone.

My name is Melina Palmer, and I want to welcome you to the Brainy Business Podcast. Today's episode is a refresh of my conversation with Annie Duke, discussing her book Quit. We humans can have a hard time saying no to things, both when it is a new opportunity and especially when we have already started down the path. We've been told that quitters never win and winners never, never quit. But as Annie will share in our conversation today, in reality, the opposite is often true. Winners say no to and quit things all the time. Not everything, of course, but quitting is a big part of winning. Since you can't do everything, you need to know what to stick with, what to quit, and when. It's often earlier than you think.

If you want to win, how exactly.

Do you know what and when to do those things? Well, that's what Annie and I are discussing in today's episode. Don't forget, there are links for everything, including my top recommended past episodes and books, all waiting for you in the show Notes for this episode, which are found within the app you're listening to and@the brainybusiness.com 402 all right, let's talk about quitting. Annie Duke, welcome to the Brainy Business Podcast.

Thanks for having me. Excited to be here.

Yes, I know I told you, I've been this day. I know it's, you know, first time meeting me for you, but I feel like I've met you many times and I've been so excited to have you come on the show someday. And it's here.

It's here. I'm so excited. Oh, that feels really good. It's always. It's always nice to be wanted.

Yes, definitely. Well, for everyone who is not yet familiar with you and your background and who you are, I love that you have such a different background than most people in general, but definitely those who've been on the show. So can you share a little bit about who you are and how you found yourself in this world of behavioral science?

Sure. So I started off my adult life actually as an academic at the University of Pennsylvania. I did five years worth of PhD work there with Lila Gleitman was my advisor, who I love dearly. She just passed last year. And her husband, Henry Gleitman, both larger than life intellectual giants. And so I, my plan in life was to go be a tenured professor. Like I was going to go get a tenure track job. And in fact I already had like all my job talks lined up at a variety of places for tenure track positions. But during the last year of graduate school, I've been struggling with a stomach illness and it became acute. So I actually ended up in the hospital for two weeks. And it was, it was bad timing for the two weeks because it was when all of my job talks were supposed to happen. So I sort of realized at that point that I needed to take some time off and attend to my health, which I did. So I canceled my talks. I was going to go back out on the job market the next year. But during that time, so what happened was so I got out of the hospital, I took time off, I needed money because I didn't have my fellowship anymore and I had like a whole bunch of constraints on what I really could do to make money because I didn't want to start a new career because I was going to go become an academic. I needed very flexible hours because I was not feeling well and I didn't want to commit to anybody when I knew I was going to leave. So a variety of things, but my brother actually was the one who suggested that I play poker. I had, I played a few times on vacations during graduate school, which he had treated me to because I certainly as a graduate student didn't have enough money to go on a vacation out to Las Vegas. So I had some familiarity with the game. I had watched my brother play a lot and he said, I'll give you some coaching and you can play. So I was planning to do that in the meantime. And I sort of joked meantime turned into 18 years basically. And I ended up playing poker for quite a long time as my profession didn't actually go back to academics, at least not for a long time. So that, that's sort of my origin story in terms of like how I start to think about decision making under uncertainty in this, like super deep way through both my academic training, which was more traditional in psychology, where I was studying, learning in particular, and then this high stakes, fast paced decision making problem that poker really presents to you, which is extreme decision making under uncertainty. So about eight years into my poker career, poker sort of exploded all over television. So that's another thing Is when I started playing poker, it wasn't on tv. The only reason that I happened to know about it was because of my brother. It's a very odd thing for somebody to be doing. So eight years in, it explodes all over television. And I start getting asked to give talks, and I get asked to give a talk. The first talk I gave, which was to a group of options traders who wanted me to talk about how poker might inform their thinking about risk. I actually talked about how poker might inform your thinking about cognitive bias, which I thought was kind of the more interesting topic. Or how cognitive bias will distort your risk attitudes. That's more the space that I was thinking in. From there, I started getting referred out and just got a lot of referrals in the first couple of years and then started doing it with more intent. Found myself really loving it and spending more time on that than I was on poker, actually. But, you know, really felt like, well, I'm a poker player though, and you know, I had some, a world championship and a few other championships and just sort of felt like I should still play poker because it's like sort of who I am is the way that I felt about it. But was finding myself doing more of the speaking, starting to develop a consulting business. And finally in 2012, I retired from poker to focus on the business side of things. And then, you know, started consulting and speaking full time. Co founded a nonprofit called the alliance for Decision Education. Trying to bring decision education what you do and think about as an adult and, and start bringing that into K through 12 education. And then I really wanted to write this book, Thinking in Bets. So I had been thinking about that actually for a while. I had two books that I really wanted to write. One was thinking about poker from more of a behavioral psychology lens, which I wrote that was called Decide to play Great Poker. But then I knew I wanted to write Thinking in Bets. And the only reason why I'd written the poker book first was because I was a poker player. So I started to work on that, published that in 2018 and then right around that time, I actually ended up back at Penn. So I'm now back at Penn. I teach effective decision making. I co lead the class with Marie Schweitzer over at Wharton for executives and the executive education program. I am active in two labs, one with Maurice where we work on understanding pre mortems and effectiveness and so on and so forth of pre mortems. And then I also work with Phil Tetlock, who wrote Super Forecasting and his Wife and collaborator Barb Mellors on improving forecast accuracy in novice forecasters. And I'm actually currently finishing my PhD, so full circle. There you go. That's how I ended up in, in this kind of world. And that, you know, if I were to say the short answer is I started there and I really kind of never left. So I think that people think that somehow like I ended up doing behavioral psychology and cognitive science, but that's what I always was. And poker was kind of a way for me to think about these problems in an interesting way. It was really exciting to be learning that game. But if you, if you look at anything that I've done from the time that I enter graduate school on, it's all decision making under uncertainty. How do you learn? How do you close feedback loops? Those kinds of questions that I sort of feel like for my whole adult life I've been obsessed with.

Yeah. So interesting. Thank you for sharing. Like you said, I think everybody assumes you were a poker player who observed behavior, got interested in psychology and then wrote some books. Right. That sort of seems like what it is and that there's this whole life before and that it's, you know, like you said, closing that loop. Right, right.

Yeah. So people didn't know me, you know, from the cognitive science side until I published Thinking in Bats. But I was not only had done the five years at Penn in one of the first real cognitive science programs in the country, but you know, again, starting in 2002, like eight years into my poker career, I was already thinking in this space and thinking about this conversation that poker and cognitive science could have with each other, one to inform the other and vice versa. And you know, somebody like Phil Tetlock, we got to know each other actually when he was writing Super Forecasting. And so he's someone who knew that I was like thinking about in this space kind of the whole time and was very supportive of the book, like very helpful with thinking in bats. And obviously it's been helpful since. And we've ended up being collaborators. So, you know, there were people who were sort of like in the know, but in terms of my public facing self, people are always like, whoa, you, you know, you had this big, you know, left turn where you left and came back. And I was like, I didn't really. I think there's different ways that you can explore these topics and to kind of borrow from David Epstein, right. With range, I think the more ways that you're thinking about problems and the different frames that you have to, to you know, ponder these kinds of issues for me about, you know, learning under uncertainty. I think you end up bringing something different to the table because you do have different ways that you think about it. So, you know, even with the work that I do with Maurice Schweitzer and Lynne again, Gandhi, who's like an amazing graduate student, she's first author on all of the work that we do. A lot of the way that I think about pre mortems, for example, is informed by not only my experience at the poker table, but also my experience as a consultant, actually trying to think about what are the practical ways that you can help people to become better at decision making. And I think that I bring something different to the table than someone who's been in academics their whole life, because I actually can see these things as they sort of manifest in the world on a more regular basis. Particularly, I can see them as they manifest when the stakes are very high. And I just think that, you know, that allows us to collaborate in a really interesting way because they're bringing perspectives to the table that I don't have. Right. And I just think it makes for a richer, fuller work.

Oh, yeah, absolutely. I've had one guest on the show that has really talked about pre mortem, and that was Matthew Confer, who had a really great TED talk about three steps before you make a decision. Very related, of course, to what we're talking about here. Can you dig in a little bit more about what a pre mortem is and what that looks like for anybody that's a little bit less familiar with that concept?

Well, sure, but I want to preface this with saying that the research that we've done has made me very much of a pre mortem skeptic.

Interesting.

And I think that you pre mortems can be very effective if you combine them with other really good decision tools, but on their own. I think my best guess right now, given the work that we've done, and it was part of the reason that we did it, is that a pre mortem feels intuitively like it would be a great thing to do, but it doesn't necessarily change behavior, improve decision quality. And this is coming from someone who's written about premortems as a really good tool. So I just want to say that. So this is work that we've done since how to Decide, which was my second book since that came out, where I do recommend doing this. I just think that now what I would say is it needs to be combined with other things. So pre mortem, simply put, well, we know what a post mortem is, right? Like you have a bad outcome, the patient's dead, and then you try to figure out why, right? So you miss a sales target and you do a postmortem trying to figure out, like, what did we miss? Right? Were there things that we could have done differently? Were there behaviors that we could have changed, so on and so forth. Now, the problem, of course, with a post mortem is that the patient is already dead, right? You already missed the sales target. So the pre mortem is a tool that was really proposed by Gary Klein. There was previous research that was done on it, which is to say, why don't we think about why the patient is dead before it happens? And this goes under the general rubric of perspective, hindsight, which is to say, let's imagine that we get a certain outcome at some time in the future, and then we look back and try to figure out why. So we're going into Q3 and we say, let's imagine it's the end of Q3 and we've missed our sales target. What do we think happened? Why did that occur? All right, so you can see why intuitively, that would feel like a really amazing tool, right? Because it's going to allow you to see the negative space. It's going to allow you to live in the failure without having failed. And we know that that's hard for people. One of the things that Daniel Kahneman says is it should normalize dissent because it allows you to think about the bad things, not just the good things. So you get, hopefully you get off some of the cheerleading and over optimism and overconfidence and bandwagoning, right, that we can sort of get rid of those things. And there was some research that was done a long time ago which suggested that if you use prospective hindsight instead of just forward planning to say, how do we think things are going to go in the future? And you say, this is how it went. Let's imagine that you'll generate 30% more reasons for failure or success than you would if you didn't do this prospective hindsight exercise. Let me just first say that we have been unable to replicate that result. But it was an older study, it was not very highly powered as many of the older studies were. And so we've done this obviously very well powered, and we haven't been able to replicate that result. So I just want to say that. So there's two different tools. There's a pre parade and a pre mortem. A pre parade is Imagining that you exceed your sales target. So it's the good side of things. So the first thing that we found is that in general, as far as confidence or over optimism goes, that the pre mortem doesn't really reduce confidence. It kind of keeps you at the same place that you would be if you just did a control against a control group in general. But the pre parade really massively increases overconfidence. That's not. And you can see why we're imagining that we exceeded the sales target. So now we think we can do it. So that's kind of like we can think about that as an adverse side effect. Like the same way that medicines have adverse side effects. I think one of the things that we want to think about as behavioral scientists is as we propose interventions, are we inserting adverse side effects into the intervention as well? And then what we want to think about with any medicine is do the benefits of it outweigh the side effects? Right. So that's kind of what we want to be thinking about. So this is one of the side effects. It's certainly on the pre parade side and it doesn't seem to moderate overconfidence much when you do a pre mortem. So that's kind of the first thing. The second thing is that self serving bias gets introduced also. So for people who don't know, self serving bias is that when bad things happen to me as an individual, like I lose a hand of poker, I'll tend to blame it on things that are outside of my control. So when I lose a hand of poker, I'll say, oh, I got really bad luck. But when I win a hand of poker, I'll say I played the hand really well. So what happens is on the good side, for me personally, I will enhance sort of the influence, right. And cognitively of things that were within my control so that I can sort of take credit for the good stuff. And on the downside, on the bad side, I will sort of lean on luck so that I get to sort of maintain the appearance that I have of good decision making. This is something that I discuss in, in my work a lot. So what I had found in my own consulting was that when I was doing pre mortems that I was sort of seeing the same thing that when I did pre mortems, People tended to be thinking about like missing sales targets because something happened in the economy, right. Or the executive that they were working with at the company left, you know, or and they had to try to make a new relationship or things like that things that. That were more outside of their control. So I just sort of noticed that anecdotally. So when we started working together, Linea, Maurice and I, we're like, let's test that. We sort of anecdotally see it. Let's see if we're having this thing introduced in prospective hindsight. And the answer is, absolutely, we do. And this is across a variety of domains. So maybe in doing a pre mortem, you actually end up changing your plan. And I have to tell you, we haven't. It's been very hard for us to get people to change their plan. Once they make them, they sort of seem to stick with them. And we've tried a lot, and it's pretty tough to get them to do that. And maybe you get better results, but you don't. We've also been having trouble showing that either. So at least in the domains that we're looking at. And this is across like having people set fantasy football lineups, having people set health goals, having people do anagrams and predict how well they'll do on an anagram test, and then actually doing the anagrams. Like, we've done this many, many times, really having trouble getting people to actually change their plans. And we don't really seem to see any difference in the results that people get, like the number of anagrams that they can actually solve. And this is giving people options about how do you want to see the anagrams? In what order do you want to think about, you know, talking about, like, can you stop yourself from being distracted? Like, things like that. We can't seem to get behavior change. Now, I want to give a couple of big caveats for this. I think that if I were standing Gary Klein shoes, I would say. But I think that pre mortems need to be done in groups. And I think that that is also what Daniel Kahneman would say is that a big point of pre mortems is to normalize dissent. We have done no work in groups, so I can't say whether that's so or not. I certainly intuitively feel like that might be true, but we need to test it. So I don't know what would happen if you're in groups. And then the other thing is I think that people would say, well, these are kind of artificial environments, and if you actually do this when the stakes are higher, like in a work environment, I think you might get behavior change. Those studies are underway in work environments, so we should be able to see more when people are actually making, like, Project plans for work. Hopefully we're going to be able to get that, start getting this data pretty soon and start to see, well, when you raise the stakes, when it has to do with somebody's career, can you actually get behavior change? So that's kind of like the bad news about it. The good news about it, which we can get in to if you want to, is if you combine it with pre commitment contracts and then also something that we may talk about because it's in my new book called Monkeys and Pedestals, it looks like that actually can get people to change plans. If you can put those things together into a really nice mix, it seems to work better than doing any of those things on their own. So it's not all bad news because you can actually do it, but not kind of in the traditional way that someone might do a pre mortem.

It's so interesting. And as we said leading into the conversation, I love when we're able to talk about something that I'm guessing you don't get to talk about publicly at least all that often. So I am delighted that you shared that with us. So thank you very, very much. Really interesting and I'm so excited. So putting a pin in monkeys and pedestals. But that was one of my favorite parts in the book. So yes to talking about that again and that framing.

See, now we've left them with a cliffhanger because it's such an interesting name, but I love that. So now they have to listen to the podcast in order to find out what monkeys and pedestals are.

I know.

Oh yes, we'll have to. We'll make a little teaser audiogram too about this part. Right. That says, you know, you want to know what that is. So I did make some notes on past episodes just while you were talking for everyone. And so while I don't have an episode on self serving bias, I would say the closest to that is there's an episode on Fundamental Attribution Error, which.

Is very much a part of self serving bias. So yes.

Yeah, I would love if of course we can talk a little bit about your, your book. We've been talking about concepts from it, but I want to. We'll take the step back. You talked a little bit already about how to decide and thinking in bets, but it would be great. I, I guess you talked about them in mentioning that they exist but not really like so what each one is kind of about and then getting to quit, which is the new book and kind of how they work together or stack on each other that sort of thing.

So Thinking of Bets is a book that I really want to write for many, many years before I actually sat myself down and did it, which is essentially just really thinking about decision making under uncertainty. So pretty much any decision that you make is under the influence of two forms of uncertainty, the first being luck. You can make a perfectly good decision that's going to work out 95% of the time. And 5% of the time, by definition, that means it's not going to work out and you don't have any control over when that 5% is going to occur. Whether on this particular time you're going to observe that 5%. Right? So like if we related it to poker, I could have two aces and you could have two fives and we could put all our money in the pot. And I'm going to lose that hand 18 and a half percent of the the time. And I don't have any control. It's just the random deal of the cards, whether you happen to win it on that particular time. But I know that I'm going to observe that outcome 18 and a half percent of the time. I just don't have control over whether I do on that particular time. And then the other form of uncertainty that our decisions are under the influence of is hidden information. For most decisions that you make, you know very little in comparison to all there is to be known. And we just have to make decisions under those circumstances. You know, if you think about something like hiring, for example, think how little you know about a candidate. When you have to make this very big decision about whether to hire them. You have like a few references, a few interviews. They, you know, unless you've been lucky enough to have them, say, intern for you for a long time, when you actually decide to bring them on for a full time as a full time employee, they've never worked for you, they've never worked in the organization. You don't really know if they're going to be a good, like cultural fit. You don't know if they're going to be a good partner. You don't really know what their work ethic is, except in as much as you sort of learn some things from people, other people that you talk to. You know, one of the things that say about, it's like if you married someone after one, you know, a few dates and like a couple recommendations from friends. And sometimes people do that, by the way, and you're like, wow, that was really bad decision. But in hiring, we make that, that literal decision all the Time where people would be like, I'm in love. I just met this person for a week, we're going to Vegas to get married. Like, what are you doing? That's nuts. But that's what hiring is. So pretty much every decision we make is like that type of decision. And so I really wanted to explore in thinking and Bats, you know, using poker kind of as a backdrop for it, what are the ways in which that really frustrates our decision making, reduces our decision accuracy? How does it make it very hard to close feedback loops? We talked about self serving bias. One of the things that I really talk about in that book a lot is something called resulting, which is when we're looking afar at other people, we assume if they have a bad outcome, it's a bad decision. When they assume when they have a good outcome, it's from a good decisions. So this is when it's not something where we're comparing ourselves to them. So I opened that book with like Pete Carroll at the end of the 2015 Super bowl with 26 seconds left on the clock, calling a pass play that gets intercepted and everybody thinking that was a terrible decision. And I talk about why that's a big cognitive error. In cognitive psychology we call it outcome bias. I like resulting. It's the way that we talked about it in poker. But it would, it would be basically saying like, if I watch somebody and they lost a hand of poker where I wasn't at the table, that they must have played poorly and if they won in the game, they must have played well. Which in a given game of poker is absurd because there's so much luck involved. So I talk about like, how does it frustrate closing feedback loops. I talk a lot about self serving bias in that book as well, and really trying to think through the fact that the outcomes that result from any decision are probabilistic, they're not perfectly linked. And how do we actually start to try to think through that in a practical way so that we can become better decision makers? So that was thinking in bats. That is very lucky. People seem to like that book. And then I wrote how to Decide as a companion, which was more like practical tools for making decisions. Pre mortems are actually in that book. So it's thinking about like, how do you think, for example? So if we think about the resulting problem, when you're looking back at a decision, how do you actually figure out whether it's a good decision or not? And you know, I really recommend people say what did I know at the Time, what did I find out after the fact? Could I have known those things beforehand? If the answer is yes, could I have afforded to know them? Would I have had the time to find them out? Because sometimes you would I have had the resources or the money to be able to find those things out? If the answer is yes, then include that in your decision processing process going forward. But most of the time the answer is no. Most of the time, what happens to us is we learn new things after the fact that we couldn't have known beforehand, but we act like we should have been able to know them beforehand. So I'm sure you've talked about this on podcasts. That's hindsight bias. So, like really giving practical tools like, how do you track your knowledge? What are the things that you should be recording in advance? What's the importance of listening opinions independently from people in order to stop, you know, this sort of cross pollination and bandwagon bandwagoning and, you know, undue influence and so on and so forth. So that's really what that book is meant to do. So, which then brings us to quit. So how. How did I end up writing Quit? Well, I was doing podcasts for how to Decide, and I have a chapter in how to Decide, chapter seven, which is on overcoming analysis, paralysis or paralysis by analysis. And one of the reasons why I did that was because I went through this whole process for, you have to think about the option under consideration, then think about the outcomes that are possible, think about what the payoffs for those outcomes are, think about what the probability is for each of those, and then that can sort of get you an expected value. And then you can start to compare options across it, taking into account what your risk attitudes are. Now, I say this in a way that is much more digestible that you can actually do in your everyday life. That's the super short version of it. But it's comp. It's like complicated.

I'm guessing most people still hear that and are like, wow, every time I have to do that when I'm making big decisions, this is what I would.

Say to them is, you do do that every time.

Right?

Right. I mean, this is one of the things that I really try to get across in how to decide, which is there are all sorts of things that are implicit in every decision that you ever make. And the more that you can make those explicit, the better off you are for decisions in particular that are high stakes. So even if you're choosing a route to work, right, and you have Route 1 or Route 2. What you're thinking about is what's the probability that I'm going to get to work on time or late or, or early, you know, that I'm going to encounter traffic, right? So on and so forth, like what's going to get me there faster? And so in this particular case, what you're trying to do is figure out, you know, the fastest route to work given, you know, the time of day and those kinds of things. And that's just an expected value. What's my expected time to work? It's just an expected value. And so whether you know you're doing it or not, you're doing it for everything that you do, even ordering off a menu. So all I'm doing is recommending like let's name it and let's make it explicit. Because then when you go back to look back and try to learn from the decisions that you make, you will be able to look back and say this is how I was thinking about the problem at the time. Instead of making up all sorts of stories in retrospect like that cause you to fall prey to self serving bias and whatnot. But doing that in a full blown way, which I would recommend you do if you're hiring like a C level executive, it feels like a lot. So I followed that with this chapter on basically most of the decision that you make you can actually probably make faster. And the way to think about how can I decide when I can go pretty fast and wing it is what are the consequences of me getting this wrong? This is a very simple trick for trying to figure out should I slow down or should I go fast? How do you figure out what the consequences are of getting it wrong? Well, one is there's just the impact of getting the decision wrong, right? So we can think about if I hire an intern and they turn out to be not great, it does not have a big impact on my company. But if I hire a CFO and they turn out to be not great, that has a huge impact on the company. So therefore I ought to slow down when I'm hiring a cfo, I ought to speed up when I'm hiring an intern. And then the second piece, which now relates to why I wrote quit, has to do with optionality or what is your ability to change your mind later. So we can go back to the CFO and intern, right? If you have to let an intern go, it's very easy to do that. But if you have to let a CFO go, that's just like a Much less reversible decision. This is super valuable in terms of thinking about how much time should I take with this decision. Now, I just want to say what's interesting is that people often get this reversed, and they take a lot of time on decisions that they shouldn't be spending a lot of time on and no time on decisions they should. So we have people going out the door after a week to go get married, but when they're looking at a menu in a restaurant, they're taking 15 minutes to decide what to. So I'm doing podcasts for how to decide, and every podcast, I'm kind of shoving the conversation over to this thing about quitting. Because I started, like, I put it in. It's only about a page and a half in this book, but I started thinking about, like, how important it is this option to quit is, and then started thinking about, first of all, how bad everybody thinks quitting is. Like, if you quit, you're just a loser. Like, I mean, there's an actual aphorism for that. Like, quitters never win and winners never quit. That's calling quitters losers. And that we're also actually really bad at exercising the option to quit when the time comes. So, you know, going into things that the option to quit is really valuable and it allows you to speed you up, right? Like, the reason why I can hire an employee in a similar circumstance, like eloping to Vegas after one week is because, unlike a marriage, you know, it's easier to unwind, right? I can let them go. But we all know that nobody lets employees go on time, right? Like, there's a management heuristic, which is, like, the first time you think about it is when you should really fire them. And I coach, you know, people all the time, where it's six months later and I'm, you know, and I'm like, okay, you've been talking about this person for a really long time. Like, let's get to it. And one of the things that I help people to do is to get that to that decision faster. Not because I'm mean, because it's better for the employee, right? Like, let them get the feedback and get into a job where they can thrive. Like, I feel like you're preventing them from actually thriving and living their best life. That's my philosophy. So I started thinking about that. So I was talking about quitting in relation to just sort of like, your ability to decide under uncertainty. But then separate from that, I'm starting to think about really, like, why do we think about Quitting so negatively. And also how bad we are at quitting. So I know that as I finished how to Decide, I swore I was never going to write another book, which is true. I did swear that. But, gosh, this topic really seems to have gotten hold of me. I'm thinking about it all the time. So I just started writing people and saying, hey, do you want to have a conversation about this topic? So the first person I always go to is Michael Mauboussin, who's incredible. People definitely should get his books. There's this success equation and think twice. And I don't just go, look, he's got a ton of books. Super smart guy. He also has lots of white papers. Just great follow on Twitter. One of the smartest people I know. And I just. I scheduled a zoom. This was in the fall of 2020, so luckily everybody was available. And I just said, you know, I'm thinking about this. And, you know, and he seemed to sort of light up and get kind of excited about it. So then I was like, okay, let me write some more people. And I got in touch with Daniel Kahneman, got him on a zoom, talked to him a couple of times. Richard Thaler, Don Moore, who wrote Perfectly Confident, another book that I really highly recommend. Cass Sunstein, Katie Milkman. And then, of course, people started to introduce me to people, right? So I got to talk to Max Bazerman, and then I start to be able to talk to people who have very interesting, like, stories about quitting. So I got to talk to Maya Shankar, who's, like, an incredible individual. She's at Google now, but she actually founded the behavioral unit in the Obama White House. Just such a fantastic person. She has an amazing podcast like Change of Plans. Super cool. People should really get to know her. But she also has a very interesting quitting story. And then Josh Koppelman, over at first round, introduced me to Stuart Butterfield, who had a really cool story. You know, 40 zooms later, what I discovered was everybody, literally every person that I wrote and said, hey, I'm thinking about writing a book called Quit. Like, I had the name right away. And the science, you know, the very rich science that shows that when we quit, we're usually doing it too late. And what the variety of different biases are that are kind of driving this behavior, many of which I'm sure that you've talked about on this podcast. And people would write me back right away and say, yes, I want to have this conversation. And that started to make me think that this was a worthwhile topic to explore because people who I really admire were very excited about it. I started doing these zooms in November, I think, you know, November and December of 2020, I was Zooming. And then by early February, I turned in like a 60 page proposal to my publisher and you know, I sold it to them in that month and started writing it so.

Well, I'm so glad for all the reasons. Like I said, I had been waiting to reach out until it felt like you'd be excited to talk about something. And so I'm glad you have another book. So we were able to. You didn't stick with your I'll never write another book plan after I had to decide. You mentioned there are great stories in Quit. It's such a fantastic book. A lot of the tying in and connection points that I know people enjoy. I do something similar here. That's something where, you know, is the superpower that apparently we share of being able to take things that seem they don't go together and say they're very similar. They, they do go together. They're two sides of the same coin, whatever that is, bringing that together.

But we have to talk about monkeys.

And pedestals because we said that we would. So I love this mental reframe this slight way to think about something in it. And I feel like because of the interesting name, it makes it so people can kind of just throw it out and say, oh, that's, you know, is this a monkey or is this a pedestal? And it, it can help you kind of stick to it. So if you can share a little bit, that'd be great.

Yeah, absolutely. So I, I mean, I feel like we've been talking about a lot of bad news so far. Like we don't quit on time. We don't like to quit in the losses. And when you think about goals, like if you think about something like Everest, one of the problems that we have is that once we set a goal, we're immediately in the losses, at least from a cognitive standpoint. Because once you set a goal, you also set a starting point and progress beyond the starting point. Doesn't really matter that much to us. It's how short are we of the goal. So we'll just sort of like head toward these goals come what may. And we don't want to walk away from things because of that. So that's like a lot of bad news. But the good news is there are ways to fix this. So to try to understand the monkeys and pedestals mental model, let me just. Hopefully you'll indulge me and let me tell a little story about the California Bullet train. So the California bullet train is a very big public works project in California. They're trying to connect San Francisco and Silicon Valley to the north, to Los Angeles and San Diego to the south with a high speed speed rail system. So you can imagine why you would want to do that. If you think about the economic engines of the country, they're these coastal centers to the north and south and what's in between does not necessarily get to share the prosperity of those particular big economic centers. So on top of that, all of those housing markets that are in San Francisco and Silicon Valley and LA and San Diego to the south are very congested. So if we think about how do we actually sort of spread the love in terms of the economic well being, but also decongest those housing markets, it would be really nice to be able to connect those to all the areas on the interior in between those two economic hubs. So that's the idea behind the California Bullet train. Great idea. Right. So back in the aughts, right around, you know, 2000, between 2008, 2010, they have this report proposal. In 2010 they put a bond issue on the ballot to try to get, to raise money to be able to start this project. The bond issue is for about 7 billion I believe of or 9 billion of a 33ish billion dollar projected budget to be able to build this high speed rail. So that's, that's approved in 2010. The section of track that they approve to start with is between basically on the interior. Okay, so it's like between like Madeira and Fresno I think. So it's on the interior of the line connecting two places sort of in the middle of San Francisco and LA. That doesn't get started until like 2015. And in the meantime as they're starting to, you know, sort of like construction, they kind of realize that there's a really big problem. In fact there's two really big problems. And those two big problems are two huge mountain ranges. So I'm not sure why they didn't know about this beforehand because it's not like the mountain ranges all of a sudden appeared or something. But there's two big mountain ranges. One is just to the south of Silicon Valley, the Diablo Range. And in particular there's something called the Pacheco Pass, which if you're dry, if you've ever driven from San Francisco south, you've had to go over the Pacheco Pass. Very dicey and you know, very Windy. And it's part of the reason why it's San Francisco in that area isn't connected to the things to the south is because of this big mountain range, the Diablo Range. Now if you look at the LA and what's to the north of la, there's another mountain range called the Teh Chapi Mountains. And they sit to the south of Bakersfield, but to the north of la. So they've kind of cut Bakersfield off from LA with this, with this very big path. So they kind of at some point in, you know, around 2015 or something, they're like, oops, wait, there are these big mountain ranges and that seems like a really big problem for completing the line. They now re estimate the budget to be somewhere around 80 million. But they say we don't even know if we can do it. We don't even know if we can engineer tunnels through these two mountain ranges to even make this whole line possible at a cost that taxpayers would be willing to bear. But meanwhile, remember, they're building this track between like Madera and Fresno. So Gavin Newsom has this. I think it was like in 2018, it's like, oh, okay, let's reevaluate the California bullet train. At this point. They know that these two mountain ranges are a huge problem. Okay, so they already know this. And what do they do? They say, oh no, we're not going to stop. Even though the budget has already exploded from 33 million to 80 million plus. And it could be much more than that. This is the knowledge of the time. Instead, we're going to prove track between Bakersfield and Merced again. That's to the north of of this range, the teh choppy mountains that are the problem, right? So you're now building again on flat land. And then after that they say the next thing we're going to do is build track and connect San Francisco to Silicon Valley, which is to the north of Diablo Range. So now that instead of stopping, they approve these two pieces of track that don't address these gigantic engineering problems at all. The last report that just came out, the budget has now exploded again to like $110 billion. And they still have done nothing to address the two mountain ranges. Okay, so you're sitting there like, what the hey, you know is going on? And obviously, like most people who are from, you know, who hear this kind of story immediately think sunk cost, right? Like, obviously this is a big problem because from their point of view, they've already sunk over $7 billion into it. And we can't stop now because that would be a waste of the taxpayer money, so on and so forth. It's not the way that we really want to think about it because what matters is the next dollar of taxpayer money worthwhile. And I would argue, well, you can't know until you know whether you can tackle these two engineering challenges. So putting money into track, connecting, you know, Bakersfield to Merced or San Francisco to Silicon Valley is, that's the real waste of money. Right? But instead of stopping, they decided, well, we're just going to build the easy stuff first. Think about it like low hanging fruit. People always say, let's tackle the low hanging fruit. That's kind of what they're doing here. Okay, so the nice thing to do here if we're approaching this kind of problem, which is exactly what we have with project management all the time, right, is what if we could have seen the mountains in advance? I mean, I'm not saying like see them with our eyes because obviously they've existed for millions of years. I mean, seeing them for what they were, which was the bottlenecks to the whole thing. If you can't solve these two things right, then there's no point in building anything else, which is exactly what they're building other things before they figured out whether they could solve these two things. And this is where Monkeys and pedestals comes in as an incredibly helpful framework for trying to figure out how do you approach projects to find out the information that you need to find out the fastest. So that you can either figure out, is this something we want to stick to no matter how hard it is, Right? Or is this not worthwhile for us? Think about it as getting to know as fast as you can know in quotations. N o not K, N o W Getting to know, find out what is the crucial information? What is the bottleneck to actually completing this so that we can get there fast. So monkeys and pedestals comes from Astro Teller, who is the CEO of Axe, which is Google's innovation hub. And they're trying to create world changing ideas. But obviously they're moonshots, right? So moonshots are always going to have bottlenecks. If we can't unlock this technology, there's no point because we can't do what we need to do in order to make this commercially viable. So he's always trying to figure out like what are those bottlenecks that really stop you from being able to get what you want to get done? Because if he can stop a project after spending $2 million on it instead of 9. You know, that's not a waste of $2 million. That's a savings of $7 million. And that's the way he approaches everything. So here's monkeys and pedestals. Imagine that you're trying to train a monkey to juggle flaming torches while standing on a pedestal. Now, obviously, if you can do that, you'll make a lot of money. This is very exciting. You can go stand in, like, Quincy Market in Boston, and people are going to throw lots of money at your juggling monkey. One of those things is a possibly intractable problem that if you can't solve it, you won't have an act. And the other thing is building the pedestal. So what does he mean by that? Right. Well, we already know that you can build the pedestal. People have been building pedestals for millennia. And you could always even just turn a milk crate upside down. You can buy pedestals on Amazon. Right? So if you spend time building the pedestal to start, it may give you the illusion of progress, that you're doing something that's moving you forward to your goal. But it's not actually, because it's not new information, you know, that you can already do it. The problem with getting this act to be successful is can you actually train this monkey to juggle the flaming torches? So what his point point is is kind of twofold. One is start with the monkey. First, you have to start trying to figure out and tackle the hard part of the problem first. So you better figure out if that monkey can juggle before you ever build a pedestal, because first of all, the pedestal is wasted time. Second of all, every minute that you spend building the pedestal, every dollar that you spend putting into the pedestal creates a sunk cost problem. Because that's, oh, well, I put this time and money into this beautiful pedestal that I build. I can't quit now because I'll have wasted it, because then you have to quit in the losses. We don't. We don't like to do that. Right. The second insight that he has that I think is really important is that when you do butt up against hard things, you tend to turn to pedestal building rather than to quit. Right. Like when you're trying to design your slide deck and you can't figure out what you want to say on a particular slide or how to design a table so that it's understandable, you start actually trying to figure out what font to use, which is pedestal building. So now if we go back to the California bullet train, we can see this monkeys and pedestal problem. So clearly, any track that you build on flat land is a pedestal. We've been building track on flat land for like two centuries. We already know that you can do it. It's not helpful to build track from nowhere to nowhere on flat land. I mean, nowhere in the sense of not helping you complete the line. Right. So if you're building between Bakersfield and Merced, is that actually helping you to figure out whether you can build the whole line? No, because you already know you can build track there. The problem, the monkeys in this case, there's two of them, are those mountain ranges and can you actually blast through them? So notice that, first of all, they never identified that in advance. They didn't figure that out until like something like eight years into the project. Okay, so that's a problem. They didn't identify the monkeys first, which is what you should always be tackling. Right? Hashtag monkeys first. But then notice that when they did identify those monkeys and they realized that this was a very large engineering problem, did they go and tackle them?

No.

Did they say, we quit because we can't do it for the $33 million that we thought this was going to cost? No. They built two more pedestals instead. They approved two more pieces of track on flat land that actually don't address the monkeys. So they turned to pedestal building when they realized that they couldn't tackle the monkeys. So I just think this is like such an. An important and useful mental model for trying to figure out how do you actually attack problems so that you can figure out as quickly as possible, is this worth pursuing or is it not worth pursuing pursuant. And then here's where it gets really important is once you've done that, once you've figured out, like, these are the monkeys, right? So in 2008, you're thinking about building the California Bullet Train. Let's say you do a pre mortum. This is actually pre morms are a reasonable way to identify monkeys to say that we actually know. That's the thing where I said, like, if you combine pre mortems with something else, that something else being monkeys and pedestals, that can actually boost pre mortems to be really useful. So you do a pre mortem, you say, what could go wrong in 2008? And then you specifically say, what are the monkeys? So you follow the pre mortem with monkeys and pedestals. You actually need to do it in that order. So you say, what could go wrong? What are the monkeys? You figure out what the monkeys are, and then you change your plans according to that so let's say you did that in 2008 with the California Bullet Train. You know what could go wrong? Well, you identify a bunch of stuff that could go wrong. And then you say, what are the monkeys? At that point, you've now identified the mountains as the monkeys. And then you say, should we change our plan? Remember, the original plan is we're going to build track between Madera and Fresno. And now you say, should you change your plan? This is where you can actually create behavior change. Because people will say, yes, I think we should change our plan. And we should probably go do like an engineering study, a feasibility study on the mountains and just spend a little bit of money to try to find out the thing we know so that we know whether we can actually unlock the whole system. Then you can combine monkeys and pedestals with something really useful, which I call kill criteria, which is to say, okay, so let's say that you do the feasibility study. You know that the monkeys exist. You're not building pedestals first. How do you actually figure out once you've started spending money, which is when it becomes really hard to quit, when it's the right time to quit, and that through setting kill criteria. So what are the things that I could see in the future or find out in the future that would tell me that I ought to quit? Right. So you do the feasibility study on the engineering thing. You could say, if we go over budget by this much, if we go over time, if we try to blast through the mountains and we come across these problems, whatever, you figure out what those things might be and then you actually set those out. So like in a sales example, if the first meeting that I have with the potential customer, all they want to talk about is price, right? So you think about that in advance. You have a very negative signal that this isn't a deal worth pursuing. And then that's going to help you to not pursue the deal anymore. It's going to help you to stop building track when you can't actually solve for, for these mountains. Right? And I think that those two things taken together with pre mortems to help identify those things, right? Imagine that you didn't close the deal. There were early signals as to what those were, what were they? That's how you figure out that price was a problem. That when you take that together, creates a really good brew for allowing us to figure out how do we tackle projects, how do we become better at quitting when we get those negative signals so that we can reduce our escalation of commitment and actually be smarter about the way we approach things. And you know, I mentioned low hanging fruit. The thing I want to say is think about how opposite this is to the way that people approach projects. Right. What do people always say? Well, let's tackle the low hanging fruit. And why do they want to do that first? Well, because they know they can do it. So they're sure that they're going to look like they've made progress. Right. And it's going to give them the feeling of moving things forward. But there's no point in tackling any low hanging fruit whatsoever if you can't train the monkey. So I don't have a problem with tackling the low hanging fruit. At some point. Right. At some point you need to build the pedestal. At some point you need to build the track that's on the flat land. I'm just saying you shouldn't do that first. And I think that it's the reverse of the way that most people. Well, you know, we'll be, we'll work on the hard problem while you go build those pedestals. No, no, no, don't do. Figure out the hard problem first and then you can build some pedestals.

Yeah, I'm going to sit, I'm going to ruminate on what I'm going to do with the monkey while I'm building the world's greatest pedestal and I'm going to figure it out. Along the way. I have an episode on bike shedding, which similar concept of, you know, we work on the greatest bike shed instead of working on the nuclear plant. Um, for Parkinson's law of triviality right there. So yes, we'll definitely link to that and thank you so much. And I love, I just love how well that's laid out with the monkeys and pedestals and like you said with the, with the bullet train. And it's one of those that you're like, you didn't really. We didn't think about the mountains. We didn't wonder why no one had built a track here before.

You know, I mean it really is like when you read it, it's a pretty amazing story. And you know, it's like all of a sudden somewhere around like, I think it was like around 2015 or 16 or something, they were like, oh no, these mountains are a problem. Oh wait, it's like you started working this in 2008.

Did you not look at a map?

I think this is a big problem that we have is we think about waste as backwards and we should be thinking about it as Forwards. It doesn't stuff you've already spent. You've already spent. What matters is, is the next minute of my time worthwhile? Is the next dollar that I put into this thing worthwhile? And I think that that's a really big mindset shift that's very hard for us as humans.

Right. Yeah. Now that you know, you gotta. It's time to. To stop and do that. And then I know you've got great stuff on status quo bias and reframing on that as well, which people will. They'll. They can get their copy of Quit now so that they are able to read all about these stories in more detail, many other really, really great ones, and how it all ties in together to help us to reframe the way we think about quitting. For one, that it's not for losers.

Winners quit.

Two, you just have to quit the right stuff so you can focus on those right things.

It's not that winners quit too. It's that. It's just that winners quit. It's like they quit a lot. That's how they win.

Mm.

The key is this. And this is what winners do. They sample a lot of stuff, then they settle on some course of action. They tackle the monkeys first. If the world gives them a single that signal that they should be doing something else, they go and do that other. You know, they switch, even if they don't even know what they're switching to. That's the. That's the other thing. Right. Like, sometimes we want to know exactly what we're switching to, but if the thing we're doing isn't worthwhile, we ought to switch right away because that actually frees us up. Better to see what the other opportunities are that are available to us. I have a chapter, chapter 10, actually, on forced quitting. And, you know, sometimes you're fired, sometimes your partner breaks up with you. Right. Sometimes you're quitting when you. You don't want to be. You know, it's not your choice. And the fact that while, you know, obviously it doesn't always work out well, it does free you up to go explore other opportunities that you wouldn't otherwise explore. And like how you should be thinking about, how can I do that more voluntarily? And that's what winners do, is they pick the right things to stick to. That is the thing they do, and they abandon everything else. Grit will help you be successful if you're sticking to the right things, if you're being gritty about the right stuff. And in order to be gritty about the right stuff. You have to quit a lot. It's just true.

Yeah, definitely. Well, thank you so much, Annie. Have so enjoyed our conversation. I look forward to hopefully the next one, as I think we could keep talking for another two hours or so here for everyone who wants to go learn more to get all of your books to connect with you, you know, what's their best course of action? We'll of course, link to everything in the show notes.

Yeah. So Twitter for sure, because I'm actually. I'm actually me on Twitter. Yay. But Twitter for sure. And then Annie duke.com you could. There's a contact form there. I actually really love when people write into me. There's a woman, actually, Dr. Sarah Martinez, who's in the book, and the reason why I know her is because she wrote into my website. So I'm not perfect at responding to everybody, but I try, and those are really the two main places. And then, you know, you can buy my books wherever books are sold near you are online.

Yeah. Yep. We will be linking for those in the show notes, of course, and for everything. And give a little. A little Twitter love, of course, to Annie. I know as an author of books myself and for all the authors I've spoken to, there's no version where people do interviews or talk about their work and somebody says, wow, that was really interesting to me. Thank you for sharing it. And they think, I wish someone hadn't told me they enjoyed my work. That never happens.

That's true as well. The other thing is, I do have a newsletter that goes out. So lately it's been a lot about quitting, not surprisingly. But you can subscribe to that over@andyduke.com and you can actually see archives of the newsletter just in case you want to figure out whether it's worth your time to subscribe. But hopefully people will subscribe and hopefully find it useful.

Yeah, absolutely. We will definitely link to that as well. And just thank you so much again for all your work and for joining me on the show. It was a lot of fun to chat with you.

Thank you.

So what got your brain buzzing as you learned about quitting with Annie Duke today? For me, one of the biggest things that has always stuck with me from this book and my conversation with Annie is the idea of monkeys and pedestals. It's such a great way to explain something we all tend to do by default, building pedestals and show why you need to do the counterintuitive thing instead. Sure. Sometimes we want and need to go after that low hanging fruit. But too often that runs into the world of pedestal building. If you spend too much time building pedestals, you can forget about the monkey. And while it is harder if you know what your monkey is and you can start with training that monkey earlier, it can save a lot of time and money in the long run if you just can't train the monkey.

Because without a fire juggling monkey all.

You have is a pedestal. And who wants that? What about you? What stood out for you today in this episode on when and how to quit so you can get more of the right things done, please do come share it with me and Annie on social media as we would love to hear about it. You'll find me as the Brainy Biz pretty much everywhere and as Melina Palmer I on LinkedIn. And to make it easy, there are links in the show notes to connect with us both, along with links to my top related past episodes, books, a timestamp summary of the episode, and more. It's all waiting for you in the app you're listening to and at the brainy business.com 402 and just like that, episode 402 with Annie Duke is done. Join me Friday for a brand new episode with Kelly Clancy to discuss her book Playing with Reality. It's going to be a lot of fun. You don't want to miss it. Until then, thanks again for listening and learning with me. And remember to be thoughtful.

Thank you for listening to the Brainy Business podcast. Melina offers brain virtual strategy sessions, workshops and other services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.