The Art of Manliness - How to Decide

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

00:00:11.360 many decisions every single day from little ones like what to eat for breakfast to big

00:00:15.200 ones like whether to take a new job. Given how regularly we're deciding, we certainly

00:00:18.960 have a vested interest in getting better at this skill. But how do we do so? How can

00:00:22.600 we get better at making big choices and spend less time dithering over the insignificant

00:00:26.400 minutia that often overwhelms our mental bandwidth? And why did anyone teach us how

00:00:29.840 to do this stuff to begin with? My guest today has written a book that offers an education

00:00:33.440 and a subject matter many of us missed out on. Her name is Annie Duke. She's a former professional

00:00:37.240 poker player and is now a decision-making expert and strategist. And her latest book is How to

00:00:41.700 Decide, Simple Tools for Making Better Choices. Today on the show, Annie shares many of those

00:00:45.680 practical tools beginning with how to overcome hindsight bias and resulting, which is our

00:00:49.580 tendency to judge decisions based on their outcomes by doing something called knowledge

00:00:53.080 tracking. We then discuss how to figure out the probabilities for things that seem difficult

00:00:56.820 to predict and the importance of embracing an archer's mindset when making decisions. We

00:01:00.860 then get into when you should make decisions slowly, when you can speed up, how to employ

00:01:04.540 the only option test when making a choice, and why when a decision is hard, it's actually

00:01:08.700 really easy. After the show's over, check out our show notes at aom.is slash howtodecide.

00:01:13.700 Annie joins me now via clearcast.io.

00:01:23.960 Annie Duke, welcome back to the show.

00:01:25.460 Thanks for having me back. I'm so excited. This is when we talked, what was it? Gosh,

00:01:30.920 it was two years ago, right?

00:01:32.480 Yeah. When your first book, 2019.

00:01:34.480 Yeah. That was absolutely one of my favorite podcasts that I did last time. So I'm so excited

00:01:39.080 to be back.

00:01:39.960 Well, same here. Thinking in Bets is the book we talked about previously. It's a book that

00:01:43.620 I keep thinking about, even though I read it several years ago and think about the ideas.

00:01:48.620 You got a new book out though. It's a follow-up called How to Decide, Simple Tools for Making Better

00:01:53.140 Choices. And this book is basically, it's a workbook, I would describe it, of the tools

00:01:59.220 you talked about in a very, I think, theoretical way in thinking in bets, but showing people

00:02:03.860 how to be more explicit and how to use them.

00:02:06.660 And as I was reading this, I was thinking, how come no one ever told me this stuff before?

00:02:12.140 Because, right? I mean, like we make decisions all the time, small ones, really big ones, but

00:02:17.620 no one ever sits you down and be like, here's how you can make a good decision. Like, why

00:02:22.480 is that? Like, why don't we get taught explicitly how to do something we do every day?

00:02:27.440 Yeah. So, you know, I think this is a very deep question. So just as background, I co-founded

00:02:31.960 an organization called the Alliance for Decision Education. And we're actually trying to tackle

00:02:36.200 exactly this conundrum that you're pointing out, which is why don't we teach decision education

00:02:43.340 to, like, K-12 students. You know, when I talk to people and I ask them, you know, did

00:02:49.640 you ever have an explicit class on decision-making? You know, if anybody has, it would have been

00:02:56.560 in college. And only if you were pursuing, like, certain types of majors.

00:03:01.760 Right. I had a, I took a philosophy of decision-making class in college.

00:03:05.560 Yeah. Yeah.

00:03:06.420 Exactly. So nobody really teaches you how to make a good decision, which is kind of strange.

00:03:12.600 So, you know, I have some theories about it. So I'll just throw a couple things out there.

00:03:17.960 One is, you know, our educational system is set up actually from way back when, from when

00:03:24.700 England obviously was very colonialist. They had, you know, people who were far and wide

00:03:32.100 and everybody needed to sort of learn very specific skills, like how to sail. So everybody had to be

00:03:39.580 taught the same thing and decision-making actually wasn't something that they were trying to teach

00:03:44.440 because they wanted people, people needed to all be doing kind of the same thing and have the same

00:03:48.640 skills. So for example, trigonometry is in there, not just because it's really good for sailing,

00:03:55.120 but also because it's hard and it doesn't directly feel like it's practical or connects to anything.

00:04:01.660 So it was meant as a screener that would tell people you're going to go on to sort of great

00:04:06.100 things because you had the grit to be able to get through trigonometry and all of you aren't.

00:04:10.660 And, and then that translated into American education with tracking. So trigonometry is literally a way

00:04:16.740 to clear out the people who aren't willing to work really hard at things that don't have any purpose,

00:04:23.640 which is kind of a weird thing to have in the school system. So our curriculum today is not

00:04:30.240 really designed for today. It's designed for a long time ago. I think that's problem number one.

00:04:35.780 Problem number two, I think is that it's kind of like walking, right? Like you've been walking your

00:04:42.640 whole life and it would never occur to you that you should take a class on how to walk.

00:04:46.660 And everybody is making, been making decisions their whole lives. And so the idea, I think that

00:04:53.880 you would maybe be bad at that, that it would be really good if you had a class that really taught

00:05:00.120 you how to make a good decision. I don't think that it's really intuitive. I don't think parents in

00:05:04.940 general think they're poor decision makers. I think that they probably think they're pretty qualified

00:05:08.820 to teach their children. And even when you look at the history of science, it wasn't until,

00:05:14.720 you know, Conor Tversky and Richard Thaler came along and people like Barry Stahl,

00:05:19.460 where they started saying people aren't perfectly rational. If you give people the information

00:05:26.180 that they need and let them make decisions, they actually aren't going to make decisions that are

00:05:30.680 necessarily really rational. And the ways that they're irrational are actually quite predictable.

00:05:35.760 And this was, you know, in the seventies, this was heresy within science and economics.

00:05:42.720 Up until then, the assumption was, you know, a rational actor. So we didn't really even start to

00:05:49.500 figure out the ways in which people are bad at decision-making until the seventies. And then it

00:05:55.940 wasn't really widely accepted until, gosh, you know, it started to gain some traction into the eighties

00:06:01.180 and nineties. And then obviously in the last two decades, people have really become wise to this.

00:06:06.460 And then you have, you know, I think it was 2014 that Thinking Fast and Slow came out and then the

00:06:12.240 general public really started to get it. So we've been pretty behind the curve on this. And so the fact

00:06:17.560 that it hasn't gotten into the school system is maybe not that surprising. So that's kind of what we're

00:06:23.680 trying to do at the Alliance for Decision Education is kind of catch K through 12 education up with where

00:06:29.560 the science is and actually where business is, because business has really accepted that this is

00:06:34.840 something that they need to work on. Well, let's talk about some of these tools you highlight in

00:06:38.540 the book. And the first one, we talked about this a bit in Thinking in Bets, but I think it's a really,

00:06:43.520 when I learned about this concept, it really, it's changed the way I think about how I interact

00:06:48.900 with the world and how I think about the world. And it's this idea of resulting. So what is resulting?

00:06:54.220 And then how does that get in the way of us making good decisions?

00:06:59.980 Yeah. So it's, that is a concept that has really taken hold from Thinking in Bets. And I'm quite

00:07:06.520 pleased because I think it's a really important concept for, to start understanding kind of where

00:07:10.980 our decision-making goes wrong. So what we want to think about is like, how do we actually learn to

00:07:16.160 become good decision-makers? And it seems obvious that the way that we do that is from experience.

00:07:20.540 So, you know, you make decisions, you get outcomes of the decisions, and then you sort of tie those

00:07:26.340 feedback loops together, then that helps you become better at making decisions. That would be

00:07:31.040 what one would hope. But resulting actually really gets in the way. So this is what resulting is. It's

00:07:36.860 basically what it sounds like. You look at an outcome and depending on the quality of the outcome,

00:07:42.560 was it good? Was it bad? Did you win? Did you lose? You then use that outcome, the quality of the

00:07:48.580 outcome, to work back to the quality of the decision. So the decision that I opened Thinking in Bets with

00:07:56.420 is Pete Carroll in the Super Bowl in 2015. He's obviously not playing, he's coaching. And he's

00:08:03.340 against the Patriots. And there are 26 seconds left in the game. So obviously it's fourth quarter.

00:08:10.960 They're on the one-yard line of the Patriots. It's second down. They have only one timeout. This is

00:08:17.060 actually a really difficult situation for them because they're down by four. So they need to be

00:08:21.360 able to score a touchdown. They can't just kick a field goal. They obviously have three downs that

00:08:25.880 they could do that in, second, third, and fourth down. But they only have 26 seconds left. So this is

00:08:31.700 quite a hard problem here because you have a clock management problem, given that you only have one

00:08:35.460 timeout. So everybody expects Pete Carroll to have Russell Wilson hand the ball off to Marshawn Lynch.

00:08:41.020 Great running back. He doesn't do that. He has Russell Wilson pass. Russell Wilson passes to the

00:08:46.560 right corner of the end zone and the ball is intercepted by Malcolm Butler. And everybody goes

00:08:51.720 nuts that this is absolutely the worst play in Super Bowl history. In fact, USA Today, the headline

00:08:58.220 that they had the next day was that it was the worst play call in NFL history, in all of NFL history.

00:09:03.600 Now, this is a really classic case of resulting because what you can do, I mean, if you go look

00:09:10.080 at any of the articles that were written at the time, like the USA Today article, for example,

00:09:14.120 it's pretty statistics free. So it doesn't tell you kind of what you need to know in order to determine

00:09:20.160 whether that was a good decision or not. Things like how likely was it that Marshawn Lynch was going

00:09:25.700 to score? Or more importantly, how likely was it that the ball was going to get intercepted?

00:09:29.540 But I could tell you those things. Marshawn Lynch was going to score about 20% of the time. It's

00:09:35.780 actually lower than people think it was. The ball was going to get intercepted less than 2% of the

00:09:40.680 time. But I don't really need to do that. All I need to do is do a thought experiment with you,

00:09:45.500 which is imagine it's the same situation, 26 seconds left in the Super Bowl against the dreaded

00:09:51.560 Patriots. They're on the one yard line down by four. Pete Carroll has one timeout, does this really

00:09:57.560 unexpected thing? And he calls for a pass play and the ball is actually complete for the game-winning

00:10:03.840 touchdown. So they catch the ball, game-winning touchdown. I'll just ask you, like, what are the

00:10:09.360 headlines look like the next day? Greatest play, gutsy, amazing. That's right. So all of a sudden,

00:10:16.540 weirdly, USA Today doesn't say it's the worst play in Super Bowl history. So in both cases,

00:10:22.140 whether the ball is complete or intercepted, people make an assumption about what the decision

00:10:29.360 quality is. If it's intercepted, they say the decision quality is terrible. If it's complete

00:10:35.220 for the game-winning touchdown, defeating the Patriots and denying them their fifth Super Bowl

00:10:40.500 ring at the time, you know, then it's the greatest play in Super Bowl history. And that's why he's going

00:10:45.380 to go to the Hall of Fame. But here's where we can see that this is an error because the decision is

00:10:51.640 the decision, right? There's math that goes into it. I told you a little bit about it. Marshawn Lynch

00:10:56.960 is only going to score about 20% of the time. Remember, he's in a compressed part of the field

00:11:01.060 on the one yard line. So there's a lot of Patriots there to stop him. The ball is only going to get

00:11:05.940 intercepted less than 2% of the time. There's some other things that go into that. Like if you pass,

00:11:11.360 you're more likely to get three plays off instead of two, which someone would assume you'd like against

00:11:15.920 the Patriots. And those are the things that we should care about as we're trying to determine

00:11:20.260 what the quality of the decision is. But the problem for us as decision makers is that if I

00:11:26.140 were to go through that, and I just went through a little bit of it, but if I were to go through the

00:11:29.480 whole thing, it's very complicated, right? Like you have to understand probability, statistics and

00:11:35.320 probability, what that does in terms of win probability, depending on the choice that you make. You need some

00:11:40.120 options theory in there so that you can understand, you know, what the value is of having the extra play

00:11:45.680 and how you might actually get to that. So there's a lot of conditionals in there as well. It's just,

00:11:51.060 it's complicated. And this is the sort of what we face when we're trying to look back on our

00:11:56.740 decisions. It's complicated. That's whether it's the Super Bowl and the last play of the Super Bowl

00:12:02.800 or trying to hire somebody based on just a CV, a few interviews and some references. These things are

00:12:08.880 very complex. So in order to simplify, what we do is we say, well, I know what the result was.

00:12:16.720 The ball was intercepted. So therefore it must've been a terrible play. I know what the result was.

00:12:22.580 It was the game winning touchdown. Therefore it must've been a great play. And you can see why it

00:12:27.660 has to do with that sort of how complex getting to the decision quality is because we don't do this

00:12:33.420 kind of resulting as much when the decision quality is really clear. Like if I go through a green light

00:12:39.480 and I get in an accident, you don't tell me going through a green light was a bad decision.

00:12:44.740 But that's because it's super, like we've already decided that. It's the rules of the road, right?

00:12:51.120 This is getting into two plus two equals four, as opposed to some kind of, you know, strange,

00:12:55.580 like linear algebra or something like that. Right. So, so it's a way that we kind of simplify

00:13:02.120 the world when we shouldn't, that really messes our decision-making up.

00:13:06.440 Gotcha. So what resulting does, it prevents you from learning whether you're actually making good

00:13:10.320 decisions. Like you could be making a decision that's terrible. Like the process, what you're

00:13:14.680 thinking about is just absolutely terrible, but you get good outcomes because of just plain dumb luck.

00:13:20.540 But you think to yourself, well, I'm making a great decision. And really eventually it's going to,

00:13:25.360 biting the butt, but you'll never know that because you're just looking at the outcome. 0.96

00:13:29.820 Yeah. It's, it's actually, I would say it's actually worse than that. So you do some things, 0.97

00:13:36.600 you get some great results from it. You decide that the decisions that led to that great result

00:13:43.780 were amazing. Then you do those things again, and maybe now you don't get such great results from it,

00:13:50.360 but now you get caught in motivated reasoning where you start to say, well,

00:13:55.180 I know the decision quality was good. So this must just be bad luck because it would be really,

00:14:02.100 really hard for you to think that the success that you'd had in the past from the decision-making

00:14:06.400 that you'd done previously was not actually because you make great decisions. That doesn't

00:14:11.800 really fit well with the way that we want to think about ourselves. And we want to think about

00:14:16.700 ourselves in a positive way. So what kind of happens is that then part of what happens with

00:14:22.020 motivated reasoning is that we'll start to sort of look for reasons that we can maintain that our

00:14:27.440 decisions were good. And this becomes really problematic as we're thinking about ourselves.

00:14:33.860 So when we look at other people, we do pretty straight resulting, which is if there's a good

00:14:38.360 outcome, it's from a good decision. If there's a bad outcome, it's from a bad decision. But when we're

00:14:43.040 actually thinking about ourselves, we have this real need to maintain a positive self-image. And part of

00:14:48.900 that obviously is I'm a good decision-maker. I do good things and bad things aren't my fault.

00:14:53.920 And because of the presence of luck, now we get into trouble, which is we have some success. We believe

00:15:00.320 that our decisions were good. Maybe they're very low quality though. And eventually they're going to,

00:15:04.720 as you said, bite us in the butt. But when they start to bite us in the butt, we start to blame 0.99

00:15:09.040 other factors. 0.99

00:15:12.600 Okay. So with resulting, we judge the quality of our decision based on its outcome. And part of

00:15:17.480 overcoming that is trying to objectively separate out what was actually luck and what was skill in the

00:15:22.520 decision. But then there's another bias connected to resulting, which is hindsight bias. And that's

00:15:28.120 where you think the outcome of something was more predictable than it was. And your memory actually

00:15:33.280 can get distorted. So when you're looking back on something, you think you really knew all along

00:15:38.540 how it was going to turn out. And there's a tool you suggest using called knowledge tracking that can

00:15:43.480 help with both of these things. So can you walk us through knowledge tracking?

00:15:47.980 When you're thinking about a decision, actually think to yourself, what did I know at the time of

00:15:53.220 the decision? What revealed itself after the fact? Those things that revealed themselves after the fact,

00:15:58.120 were they knowable beforehand? If they were knowable beforehand, you still aren't done though.

00:16:04.460 You want to move on to two other questions. Could I afford to get it?

00:16:07.760 So, you know, as an example, I think I have an example in the book. You've only lived in the

00:16:13.820 South your whole life. You get offered a great job in Boston and you're trying to decide whether

00:16:17.980 to move there because you're concerned about whether you like the weather. You go up for a

00:16:22.380 couple of days in February just to kind of check it out. Doesn't seem so bad. The job's a great

00:16:28.320 opportunity. So you move there and it turns out that you hate it. Your first winter, it's just like

00:16:34.280 brutal and you end up moving back to the South. So this would be a good example where knowledge

00:16:40.780 tracking would be really helpful. Like what did I know at the time? Well, I knew that New England had

00:16:45.840 bad, you know, winters. I wanted to kind of try to figure out if I liked it or not. So I went up there

00:16:53.020 for a couple of days in February. It didn't seem so bad. And so I took the job. What revealed itself

00:16:58.000 after the fact? Well, it turns out that when I had to endure a whole New England winter, I hated it.

00:17:02.880 So now you can say to yourself, was that knowable beforehand? The answer is yes.

00:17:07.100 Right? Like I could have gone and spent a winter up in New England before deciding,

00:17:11.960 except that I couldn't afford to do that because the job wouldn't have still been available to me.

00:17:17.640 So that's just kind of like, then you just sort of shrug your shoulders and notice that that's a

00:17:22.400 case where hindsight bias really happens, where like your friends are going to be like, I knew you'd

00:17:26.040 hate it there. And you're going to say to yourself, I should have known I would have hated it.

00:17:29.700 But this is actually a really helpful tool to sort of get you away from that. Because what you can

00:17:35.100 see is, well, of course I couldn't have known that I would hated it. I hated it because I couldn't

00:17:38.840 have been up in New England for a whole winter to be able to find it out. Then you can ask yourself

00:17:45.240 the next question, which is, even if I couldn't have known it beforehand, either because it wasn't

00:17:49.760 knowable or because I couldn't afford to go find this information out, could I use that in my

00:17:56.680 decision process going forward? And then another tool that you can use, and this is in retrospect,

00:18:02.020 is actually to try to recreate for yourself, what are the possible things that could have occurred?

00:18:08.740 Right? So if you're thinking about, for example, like the job in Boston,

00:18:13.000 to actually go back and try to get yourself away from that feeling of inevitability that resulting

00:18:17.700 creates and hindsight bias creates that obviously it was going to work out horribly. And so therefore,

00:18:23.580 I should have known it, which is kind of what ends up happening. And instead say, let me try to

00:18:28.340 remember, like at the time that I was thinking about the job, what were all the different ways

00:18:32.020 it could have turned out? Right? And you can think like, I could have loved the job and become a

00:18:36.640 winter nut who then goes skiing. I could have hated the job, but loved Boston so much that I ended up

00:18:41.440 staying in Boston and I found another job. Right? I could, you know, I could, it could have been okay.

00:18:46.920 Like the job could have been okay and Boston would have been fine. And I could have spent a few years

00:18:51.320 there and ended up, you know, moving back kind of on my own terms. I could have loved the job,

00:18:57.040 but hated the weather so much that I left, or I could have loved the job, but hated the weather,

00:19:01.220 but felt it was worthwhile to stay. You know, and we can see that once we start to do that,

00:19:05.520 we start to realize like, no, the thing that happened wasn't inevitable. There were all sorts

00:19:10.660 of ways that this could have turned out. So that's kind of like, it's, those are sort of the first

00:19:15.220 three chapters of my book, you know, of how to decide the follow-up or trying to help you with

00:19:20.300 these retrospective problems. How do I look back on a decision and actually start to dig down

00:19:26.040 into the decision quality without falling into these traps where I start to, you know,

00:19:32.060 do resulting or hindsight bias or whatever. And hopefully those tools are pretty clear.

00:19:36.220 The knowledge tracking tool in particular, I think it's quite powerful.

00:19:39.200 Yeah. I like that. I actually am going to start doing that now that when I'm making a decision,

00:19:42.500 like here's the information I'm using to make that decision, like actually write that down explicitly.

00:19:47.220 Yeah. So that actually brings up a really good point. If we're looking at something

00:19:50.020 in retrospect where we don't have any record of what we thought at the time or what the

00:19:55.540 information that we had was, or what the process that we went through, who we talked to, who we

00:20:01.400 asked for advice, what they thought, any of that stuff, that then becomes really hard. As we go back

00:20:06.260 and try to do these reconstructions, you know, we, we try to do this knowledge tracking and try to

00:20:11.200 figure out what did we know at the time, or we tried to, we try to reconstruct sort of these simple

00:20:16.520 decision trees of like, here's the decision I'm thinking about what are the different outcomes that

00:20:20.300 could occur. It's just hard to do in retrospect. So the big lesson of that first section of the book

00:20:25.440 is do it beforehand. That a really great decision process is going to have you explicitly creating

00:20:33.060 some sort of evidentiary record of what your beliefs are, what your rationale or thesis is for the reason

00:20:39.720 that you're thinking about doing something, what you think the different outcomes might be, how likely

00:20:45.500 you think those are. It's going to have you interacting with other people to get their

00:20:50.060 viewpoint on it in order to improve the quality of the knowledge that's going into the decision

00:20:54.880 that you make. And that's all going to, you're going to have a record of that so that when you do get

00:20:59.600 the results, you can look back and you can say, what was my rationale? What did I believe at the

00:21:05.680 time? What was the information that I had? What did I go find out? And then you can actually ask

00:21:11.640 yourself these questions in a much clearer way that's going to allow you to close these feedback

00:21:15.440 loops in a more objective way. It won't be completely objective, but it's going to be a lot better

00:21:19.560 than it would be otherwise. And this now allows you to create really good learning loops that's

00:21:25.700 actually going to improve your decision-making going forward in a much, much faster way. It's going to

00:21:30.980 do a lot more heavy lifting. We're going to take a quick break for your words from our sponsors.

00:21:34.900 And now back to the show.

00:21:38.940 So we've talked about analyzing past decisions so we can make better decisions, but then you also

00:21:42.740 lay out like, how do you make a good decision that you haven't made yet? And you basically lay out

00:21:48.160 this process where you look at preferences, payoffs, and probabilities. You make a decision tree. So

00:21:52.860 what you do is you say, here's my decision. And then you are going to list out reasonably all the

00:21:58.680 potential possible outcomes. So like, you know, you can't, obviously you're not going to be able to do

00:22:03.020 every single possible outcome, but reasonable outcomes. It's like the example you gave of

00:22:07.080 moving from the South to Boston for a job, you know, possible outcomes. And you had to look at

00:22:12.220 the, the, the payoffs, the upsides and the downsides, like the pluses and the negatives. So if you move

00:22:16.780 to the South to Boston for this job, it could be, you love the job, you love the weather, you hate the

00:22:21.660 job, you love the weather, you love the weather, hate the job, you hate the job. I mean, just that's

00:22:26.680 we're kind of doing it. So you're going to do that. But I thought the really interesting part

00:22:30.680 of this process is figuring out probabilities and humans are really bad at this for the most part,

00:22:36.520 describing probabilities. So like, okay, how do you do that? So like, how do you figure out

00:22:40.620 with the decision of, you know, moving from the South to the, to Boston, like the probability of

00:22:47.240 whether you're going to love the job and you'll love the weather or love the job? Like, how do you

00:22:52.460 assign something for something you don't even know? Yeah. So this actually brings up something

00:22:57.160 really deep. I mean, the answer is you guess. And it, it takes a particular type of mindset to

00:23:05.080 be willing to do that. And the mindset is to say, look, no guess is ever like super random,

00:23:13.940 pretty much about anything. It's about figuring out when we think about that, you know, we have that

00:23:19.480 distinction between a guess and an educated guess. It's about how much educated can I get into it?

00:23:26.100 Because if I can, if I can get a little bit more educated into the guess than I would have, if I

00:23:31.500 hadn't tried, that actually going to really improve my decision-making. So you can kind of, so any

00:23:36.760 decision is really just, just a prediction about the future, right? If I'm thinking about moving to

00:23:41.100 Boston, I'm making some predictions about what's going to make me happiest in the future.

00:23:44.540 Now, the future is always going to be cloudy to us as mere mortals, but if you're willing to make

00:23:50.500 some guesses, then you can get to a point where it's less cloudy. And even though it's not going

00:23:56.520 to be a perfectly clear picture where you're going to know exactly, less cloudy actually does a lot of

00:24:03.980 work. It gets you pretty far. So let's, let's think about what do I mean by this, that all guesses

00:24:09.100 are educated guesses, that there really isn't anything where you should say, like, I don't know,

00:24:15.120 period, right? It should be, what do I know is really what you should be asking. So I'll give you,

00:24:20.220 I'll give you an example. Okay. So I have a new puppy. He's sleeping next to me. You've never seen

00:24:30.860 this puppy, correct? Correct. Okay. How much do you think this puppy weighs?

00:24:35.800 Um, 15 pounds. Okay. And how, what's the lowest amount you think the puppy weighs?

00:24:42.600 Five pounds. And what's the most amount you think the puppy weighs? 25.

00:24:47.520 Okay. So that's great. Uh, so you can't see the puppy. We're not on a video system. I haven't

00:24:54.220 showed you a picture of the puppy. You don't even know what breed the puppy is. Don't know. Yeah.

00:24:58.300 You don't know how old the puppy is. You just know it's a puppy. Right.

00:25:01.600 So, you know, a little bit of something about how old it is, but not a lot. And you just gave me a

00:25:05.700 really good guess. And by the way, your lower bound and upper bound captured exactly the weight.

00:25:09.620 So you gave a lower bound of five and an upper bound of 25. He's 10 pounds.

00:25:13.960 Okay. So you actually did your job. You found the right answer. It was in that range.

00:25:18.400 Now, was your point forecast of 15 exactly right? No, but it was pretty darn close when you think about

00:25:23.540 the weights of all things, right? Like if you thought like, what's the full range of things

00:25:29.020 could weigh it's zero to, I don't know how much, how much does the earth weigh, right? Like millions

00:25:34.960 of pounds, trillions of pounds. So what we've just discovered is that when I asked you to guess at the

00:25:41.060 weight of this dog, even though you've never seen the dog, you started, I assume to recruit,

00:25:46.640 well, what do I know about puppies? Right? Like, well, it's a puppy. So would she describe a dog

00:25:53.720 that's over six months as a puppy? Maybe, but probably not. I just got the puppy. So that means

00:25:59.980 the puppy's probably young, right? How much do puppies in general weigh? How much do dogs in general

00:26:05.940 weigh? How much do they compare to other things? And even if I asked you something like, which I assume

00:26:11.880 you don't know the answer to, maybe you do, like what's the distance between the earth and Jupiter?

00:26:17.260 Yeah. I have no clue. I would say, uh, 2 million miles.

00:26:22.260 Right. So lower, lower bound and upper bound. Yeah. Lower bound 2 million, upper bound 10 million.

00:26:27.580 Okay. So, um, I would probably, so I know a little something more than you do, right? So I know that

00:26:35.160 the sun is 93 million miles away. Oh, wow. Right. But notice though, even so you knew it had to be in the

00:26:43.300 millions. So did you get it exactly right? No, but, but you cleared away a lot of the possibilities

00:26:50.680 because you understood that it at least had to have a million in front of it.

00:26:55.280 And that's, that's actually a really big improvement over not having tried it at all. So when you think

00:27:04.100 about like, what's the likelihood that I'm gonna, you know, enjoy the weather in Boston?

00:27:11.900 The answer isn't, I have no clue because you know things about yourself. You've experienced some

00:27:21.820 cold weather, right? Maybe you went up there in February for a couple of days. So you tried to

00:27:28.420 get some more educated into the guests and you didn't think it was so bad. So, you know, when you go up

00:27:35.140 there, you recognize like, okay, like, um, I think that I'm probably likely enough to like it, that I'm

00:27:42.120 willing to do this. And are you going to get an exact answer? Of course not. But if you can get close, it

00:27:48.880 matters. So I talk about this as like the archer's mindset that we're really focused on the bullseye and we

00:27:55.720 feel like we've got to get to the bullseye. And what that causes us to do with is either claim we

00:28:00.180 have the bullseye when we don't, right? Or not try at all because we recognize we won't be able to hit

00:28:05.540 it. And instead we need to have more of an archer's mindset to say in archery, you have a target.

00:28:12.040 And while you might be aiming at the bullseye, you're, you also get points for hitting the target.

00:28:17.260 And so we want to realize that in decision-making, we get points for hitting the target.

00:28:21.100 And then beyond that, we get a lot of points for actually defining the size of the target

00:28:26.980 because that does a lot of work for us. So like in the puppy example, you said your lower bound was

00:28:31.100 five pounds and your upper bound was 25 pounds for the weight of this puppy, right? Well, that tells me

00:28:36.520 something about how uncertain you are. When I asked you for the lower bound on Jupiter and the upper

00:28:41.800 bound on Jupiter, this was a much wider range. Why? Because you're less certain, you have less

00:28:47.280 knowledge about the relationship between earth and Jupiter than you do between, you know, the lower

00:28:54.060 bound of the weight of this puppy and the upper bound of the weight of this puppy, which, which you

00:28:57.240 know a lot more about. We can take that to the extreme. If I asked you, what, what's your birthday?

00:29:03.100 You would give me the bullseye because you know it.

00:29:07.760 Gotcha. So yeah. So you know more than you think you do.

00:29:10.660 Right.

00:29:11.580 Gotcha. All right. So yeah, we, so you sign a probability and you even recommend like, don't just,

00:29:16.260 don't just settle for likely or not likely, like actually put a percentage on it. Cause that'll

00:29:21.060 make things more concrete for you.

00:29:23.680 The reason why we don't want to use words that, so there's all these words that describe probability

00:29:29.600 that we use every day, like likely, always, never real possibility. Like we say that, like,

00:29:34.520 do you want to go out to the movies this weekend? Yeah. I think that's a good possibility.

00:29:38.220 Right. Like, so, so that would be a way that we throw this around, but you, you can think about,

00:29:42.220 we do this like in a hiring process, right? Like, what do you think of that?

00:29:46.240 I think there's a good possibility. They'd be great. So we use these kinds of terms all the

00:29:50.960 time. Well, the reason why we don't want to is kind of twofold. One is that we want to be able

00:29:55.860 to circle back and actually close those feedback loops. And if we use these kinds of mushy terms,

00:29:59.680 it's hard for us to do that because the, these terms have pretty broad meanings. And when you

00:30:04.640 actually ask people, like if you survey people on something like real possibility, which is a term we

00:30:09.200 use a lot and you say, Hey, you know, when you use that word, like what probability do you actually

00:30:15.040 intend? You get a range from about 15% to 90%. So that should be the first clue that there's a

00:30:23.420 problem with those words. So the first problem has to do with that feedback loop is that when you go

00:30:27.180 back and you try to close your own feedback loops, and you've said that something's a real

00:30:30.720 possibility, you can kind of mush around in there in order to motivate a reasoning is going to get

00:30:35.540 there. Right. Because you can say, well, I said it was a real possibility. I didn't say it was like,

00:30:41.060 so if like, for example, if I hire someone and they turn out to be great, I get to say, yeah,

00:30:47.960 I told you it was a real possibility. That would be great. And then if I hire someone and they turn

00:30:52.480 out to be poor, I get to say, well, I told you it was a real possibility. They'd be great, but I

00:30:55.980 didn't say it was a hundred percent. So, so, okay. What does that help you with? So that's kind of

00:31:01.820 problem. Number one, that it helps you, doesn't help you with problem. Number two, that it doesn't

00:31:06.080 help you with is actually a very broad problem in decision-making, which is, so when we think about

00:31:12.160 problems like confirmation bias, which is just, I notice information that confirms the things I

00:31:17.960 believe to be true. And I don't notice information that disconfirms the beliefs that I have or something

00:31:24.680 like availability bias, which is that I judge things to be more frequent that I have interacted with

00:31:30.160 quite a bit or that are more vivid for me to recall. So when we take those, what you can see

00:31:34.880 is those are all me problems, right? They're all things that have to do with me trying to affirm my

00:31:40.040 beliefs or quirks of my own memory or my own experiences, the way that I've interacted with

00:31:45.280 the world and what my motivations are about the way that I'm reasoning about the world. So Kahneman

00:31:48.940 would call this the inside view, that when we're reasoning about the world, we reason about it from the

00:31:55.120 inside view. In other words, driven by our own experiences, our own knowledge, our own

00:31:59.560 perspectives on the world and the mental models that we sort of, you know, apply to the world. And

00:32:05.060 that's actually where the most of the bias is living, is in the inside view. So the antidote to

00:32:11.600 the inside view is the outside view, which is essentially one of two things. It's what's true

00:32:17.000 of the world in general. So an example of that would be a base rate, which is just how often does

00:32:22.100 something happen in a situation similar to the one that I'm considering? So I'll give you an example of a

00:32:27.660 base rate. So let's say it's when coronavirus doesn't exist yet, and you're thinking about

00:32:32.360 opening a restaurant in a certain area, and you think that you have a 90% chance of being successful

00:32:38.440 by the end of the first year. So that would be your guess inside view. You think pretty well of

00:32:44.360 yourself. You probably have some overconfidence. You're probably cherry picking some data that's kind

00:32:50.640 of getting you to that conclusion. Again, not on purpose, but because that's what we naturally do.

00:32:55.760 But the base rate, this would be getting to the outside view, would be to say,

00:32:59.860 well, how often do restaurants succeed within the first year in general in my area? And if you look

00:33:08.580 that up, what you would find out is that the percentage of restaurants that are open after

00:33:13.000 the first year is 40%. So we can see how that helps to discipline the inside view. If I think it's 90%

00:33:20.140 and the world says it's 40%, I ought to rethink my 90% number. So that's one way to do it. But

00:33:26.700 another way to do it, and this is a great way to do it, is to actually get other people's perspectives

00:33:31.400 on your situation. Because other people can be looking at your situation, and they can think

00:33:36.660 very different things than you do about it. This is even if they have the exact same data,

00:33:41.160 they may model the data differently than you. This is even if they have modeled the data exactly the

00:33:47.340 same as you, they may think that you're supposed to do different things about it, given what the

00:33:52.040 model tells you. So it's really, really good to get other people's perspective on the situation that

00:33:58.420 you're considering, on the decision that you're considering. Well, in order to do that, you have

00:34:03.780 to actually communicate clearly to other people what it is that you think, what it is that you believe

00:34:12.060 to be true of the world. And this is where terms like real possibility really become a problem.

00:34:17.420 Because if I tell you that something is a real possibility, it's so unclear that we know what

00:34:25.960 that means, right? You might think it means 20%, and I might think it means 60%. And we could think

00:34:32.420 that we totally agree that it's a real possibility this candidate could do well. And you may be thinking

00:34:37.820 it's a 20% chance, and I may be thinking it's a 60% chance, and we actually disagree. But we can't

00:34:43.640 find it out because we haven't expressed what we believe with any type of precision. So that's where

00:34:51.420 this idea of giving essentially a bullseye estimate, right, which would be like, I think it's a 55% chance,

00:35:00.480 and then giving a lower and upper bound now becomes really valuable because it's what gets people

00:35:05.860 involved in the conversation, right? So if I give the bullseye estimate, like I think it's a 55%

00:35:12.100 chance, you know exactly what I mean. And you know whether you agree with that. And then when I give

00:35:17.160 the lower and the upper bound, I tell you how certain I am about it. So I'm giving some sense to

00:35:22.200 you of what my target area is. And what's really wonderful when you do that, when you say, well,

00:35:28.400 I, you know, I think the puppy is 15 pounds with a lower bound of five pounds and an upper bound

00:35:33.840 of 25 pounds is that you have said very clearly what your beliefs are in a way that you have

00:35:38.560 actually invited me into the conversation. Inherent in that lower and upper bound is a

00:35:45.620 question of, can you help me with this? I'm telling you how much certainty I have. I've obviously thought

00:35:51.860 about it. I am telling you something that's quite precise. And is there a way that you could help me

00:35:58.360 narrow the range? And that creates really great decision-making, great conversations, because you

00:36:04.440 actually are very clear about what the conversation is and you're maximizing your access to the

00:36:09.460 outside view by doing so. So this is a pretty involved process. And I imagine when you first

00:36:14.680 start doing it, it'll take a long time, but I imagine the more you do it, you kind of, it becomes

00:36:18.880 like a skill, it becomes like intuitive. So we've talked about how to improve the quality of our

00:36:23.500 decisions. The other issue with decision-making that people have is the amount of time, like the

00:36:29.340 bandwidth people spend on making, they just agonize paralysis by analysis. But one of my favorite

00:36:34.480 sections, you give some sort of like hacks to short circuit that analysis by paralysis. Can you share a

00:36:41.400 few that you think are really powerful that people can start using today and actually see a profound

00:36:45.620 change in how much time they're spending on decisions? Yeah, absolutely. So yeah, so I want to say

00:36:51.380 like when I talk about this stuff and I say like, oh, you know, you should build out these decision

00:36:55.020 trees and you should do this knowledge tracking and you should be, you know, thinking about the

00:36:59.560 probability of different things happening. You know, the response is like, how am I ever going to make

00:37:03.200 a decision again? This is going to make me go so slow. And I just want to remind people that I was a

00:37:09.140 poker player. And obviously at the poker table, you have to, you know, you're making decisions very,

00:37:15.300 very quickly and you're iterating them a lot. I obviously don't think that you need to go really slow

00:37:20.640 on every single decision. So a couple of things on that. One is that if you do understand what a

00:37:24.960 robust decision process looks like, this is actually going to help you speed up your decisions

00:37:29.080 because you're going to be able to hone in on the things that matter instead of spending your time

00:37:33.040 spinning your wheels, thinking about things that don't matter. So this just is more efficient because

00:37:37.580 it tells you what you should care about. Number one. Number two is kind of like with riding a bike,

00:37:43.380 you have to kind of understand it in a slow way or driving a car. Like this is what the gas pedal

00:37:47.680 does. And this is what the brake pedal does. And if I turn the steering wheel this way,

00:37:51.700 this is what happens. And before you can actually put that into a more, you know, automatic, quicker

00:37:57.720 process. So understanding what a really robust decision process looks like will tell you what

00:38:04.520 the heart of the matter is, but it will also help you to speed up just because you kind of understand

00:38:08.480 what it would look like in its fullest form. But as far as most of the decisions that you're making,

00:38:15.220 generally, we kind of a little bit get it backwards. With some decisions that we should

00:38:21.000 be taking like quite a bit of time on, we'll often just go really fast. I think partly because we know

00:38:26.440 it's complicated. And so we sort of give up in that sense of, I don't want to guess because I don't

00:38:30.320 know. And so therefore I'm just going to go with my gut. And there's a wide variety of decisions where

00:38:34.900 we actually go quite slowly, where we should actually be speeding up. And it's because we're not

00:38:40.820 thinking about the type of decision that we're facing very well. So in order to figure out when

00:38:46.000 you can go fast, what you're essentially figuring out is, if I go really quickly, the time that I'm

00:38:55.040 saving comes at a cost. And that cost is that I'm probably going to increase my error rate.

00:39:01.320 So the decision is just going to be less exact. It's going to be less accurate.

00:39:04.920 And if I increase my error rate, that means that I may get a bad outcome more often than I would

00:39:10.600 have if I would have taken more time. And so that once we sort of understand that, that there's this

00:39:14.820 trade-off between time and accuracy, then we can figure out when we can go fast and when we should

00:39:22.200 slow down. And it's when we can tolerate higher probability of a bad outcome. So let's think about

00:39:30.040 when we can do that. Because that's kind of the broad framework that we want to think about it

00:39:33.640 through. And we can think about this through two things. One is impact and one is option.

00:39:37.480 So I'll give you an example of a type of decision that people take a very long time with. And I'm

00:39:42.560 sure you've experienced this. So when we all used to go to restaurants, you probably know the person

00:39:48.200 who would sit with the menu. And they'd be like staring at the menu, asking the waitstaff for their

00:39:57.080 opinion, asking every single person at the table what they were going to order, agonizing over it.

00:40:02.800 And then once the waitstaff came over to take

00:40:05.640 the order, they'd be like, let me go last. Do you know that person?

00:40:10.580 Of course. Yes.

00:40:12.160 Is it you maybe?

00:40:13.600 No.

00:40:14.080 You did philosophy. I'm guessing you go faster.

00:40:16.780 Yeah, I go really fast. I just don't care.

00:40:18.920 Yeah. So, but you know that person and those people are very, very common.

00:40:23.080 So it turns out that actually, if you look at the statistics, that when you take together what to

00:40:29.480 wear, which I think is probably a faster decision in the pandemic, because it's like sweatpants and

00:40:32.940 then something that looks decent on top, but whatever. But what to wear, what to watch on

00:40:38.300 Netflix and what to eat, that people are taking about six to seven hours of work week time per year

00:40:45.200 on those decisions. That's pretty surprising.

00:40:49.960 Right. That's a lot of time.

00:40:51.320 That's a lot of time. And I believe that the reason that they do that actually is related to what we

00:40:57.160 talked about with, you know, why people sort of guess in these situations where it's really

00:41:02.520 complicated or are unwilling to guess, they just go with their gut. And it's because when you're

00:41:06.940 thinking about something like ordering off a menu, I think it feels very solvable, right?

00:41:11.480 In kind of the know thyself sense, right? Like you should be able to figure out what your own

00:41:15.300 preferences are. And you know a lot about food. And if you just asked the wait staff and you looked

00:41:21.220 at a few more pictures of the dishes on Yelp, that you should be able to get this decision.

00:41:25.680 And I'm going to put it in air quotes, right? Because it feels like a pretty simple decision

00:41:30.400 that's about your own preferences and you should be able to get it right. And you're kind of in fear

00:41:35.100 of, you know, that moment where you've tried to decide between the chicken and the fish and you

00:41:41.720 get the fish and it's yucky. And you, what do you say when that happens immediately? I made a mistake.

00:41:48.480 I should have ordered the chicken. That's what you say immediately. But if we go back to that

00:41:53.560 hindsight bias and resulting problem, that's just hindsight bias and resulting,

00:41:58.280 right? Because obviously less a time machine, there's no way for you to know that that fish

00:42:03.520 wasn't going to be very good. What you knew is that you like chicken and fish. They both seem

00:42:07.540 pretty good to you. You looked at the preparations and whatever. So it's weird to call that a mistake

00:42:14.400 when the food comes back poorly. And it's weird to say, I should have ordered the other thing,

00:42:19.800 or I should have known to do that. But that's what we do. So we're thinking very short term.

00:42:23.700 So what we want to do is actually think about what is the long-term impact of that decision going

00:42:29.360 awry. So I'll just ask you. So you get crappy fish. We have a meal and the fish is yucky.

00:42:37.020 And you're sad because it was gross. And now I catch you in a year. So it's a year later.

00:42:42.700 And I say to you, Brett, how's your year been? And I'll just ask you that. How's your year been?

00:42:48.960 It's been, all things considered, it's been pretty, it's been all right.

00:42:52.580 Yeah. All things considered. That's, yeah, that's my answer too. All things considered,

00:42:56.100 it's been okay. So that, do you remember that fish that you had when we were in that restaurant

00:43:00.280 a year ago and it was kind of gross? I don't even remember that.

00:43:03.740 Right. Does it have any effect on your happiness today?

00:43:06.480 No, not at all.

00:43:07.640 Outweighs the pandemic, right?

00:43:09.140 Right. So, right. So what if I catch you in a month?

00:43:14.920 Same thing. I would have been like, we had, I don't remember. I would have forgotten about it

00:43:19.040 or I just wouldn't even been thinking about it. Yeah.

00:43:21.240 Or how about a year? I mean, how about a week? I mean, you've had 21 meals since then.

00:43:26.200 No, wouldn't even have been thinking about it. No, no. If it was really expensive, I still,

00:43:32.280 I might be a little, uh, having a little, yeah, miffed about it.

00:43:36.720 Okay. So, so this, this particular exercise of how would I, you know, does it affect my

00:43:41.820 happiness in a year? Does it affect my happiness in a month? Does it affect my happiness in a week?

00:43:45.880 It's called the happiness test. And the reason that we want to do this, this is a way for us to

00:43:52.680 go have a conversation with the future version of ourself so that the future version of ourself

00:43:56.760 can say, Hey, by the way, that decision makes no difference to you. You can get bad fish. It's,

00:44:02.320 I don't care here. Here I am a year later. It makes, it doesn't matter to me. And happiness

00:44:07.780 here is meant as a proxy for whatever the goals are that you're trying to achieve. You know,

00:44:13.380 happiness, you know, assuming when we reach our goals, one assumes that we're happier.

00:44:18.340 So that's why I use happiness as kind of a proxy. So this is a really good test to apply

00:44:23.640 to figure out if I can go faster, I can go slow. So when you feel yourself hung up in that decision

00:44:28.280 and you're taking a lot of time with it, just say, am I going to care about this in a year?

00:44:33.000 Am I going to care about this in a month? Am I going to care about this in a week?

00:44:36.520 And the sooner that you're not going to care about it, whether it turns out well or poorly,

00:44:41.660 because if the fish is great and I see you in a year, it also didn't affect your happiness at all.

00:44:46.500 This is just low impact all around. The shorter the time period in which you're not going to care

00:44:50.620 how it turns out, the faster you should go. So this is the first thing. This has to do with

00:44:56.260 impact. This decision is low impact. Good or bad, it doesn't matter. Now, in the case of ordering

00:45:05.140 off a menu, it's particularly low impact because it's also what we would call a repeating option.

00:45:09.880 So remember I said to you, when I see you in a week, you've had 21 meals since then,

00:45:14.760 assuming you eat three times a day. So that's what I'm referring to there is it's a repeating option.

00:45:20.880 So even if your lunch is bad, you get to go have something for dinner. So you get to,

00:45:24.720 you get basically get to try again pretty quickly. And that's going to be true also of like what to

00:45:29.140 wear or like what to watch on Netflix. Dating, right? If a date, if a date goes poorly, so what?

00:45:37.520 It's just a date and it's a repeating option. You can get right back on Tinder or Bumble or whatever

00:45:44.720 your app is that you like, and you can click and go on a date with somebody else.

00:45:49.760 So that's also a repeated option. Choosing classes in college, it's a repeated option.

00:45:55.260 You get to do it a lot. So when we're repeating options, we can go a little bit faster with those

00:45:59.620 decisions. And when they're low impact, we can go pretty fast. So those are kind of the two

00:46:04.060 things on the impact side. Now there's another framework that we want to think about, which is

00:46:10.460 optionality, which has to do with how easy is it for me to quit the thing that I'm doing and go and

00:46:18.700 do something else. So people may have heard, you know, Jeff Bezos talking about type one or type

00:46:24.320 two decisions or two-way door decisions versus one-way door decisions. And this is basically what

00:46:30.000 he's getting at. The easier it is for me to quit something, the faster I can go because I'm going to

00:46:35.980 be more tolerant of getting a bad outcome because I can switch. I can just quit and go do something

00:46:41.980 else. So this is actually a really, really important concept for great decision-making.

00:46:50.100 So we can apply that. Like if you're in a hiring situation, the difference between hiring an intern

00:46:56.340 versus hiring someone who's quite senior, it's going to be harder on the company, on you to unwind a

00:47:05.640 relationship with someone who's senior. So that is a less reversible decision. It also happens to be

00:47:10.860 higher impact, right? So we've got sort of both of those things working together. Whereas an intern,

00:47:16.160 if the intern doesn't work out well, that's relatively low impact, but it's also very easy

00:47:20.360 to unwind. It's not hard to, you know, part ways with an intern in the same way that it can be very

00:47:27.800 difficult to part ways with someone who's quite senior. So that there we can sort of see, like,

00:47:33.760 how should we be spending our decision-making time? And what you'll see, like people will spend,

00:47:37.280 like they'll find a couple of interns who look like they might be really great for the one job,

00:47:41.940 and they'll just agonize over that decision. Which one should I choose? I don't know. I don't know

00:47:46.500 what to do. But this is a decision which you actually should be going pretty fast on, certainly

00:47:51.540 compared to how much time you might spend on the more senior person. So that you can see how this

00:47:56.400 kind of allows us to allocate our time, right? Dating versus marrying is another example of that.

00:48:02.260 It's pretty easy to quit a date. People do it sometimes in the middle of the dates.

00:48:06.940 People do it sometimes before they actually sit down. They look across the room. I don't think

00:48:10.680 that's very nice, but people will do that. I don't recommend it. I think you should at least talk to

00:48:16.040 the person. But marrying, obviously, that is much harder to unwind. It's more difficult to actually

00:48:21.100 quit that decision. So that's one of the things that we want to think about. And then you can

00:48:26.040 start to take that and say, well, now I could actually take that into my decision-making life

00:48:30.700 and use that as a strategy. This would be called decision stacking, which is when I think that I'm

00:48:35.640 going to be facing a very big decision, what could I do that would give me a lot of information that

00:48:41.200 would help me to improve the quality, to improve the educated guesses I'm making that are going into

00:48:46.260 that bigger decision? What could I do now that's pretty low impact and pretty reversible that would

00:48:50.980 help me with that bigger decision? So like as an example, if you're thinking about moving to a

00:48:56.240 new city or moving to a new neighborhood, renting first before you buy. That's an example of decision

00:49:04.180 stacking. Agile software development is an example of decision stacking. I'm not going to do like a

00:49:11.040 large batch software development where I'm going to have to roll this out to my whole customer base

00:49:16.620 all at once. I'm going to do a small test that's pretty beta to a few people and see how they like

00:49:26.120 it, that little handful of people. And then if it doesn't work out, it's not a big deal. I can just

00:49:31.540 roll the code back. I didn't somehow upend my whole customer base. So you're lowering impact of making it

00:49:36.540 much more reversible. Another really good example of this type of decision stacking strategy would be

00:49:42.200 pop-up stores, right? If I'm thinking about releasing a new consumer packaged good, for example,

00:49:48.320 and I don't know whether people are going to respond to it and I'd like to test it maybe in a

00:49:54.740 new city or something like that. I don't want to sign a year-long lease or try to put this across all

00:49:59.640 Whole Foods or something like that. I can just do a pop-up store where the impact of that not working

00:50:05.940 out is not so great. If I do find something good about it, then that's awesome. I can get some

00:50:10.840 information out of that and it's very easy to reverse and shut down because I haven't made any

00:50:15.240 long-term commitments and I'm doing this in a very small way. So this becomes actually really

00:50:19.840 powerful that it doesn't just help you to figure out how to speed your decisions up, but it also

00:50:24.700 gives you a decision strategy for improving the quality of those long-term commitments that you're

00:50:29.900 going to have to make by trying to figure out how to do some lower impact, more reversible decisions

00:50:35.500 beforehand in order to get you the information that you want for that longer-term decision,

00:50:40.040 which is basically what dating is. It's decision stacking.

00:50:45.580 So another tool you use that I started using right away after I read about it is if you're

00:50:50.740 agonized between like two or three decisions, like you give the examples like, well, should I go to

00:50:54.420 Paris or Rome? Like, ah, which one should I do? And you just make this super simple way to frame

00:50:59.800 it. It's like, well, if you could only go to Rome, would you be happy? Yeah, I would be happy if that's

00:51:05.540 the only place I could go this year. Well, if you go to Paris, would you be happy if that's the only

00:51:09.160 place you go this year? Yeah, that would, I'd be fine. I was like, well, then either one would be

00:51:12.940 a good decision. So just pick, just flip a coin basically.

00:51:17.960 Yeah. So, so people actually will ask me like, when is it okay to go with your gut?

00:51:22.040 And this is the situation where I say it's fine. And the reason why it's fine is because it doesn't

00:51:27.020 matter. So I'm a big fan of using your gut to make decisions when it makes no difference.

00:51:32.320 So use your gut to order off a menu is totally fine, but this is a good example. So, so obviously if

00:51:38.180 you're thinking about like a European vacation, you're trying to decide between Paris and Rome,

00:51:42.280 this doesn't pass the happiness test. If you have a crappy home vacation and I see you in a year,

00:51:48.820 it probably did affect your happiness over the year. It's not a repeating option for most people.

00:51:54.020 They can't just like go on another vacation in the next month, right? This clearly has big downside.

00:51:59.020 It's very expensive. You can't really reverse it. Oh, I don't like Rome. Let me go somewhere else.

00:52:04.460 Immediately I'll just abandon and be somewhere else. I mean, these, you know, it doesn't sort of

00:52:08.700 satisfy all that stuff that would tell you that you can go fast. This is clearly something that's

00:52:13.000 very high impact. So, but what happens to us is that when we sort of get into these decisions and

00:52:18.600 we get a couple of options and the category of the decision is high impact. My big vacation for the

00:52:25.360 year, this is very high impact. Now we sort of get down into, we've got two options that we're

00:52:30.980 trying to decide between in that category of thing that's going to really matter. And now like,

00:52:37.020 you know what everybody does. It's like you're on TripAdvisor, you're looking at every single

00:52:41.660 review, you're asking anybody, you know, who's been to either Paris or Rome. And it's just like

00:52:50.020 total anxiety. But what you just pointed out with the only option test is that what's hanging you up

00:52:56.960 about this decision. What makes this decision feel so hard is that the options are from your

00:53:04.100 vantage point identical. And what I mean by from your vantage point is your vantage point where you

00:53:10.420 aren't omniscient and you don't have a time machine. This is very, very important. You cannot see

00:53:17.480 how those vacations would go in some kind of perfect sense where the image is crystal clear.

00:53:24.000 It's going to be cloudy, right? Same as the Boston problem. You don't have all the information you

00:53:31.080 need about either place. But from the standpoint of the information that you do have and what you

00:53:38.040 know about your preferences and what you can afford and the time that you have to go, Paris and Rome are

00:53:44.220 kind of identical to each other, given the acuity that you have on this issue, right? So they're the

00:53:52.000 same. And the way that you can find out they're the same is through the only option test. If Paris were

00:53:57.600 the only option that I had, would I be really, really happy with it? Of course, the answer is

00:54:02.320 yes. If Rome were the only option that I had, would I be really, really happy with it? Of course,

00:54:07.080 the answer is yes. So this brings up a really important decision process, which is when a decision

00:54:11.960 is hard, it means it's easy. Meaning when a decision is hard in this particular way, that you have two

00:54:17.120 options that you can't decide between. What that means is it's really easy because what that is telling

00:54:22.120 you is that the options are identical, that there is really no difference between the two. So whichever

00:54:27.900 one you choose, it's probably a pretty good option, which is what you get to through the only option

00:54:33.420 test. So we can go back to that intern problem, right? You have two interns that you're thinking

00:54:38.860 about for one job. They both seem really great. And now you're agonizing about which one you should

00:54:44.640 hire. But you should just step back and say, if intern A was the only person I could hire,

00:54:49.060 would I be ecstatic to have this person as the person to fill the job? Yes. If intern B were the,

00:54:55.340 you know, the only person that I had available to hire, would I be ecstatic to have that person in

00:54:59.800 the job? Yes. Okay, well then you're done. And you can flip a coin. You can go with your gut. You

00:55:04.500 could ask someone else to decide. I don't care. But don't take any more time on the decision.

00:55:09.520 And then we can sort of take this and go back to this. We can take a step back and say,

00:55:13.680 what is this telling us? And what it's telling us is that decisions are generally thresholding

00:55:18.320 problems. That there's a sorting process, which is of all the options that I have available to me,

00:55:24.440 which of them satisfies the requirements that I have for thinking that this is something that I

00:55:30.080 would want to choose. So that's the sorting process, which means I need to get it above a

00:55:34.760 certain threshold where this is going to be reasonable for me to choose. In the case of a

00:55:39.940 European vacation, this is the amount of money that I have to spend. I'd like there to be great

00:55:44.220 architecture. I'd like the food to be amazing. I want there to be history. I'd like it to be a

00:55:50.040 place where I can walk everywhere, as an example. Okay, so now you've figured out, here are my

00:55:56.300 requirements. I want to look at the options that I have of places that I can go and figure out what

00:56:00.900 meets that threshold. Once something is above that threshold, you're done with the sorting and now

00:56:06.020 you're in the picking part. Picking between options that all have met the threshold, that have all

00:56:12.600 met that sorting process and satisfied that sorting process. And once you're in the picking part of the

00:56:18.400 decision, flip a coin. I love it. It's really, really helpful. Well, Annie, this has been a great

00:56:23.660 conversation. Where can people go to learn more about the book? Okay, so anniduke.com is a great place

00:56:29.840 to go find out about me because that has kind of all things Annie Duke on it. You can find my books

00:56:35.120 there. You can find video of me talking. You can get links to podcasts that I've done. There's also a

00:56:40.680 contact form. I actually love hearing from people who've heard me speak or have read my work. In fact,

00:56:46.000 How to Decide was born of conversations with people who contacted me because what I found after I put out

00:56:53.460 Thinking in Bets was that people were asking me, you know, okay, I see what you're saying in Thinking

00:56:58.260 in Bets about uncertainty and the way it really frustrates our decisions. How would I actually make great

00:57:03.860 decisions? Like what are the tools that I could use? What would the process look like? How can I

00:57:08.100 think about these things in a clearer way? And I just realized they were asking me to write something

00:57:12.340 that was more how-to. And so I did. So I find it very helpful when people actually reach out to me.

00:57:18.180 So please don't be afraid to do that. So that's one place you can find me. I'm on Twitter at Annie Duke.

00:57:23.540 And then the last thing is, you know, I would love it if people would check out the Alliance for

00:57:27.220 Decision Education, kind of rolling back to the beginning of the conversation.

00:57:30.120 We really think it's an emergency right now to get decision education into K-12. And I, you know,

00:57:37.860 when we think about the information ecosystem that we live in, you know, and how much information is

00:57:42.600 coming in and, you know, there's disinformation and a real ability to fall into serious echo chambers,

00:57:50.520 become extremized. This necessity to be able to navigate the world and figure out what's true

00:57:57.760 and then figure out what to do about it in both the information ecosystem that we're living in.

00:58:04.080 But also, you know, right now, when we think about what's happening in terms of career trajectories

00:58:09.140 and technology and what jobs are going to look like in five years or 10 years, there's so much

00:58:14.420 around that. And it's changing so rapidly that, you know, equipping our youth to be able to sort of

00:58:20.480 navigate that changing landscape, we think is just incredibly important. And as you pointed out,

00:58:24.980 this is not something that's taught in school. And so at the Alliance, we're really trying to

00:58:29.360 change that. So I would love it if people would check the Alliance for Decision Education out.

00:58:33.720 Fantastic. Well, Annie Duke, thanks for your time. It's been a pleasure.

00:58:36.520 Well, thank you so much for having me back.

00:58:39.680 My guest today was Annie Duke. She's the author of the book, How to Decide. It's available on

00:58:42.940 amazon.com and bookstores everywhere. You can find out more information about her work at our website,

00:58:46.900 annieduke.com. Also check out our show notes at aom.is slash how to decide. We find links to resources

00:58:52.260 where we delve deeper into this topic. Well, that wraps up another edition of the

00:59:02.880 AOM podcast. Check out our website at artofmanliness.com where you can find our podcast

00:59:06.440 archives, as well as thousands of articles written over the years about pretty much anything you can

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00:59:33.660 support. Until next time, this is Brett McKay, reminding you not only to listen to the AOM

00:59:36.500 podcast, but put what you've heard into action.

The Art of Manliness - February 17, 2021

How to Decide

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