Once called "the Duchess of Poker" for her victories in the World Series of Poker, Annie Duke is also widely respected for her bestselling books "Thinking in Bets" and "How to Decide." In this interview, Mike Maples Jr of Floodgate talks to Nancy about how startup founders can also master decision-making frameworks that improve the odds of winning in the face of massive uncertainty.
(04:51) - Explaining what makes a decision good or bad
(6:36) - On how memory creep impacts our view of Hilary Clinton’s 2016 campaign
(11:30) - On the use of a knowledge tree to inform decision making
(16:59) - Looking at Pete Carrol’s controversial decision in the Super Bowl
(26:16) - On founders falling into a trap around pricing their product
(38:08) - When to make informed decisions and follow a process
Annie Duke:
Let's think about what a great decision is. And a great decision is a forecast of the future. So the better your forecast of the future, that's how we can tell that a decision is great.
Mike Maples Jr.:
That's the voice of Annie Duke, who's known by many as the Duchess of Poker. Annie's two books, "Thinking in Bets" and "How to Decide" are instrumental for me when I face a crucial decision under massive uncertainty. And in our interview, she's going to talk about how you can be the best possible leader in the chaotic world of startups. This is Mike Maples Jr. of Floodgate and it's go time with Annie Duke.
Mike Maples Jr.:
Let's get started. Annie Duke is a colorful person with a lot of good stories to tell. She started as an academic, graduating from Columbia and then pursuing a PhD in Psychology at the University of Pennsylvania. But one month before defending her PhD dissertation, she decided she no longer wished to pursue academia and left school. Naturally, she decided instead to become a Professional Poker Player, amassing 4.2 million in winnings over time. She then embarked on a career as an author writing about her decision process ideas that contributed to her success in poker. Many of her discussed frameworks for poker are applicable to any life situation that involves massive uncertainty, particularly the kinds that startup founders are faced with regularly. Let's catch up with her.
Annie Duke, welcome to the podcast.
Annie Duke:
Well, I'm glad we finally made it after some internet problems and the whole state of California, as well as Oregon and Washington are on fire. So I'm happy for the technology that allows us to be speaking to each other in the midst of many disasters, including the pandemic and the fires. At least we have that as the world is literally burning.
Mike Maples Jr.:
Well, and I guess a lot of those outcomes weren't in any of my decision trees.
Annie Duke:
Yeah, well, I assume fires were. We have enough data now that you should probably be expecting some fires every fall but pandemic wasn't on my bingo card, for sure.
Mike Maples Jr.:
So Annie, in recent years, you've done a lot of work in decision making, under uncertainty and mental models and things like that, but your relationship to this topic started in a less familiar intuitive context. You were a Professional Poker Player. How did you get into poker in the first place? And how did the journey go from you being a Poker Player to a Decision Theory Expert?
Annie Duke:
Actually, getting into the decision strategy and decision-making cognitive science was actually a circle back for me. I actually started off my adult life in a PhD program in Cognitive Science at the University of Pennsylvania at a National Science Foundation Fellowship and was planning to go off and become a professor, to tell you the truth. So right at the end when I was going to go off and become a professor as planned, something I didn't have on my bingo card then was that I was going to get sick and end up in the hospital for a couple of weeks. And it happened right around the time that I was supposed to go out for all my job talks. So I needed to take some time off to recover and during that time, I honestly I needed money. That's when I started playing poker. During that year when I was playing poker, I just ended up really falling in love with the game.
I really, really loved the problem that you're trying to solve in poker, which is how are you making decisions in these environments where there's very limited information, there's quite a strong influence of luck. During the last 10 years of my poker career, so really 2002 to 2012, I had already circled back into the more academic work. 2012, I retired from poker really to focus on that part of my life. It was actually what I was mostly spending my time on anyway at that point. I was more playing poker on television by then because I was really interested in this topic. Ended up writing, "Thinking in Bets" and then now I've got the new book, "How to Decide" coming out. I fully circled back into that more research and academics and what has resulted in a lot of consulting, as well.
Mike Maples Jr.:
Annie, how do you think about what makes a good decision then? It's pretty easy usually to tell if the outcome was good or bad, but what's your sort of organizing principle for evaluating whether a decision was good or bad?
Annie Duke:
Let's think about what a great decision is. And a great decision is a forecast of the future.
Mike Maples Jr.:
Mm-hmm (affirmative).
Annie Duke:
The better your forecast of the future, that's how we can tell that a decision is great. We can think about if you were omniscient or you had a crystal ball, obviously all things being equal, you would be an amazing decision maker because you would be able to say, "I'm omniscient. I know what all the different ways that the world is going to go and then therefore I can make decisions about that." If you're omniscient that doesn't necessarily, I'm not saying that you know that the coin is going to flip heads, but what I'm saying is that you know that the two possibilities are the coin could be heads or tails and that it will land that way some percentage of the time.
Mike Maples Jr.:
[inaudible 00:05:46] The decision problem with perfect accuracy.
Annie Duke:
Exactly. What that means is that a great decision process is one in which you have identified as well as you can, given the limitations of not having a time machine and not being omniscient. That you have identified as well as you can with a reasonable set of possibilities are for any outcome that you're considering. That you have a really good sense. In other words, that you can see the luck. I'm not saying you can control it but you can see the luck in the sense that you have a good idea of how probable any of those particular outcomes are.
Mike Maples Jr.:
Mm-hmm.
Annie Duke:
And that obviously you have a good sense of what the payoffs would be. In other words, your goal is, I want to be able to see two things, one is what's the expected value and what's the variance or the volatility.
Mike Maples Jr.:
And I mean, this kind of gets to, you can't really analyze, this is the other thing I learned from both of your books is, you can't analyze the quality of a decision effectively, unless you capture your thoughts at the time you made it.
Annie Duke:
That's exactly right.
Mike Maples Jr.:
[inaudible 00:06:51] can't remember how you thought about it at the time.
Annie Duke:
Yeah, actually I think there's a really powerful example of that. I talk in "How to Decide" the new book about memory creep. And this is a really big problem. First of all, the result in problem in itself is an issue because once you know the outcome, what happens is that, certain aspects of the decision get highlighted and certain ones of them get low lighted that help you to sort of square the decision. In other words, to make the outcome makes sense in relationship with the decision. If it's a good outcome, you'll tend to remember the bullish parts of the decision process. And if it's a bad outcome, you'll tend to remember the bearish parts of the decision process. But then we lay on top of it this problem of we misremember, we aren't particularly good at, in retrospect, really accurately reconstructing what our state of knowledge was at the time that we made the decision.
There's this great example that I feel collides these two problems of resulting and then hindsight bias or this memory creep problem, which is really just the 2016 election. So very briefly, obviously Clinton lost the Rust Belt. Michigan, Wisconsin and Pennsylvania. Across the three States, it was about a total of around 80,000 votes, across three States. It was a razor thin margin, but the first thing is we can see the resulting problem because everybody's agreed that she made, there was a horrible decision that she didn't invest more time in those three States. Why was she in Florida? Why was she in New Hampshire? Why was she in Arizona? Why was she in North Carolina? So, here we can see the resulting problem in the sense of, we know that it was a bad outcome for her.
And we're assuming that her decision-making around where to campaign was there for bad, but there's nothing more crowdsource than decision-making during a presidential campaign. We can see that right now, there's billion things written about Biden's strategy or Trump's strategy and whatever and comparisons. So I actually just searched. And the first article that appears criticking like saying that it was a really bad strategy on her part appears on November 9th of 2016, which I don't know if people remember, but the election was on November 8th. So obviously if it was a terrible decision, you would think that all of the brain trust of every political analyst in America would have figured that one out in advance. And why would we assume that she somehow would have figured out something that nobody else did? So we know that, for just that reason alone, we've got a problem, but then we have this other problem.
So I actually pitched this particular story to an editor at one of the big three newspapers. I won't say which one. And they said, "well, I'm not going to publish this because all of my friends knew about it. We were all talking to each other and I read a billion things about it. And so your thesis is wrong." And I was like, I literally said, "I did the Google search." So if you all were talking about it, it was this really big secret in journalism that apparently nobody thought would be interesting to write about, even though it was an incredibly contrary and take at the time to say that she was making a mistake. And the funny thing was, that there were a couple of articles that I could find that were about those three States, but they were criticizing Trump.
Phillip Bump wrote a huge piece in the Washington post saying, "what an idiot Trump is for campaigning in Pennsylvania."That's the Memory Creep problem. Here I'm talking to someone who's in politics. He's a journalist. And he say, "No, no, no. I knew that at the time." I mean, this is part of the problem. And it's part of the reason why it's so important to create an... to have this evidentiary record, the reason why I can go back and say, "Look at this resulting that was happening. And the memory Creep that happened with the 2016 election because I could Google it." So you want to be able to Google your own decision-making. I mean, that's the thing.
I just want to separate this from journaling. People talk about like having a journal, which I feel like feels like something on top of the process. If you have a good decision process, the record will be created naturally. Part of the process, it will just appear because you can't have a good decision process without the record without naturally creating a record. So, journaling, I feel like people are like, "Oh, I got it. I have to do this extra thing. And I need to write all this stuff down." After we've already made the decision or even during, or whatever, that's not an extra step.
Mike Maples Jr.:
I've seen you describe the notion of a knowledge tracker where it's kind of like, here's the stuff I knew before the decision. Here was the decision. Here was the outcome. Here's the stuff I know after the outcome. That's not an expert task. That's decision-making 101. It's like, "if it seems like an effort to have to fill in those blanks, you're not really teeing up a good decision." You're just like, "you're taking risks that are unnecessarily risky." And this I think would be helpful for founders. Because in the early days of a startup, what you care most about is speed of learning. You're at zero and you're trying to go to one and ideally you create something unique that people are desperate for.
And so you're on a truth seeking exercise to understand how to do that. And so it feels to me, mastering kind of this knowledge tracker approach that you take could be really valuable for people. How did you start this idea of a knowledge tracker and what are the elements of it? How do you encourage people to use it?
Annie Duke:
So, yeah, let me just say, first of all, like a lot of the ways that you think about how do I improve the look back, in order to understand when I get an outcome, what it means and the way you think about how to solve that retrospectively actually opens up how you do it prospectively, like how you create a better decision when you're actually making the decision, which is actually the order that the book goes in, it starts with this retrospective problem and then says, "Aha! But now we know how to make a good decision. So let's actually get that into our decision process." So the problem when we're doing the look back is two-fold, as we've just said, the first is that we forget that there's a whole bunch of other ways the decision could have turned out. That the branch that we ended up observing, might've been like incredibly low probability.
And we forget to realize, I have to put this in context of kind of the multiverse. Because there's all sorts of universes that can unfold. And I happen to be on one timeline. So let me try to think about the other timeline. So that's the first piece to getting this kind of retrospective look, which helps us to populate those four cells that we talked about. That different relationship. And then in terms of the knowledge problem, we're trying to figure out, what did I actually know at the time of the decision? Because my decision can only be as good as the knowledge that I had. So what we want to do is think about what did I know before the decision. And in other words, what informed the decision that I made?
What was the decision? What was the outcome? And then what revealed itself after the fact, and then as you look at what would revealed itself after the fact, now you can say to yourself, could I have known this beforehand? Now for most things, actually, the answer is going to be no. And that's where we really get into the problem by saying Clinton should have known that there was a polling error in exactly three States, but not nationally and not another state. And she should have known this thing that nobody else on earth knew. And obviously that's silly and it can get even worse because we can remember we did know it, which is what happened with the editor that I talked to. He said, "No, I knew it." I'm like, "You're at a magazine. I mean, a newspaper. Why didn't you publish this opinion then?"
Once we have what revealed itself after the fact. We can think about that, which category does it go in? Could I have known about it beforehand? And that you're going to get a yes or no answer. If you could have known about it beforehand, try to figure out what happened in your decision process, that you didn't spot it. If it was something that was totally knowable beforehand, and then don't beat yourself up too much about it. Just make sure that you make sure that it's something you're looking for in the future. Now, then there's going to be another category, which is, I couldn't have known about it beforehand. It, wasn't something reasonable for me to know about beforehand. And that is going to fall into two categories. One is going to be, it's never going to be knowable. Because it could be, for example, the outcome itself.
Mike Maples Jr.:
It's like the flip of a coin. If it goes the wrong way, there's nothing you can learn to change your future about that.
Annie Duke:
Exactly. So if that's the case, you just move on. But sometimes it's things like a pandemic, is that something that I could reasonably have known was going to occur beforehand? Well, for some people like Bill Gates, the answer is yes, but not for me. So that's that category. We want to divide it into, was it reasonable to know? Could I have known it beforehand, reasonably? And if the answer is, no, you still want to go the further step and say, "Is this something that I could know in the future?" And sometimes the answer will be no, sometimes the answer will be yes.
But notice that all of this is kind of accomplishing a couple of things. One is it's helping you with the memory Creep problem, because it's really getting you to think about what did I know and what didn't I know. And then it's actually helping your decision process going forward. Instead of all this, like, "I should have known, I can't believe it. I'm such an idiot. How did I not know?" Or even worse yet when we do that to other people and say, how could you not have known? How could you not have seen this coming? Whatever. Now we're actually focusing on... Well, now we can actually improve our decision process going forward because we're actually thinking about what's reasonable to include in our future process and what's not.
Mike Maples Jr.:
One of my favorite examples. I think you mentioned this, actually it was when Pete Carroll has Marshawn Lynch, the Seattle Seahawks, he decides to throw the ball and it gets intercepted by New England at the super bowl. New England wins the super bowl. And they're on the one yard line or something, everybody's like, "Pete Carroll, how can you be so stupid? You got the best running back in the league. Just give him the ball and wait to be amazed." That's another one of these things where you're looking at the outcome, rather than the decision.
Annie Duke:
I actually open the Thinking in Bets with that, my first book. My first general audience book. Yeah. What's really interesting about that is people are still talking about that five years later, by the way, he totally got called an idiot. And nobody's really looking at... Well, he couldn't know it was going to be intercepted actually matters is what's the probability of an interception there? What's the probability of a touchdown? What's the probability of an incomplete pass?
Then you want to compare that to the other option, which is what's the probability if you handed it off to Marshawn Lynch, that Marshawn Lynch fumbles and gets an interception, or that he gets stopped or that he scores a touchdown. And then you have a third thing which just has to do with a little options theory. Which is, which path gets you three plays as opposed to two? All of those things lead to calling a pass play on either first or second down. Now I'm completely agnostic as to whether it's correct to do the past play on first or second down. I just know that it's correct to do it on one of them. And Pete Carroll, who's a smarter coach than I am, choose to do it on the first one. So I defer to him.
Mike Maples Jr.:
That's the thing I think people miss. Like what you're really comparing is the odds of getting intercepted. Because if he throws an incomplete pass, he's got another play. You could still, you can run Marshawn Lynch on fourth down.
Annie Duke:
Well, the fact is actually, yeah, if it's an incomplete pass, the clock stops on its own. You can still hand the ball off to Marshawn Lynch twice. If you hand it off to Marshawn Lynch twice, you get two plays. If you pass on either first or second down, you get three. I mean, I'll take that.
Mike Maples Jr.:
You can get intercepted. But I think the probability of that from the one yard line is probably pretty low.
Annie Duke:
Yeah. So basically, when we think about options theory, it's like, okay, so you're getting the option for a third play. And the question is, what does that cost? And the cost is whatever the interception rate is, which is between one and 2%. It seems like a pretty good decision to me. I have to tell you that the resulting it's cognitively, it's so strong that I have played that video a hundred times. And every time I see the video, I'm like, "Ooh!" I still-
Mike Maples Jr.:
Even now I can't believe he just, "Give it to Marshawn Lynch."
Annie Duke:
And I have to work my way through it, but that's part of why you need these really good decision processes. Because the fact is that we can't, re-install our mind where it is what it is. So we need to think about how can we improve our processes that are kind of setting up guardrail and allowing us to think more rationally about this stuff.
Mike Maples Jr.:
What are some of the other tools? I know that in your book, "How To Decide" you talk about some of the steps. In terms of the potential outcomes and your preferences and things like that. How can founders take advantage of the six steps of decision-making.
Annie Duke:
Let me just kind of step back a little bit. I think we talked about a decision is really just a prediction of the future. Ideally we'd like to have a crystal ball. So we're kind of trying to get as close to that as possible. And when we think about that, when we talk about these two different influences, which is luck, you have a particular option. And that option has a set of things that could occur that are associated with it after you've chosen the option, that's where the influence of luck occurs. Because you can think about what those possible futures are, but you have no control whatsoever about which one you actually observed that particular time. That's the influence of luck. But we have this other problem which is the hidden information or incomplete information.
And we can think about, we can be really amazing in terms of what our process is. We're going to forecast things and we know, we're going to think about the different options and we're going to forecast them. And we're going to think about what the expected value is. But if what's informing our decision isn't high quality, that house is going to fall down. Everything about this process that you're doing is trying to motivate you to deal with this informational problem. Because that's the foundation on which this whole decision house is built. And we have a problem with that foundation.
The first is that a lot of the stuff we believe is inaccurate. So let's call that cracks in the foundation. And the second is that we don't know very much, it's like a super flimsy foundation. So we can think about the universe of stuff we know versus a universe of stuff we don't know. The universe of stuff we know is like a speck of dust on the head of a pen it's like Whoville. And then the universe is stuff that we don't know is like the size of the actual universe.
Mike Maples Jr.:
Crazy bit. Yeah.
Annie Duke:
So what we're trying to do is how do we get stuff out of the universe of stuff we don't know into the universe of stuff we know to repair our foundation? Because we can solve both problems by exploring that universe of stuff that we don't know. So we can add that tore, we're going to find ways to repair the inaccuracies, and that's where we're going to find new information. Anything that I talk about is really focusing on that particular problem. So even when we talk about what are the steps for a good decision? The six step process. It's really meant to start getting you to ask questions, to seek knowledge, to explore the universe of stuff you don't know in a much more objective way, because we don't take random walks through that universe.
We take very specific walks where we're particularly shining lights on stuff that confirms our beliefs and on people who believe the same things that we do. And information sources, i don't know a lot of Bernie supporters who are spending their days watching Fox news. And I don't know a lot of Trump supporters who are spending their days watching MSNBC. There you go. That's kind of what we're trying to solve for. Let's now step back and say, okay, so what does a really good decision process look like? Well, it's got to be an accurate prediction of the future. Is it accurate as we can get. We're going to usually be pretty far off, but small differences aren't really good. If we can make small improvements, we're really good. It starts with identifying the reasonable set of possibilities. So we wanted to do, and by reasonable, I mean, I don't want you to sit there and go down a rabbit hole of what if an asteroid hits Russia.
Okay. Why are you spending your time on that? You're trying to decide whether to release a particular piece of code. Let's not worry about that. What are the reasonable set of outcomes that actually matter for the decision that I'm making. And then you want to go farther than that and you want to look at them and say, what is your preference for those outcomes? And that really just has to do with the payoff, what's going to get you the biggest bang for your buck of those possible outcomes that I'm considering if I choose this particular option. How much is it going to advance me toward my goal? How much of it is going to advance me away? And we can think about that in a lot of different ways. It could be, for example, if I'm a founder, I'm trying to maximize how much information I'm going to get from any option I choose.
I can think about what of the set of possible outcomes is going to informationally give me the heaviest lift? It could be like if I'm trying to hire someone and turnover is just a mess, I may be very particularly worried about how long the employee is going to stay with the company. We can think sort of broadly or narrowly about what those outcomes are and what payoffs we really care about. But we want to think about, what's the magnitude of the payoff, good or bad. Then we want to think about what's the probability of those things occurring. That's obviously incredibly important because otherwise we don't really know how to compare the good to the bad. It could be that there's like some disastrous outcome in there, but it's like really, really low probability.
Likewise, there may be something that's very low probability on the good end, but it's got a big enough payoff that it outweighs the bad. So unless we combine these two things, the payoffs and the probability, it's very hard for us to think about what the quality of that option is. And then basically the rest of the steps are just to rinse and repeat. What are the options I have? Let me go through the same process. And then I can compare these things together to figure out which option is going to be more likely to get me to advance toward my goal in a way that has obviously the appropriate risk associated with it. Because you always have to be thinking about those extreme downside outcomes.
Mike Maples Jr.:
The other thing I learned was by framing it in a set of outcomes, you can avoid traps that you don't even see. So for example, pricing, a lot of founders that I've run into that have struggled with pricing. Well, they make a couple mistakes, one is they just have the discussion too with the customer because they're afraid to. But now let's say they're going to have the discussion. It's tempting to think I need to get this price to cover my costs and then frame it as, "can I get that price or not get that price." But if you think in bets, that's not the right way to think. The right way to think is there's a range of prices you might get and you should run a test. When I was working with Chegg, which does textbook rentals, Asman Rashid was like, "well, we got to get, if it's a hundred dollars new textbook, we got to be able to rent it for 35, or we don't have a business."
But his insight was, "I'm not going to ask, will you pay 35 to rent it?" He's like, "I'm going to test, will they pay 35, 45, 55, 65, 75?" And what we found was that a lot of students would rather rent a textbook for $65 than pay a &100 for a new, which was way better than we'd hoped. But if you frame the test incorrectly, you never get that information. You would have never known that you can get an even better outcome than you possibly imagine you were going to get. But that's kind of taking the outside view versus the inside view, the inside view would have been how much money do we need to get to be profitable or for this to be business for us? But that doesn't really matter, because pricing isn't a math problem. It's a psychology problem. What we needed to understand, was what the outsiders thought it was worth to rent a textbook and you have to run that test and test those outcomes independent of what your costs are.
Annie Duke:
I love that example because basically what you're saying is, okay, we're thinking about what the buckets are here. What are the bins that we're forecasting? And we can say, would they rent it for 20, between 20 and 30, 30 and 40, 40 and 50, 50 and 60, 60 and 70, 70 and 80, 80 and 90.
So you can just say that, all right, let's try to put a probability on these things and everybody's going to go, "Ooh, this is really hard because we don't really know. Because nobody's ever done this before." Or, "There's not enough data for us to answer it." And then of course what comes from that? And that's what I'm saying about a good decision process really makes you start to think about what can I go find out.
And now that's where that naturally comes from that. Is that you say, "Oh, you're making me forecast this stuff. What can I find out?" That's at a reasonable cost for me to go find it out. That's going to allow me to actually better understand how I'm supposed to forecast these different bins. And then naturally what comes out of that is, well, maybe I could ask some people.
Mike Maples Jr.:
You bet you've talked about this a little bit already, but you're implicitly making decisions all the time. For example, the job that we're in, we're making a decision to be in that job every day, because tomorrow we won't get today back.
Annie Duke:
That's exactly right.
Mike Maples Jr.:
So we made a decision to spend a day, the way we spent it and looking back 50 years from now, would you say, "That was the optimal choice?" If there's even a shred of doubt in your mind, that's a decision opportunity. You don't just have to make decisions when you're confronted with problems or what next. Every opportunity you have to make your life better all the time is a decision opportunity on some level.
Annie Duke:
Yeah. I think what happens, what you are uncovering here and exploring is that, I think that we generally think about decisions as doing something new, as opposed to stay in the course. I mean, I'm sitting here and talking to you, that's the status quo right now. I could get up and leave. I mean, but I have to think about what the consequences of that would be. I think that would be very weird and it wouldn't be really something that I didn't want to do, but I could do it.
Mike Maples Jr.:
I would not hold it against you.
Annie Duke:
Well, it would be strange though. It would be, Annie left. But we can think about that in terms of even jobs, I'm in a job and I don't like it, but so why don't people then go find another job? Well, because that feels like a decision and what if I make that decision and I make that change and it doesn't work out. We're going to... Because we view that as a decision, we think we somehow decided to have things not work out, whereas, because we don't view it as a decision to stay in the job that we're already in.
If it doesn't work out, we on as hard on ourselves. And this actually comes generally into this sort of world of like, when we think about the interaction between the resulting problem and this kind of status quo versus innovation problem, things get really interesting. So let's go back to the Pete Carroll example. This actually will be fun. Pete Carroll did something that was really strange, at least to the public, on the first play. Very weird. And we know what happens in that case when it fails, he's an idiot, but let's do the thought experiment, it succeeds. What do the headlines look like?
Mike Maples Jr.:
Genius.
Annie Duke:
Genius. We get these big extremes, heads or the tails. Idiot, genius.
Mike Maples Jr.:
But here's the other thing that I think is interesting and where the choice was kind of brave. If he gives it to Marshawn Lynch, he scores a touchdown. Everybody's like, "Well of course." If he gives it to Marshawn Lynch, he fumbles. Everybody's like, "Well, I mean, gosh, who could've seen that."
Annie Duke:
And also by the way... Or if Marshawn Lynch just doesn't score? "Ah, I mean, it's the Patriots, what are you going to do?" So that's exactly right. What's interesting is that when you do the consensus thing, when you do the status quo thing. When you stay the course, if it works out, it's like, "Eh, good job, you know, great." And if it doesn't work out, "It's like we have bad luck." It's when you do something different. When you go on a different route that all of a sudden you've got this sort of genius, idiot thing happening. So this is actually really, really important in a variety of ways we think about high level and then sort of low level. What is it when you think about what happens with these really innovative companies that then become establishment players and somehow then they all of a sudden lose market share and they stop innovating.
Well, this is why, because once you become established, there are established things that you do. There's an expectation of consensus. There was an expectation of following particular process or rules within the company. And if you veer off those, there's a tremendous amount of career risk for you. If it doesn't work out and we're not really willing to trade that. I mean, obviously that's what prospect theory is. It's not an even trade between the accolades and the downer. Accolades do not pay you for the idiot part. So, we've got this really big career risk. That's what happens when suddenly IBM in the 1980s, which was the innovator and you're like what happened there? That was really weird. They were great. And they had all the market share and all the money and all the balls and then Apple came and kick them in the butt or whatever.
So that's a little bit of...so you can think about that in sort of a big macro way of what's happening when someone's transitioning from startup to establishment and how much do those established procedures actually end up inhibiting new innovation? That's a question, but even in a world where the innovation is expected. Obviously one of the nice things with a startup is there isn't nothing established. You're supposed to do these weird things and try these weird things. That's still the case though on a personal decision making level. While it's true that you might be doing weird and disruptive things in terms of the product or the market within your own decision-making on your team. You're still going to run into the same problem. "Well, you know what? You didn't talk to me. I didn't agree to that. Why did you go and do that? Now You failed. You're an idiot." So, in a macro sense, it's an advantage for the startup, but you have to think about that as an individual. How is that affecting me as an individual?
Mike Maples Jr.:
You think about it, of all the things that it can impact your quality of life, decision-making has got to be right at the top. "We are the product of our choices." And it's funny, they teach you all kinds of things in school. They teach you English, Spanish, math, all kinds of different stuff, physics. And what I came to realize later in life, as I read some of these books is that decision-making is a discipline too. And talk about one that's worth learning. I mean, you can just make a little bit of an effort to understand how to decide better and be better than 99% of the world.
Annie Duke:
Yeah. And to be clear, I'm not downplaying the role of luck. Luck is the greater influence here. The issue though you don't have any control over luck. So pay attention to the thing that you do have control over. And everybody is subject to . I mean, that's the thing. It's not like "I'm differently subject to luck than other people." Luck is a problem for all of us to deal with and problem, or maybe, a mitzvah, whatever, like it exists. But the whole point is that you can't, you have no control over that anyway. So you have to focus on the thing that you do actually have some control over. I didn't have any control over when I was born or who I was born to or how tall I was or what my talents were, but I do have control over the way I decide about all those things.
I didn't decide that I wanted to have a career as a singer, given what luck dealt me. I think that that separation is really, really important. So I just want to make clear, I'm not downplaying the role of luck. Obviously luck has a huge influence in the way that people's lives turn out. It's just, I can't do anything about it.
Mike Maples Jr.:
You got to play the hand you were dealt. Now, that doesn't mean you can't play it better than the next person.
Annie Duke:
That is exactly right. Thank you. I will take that from you as well.
Mike Maples Jr.:
Thanks Annie. It's a pleasure as always. And I mean, I've really for what it's worth. Just even not in podcasts, I've just really appreciated your books and-
Annie Duke:
Oh, Thank you.
Mike Maples Jr.:
Probably the most influential person in how I think about decisions.
Annie Duke:
Oh my gosh! Thank you. I can't even believe you said that. Holy cow. Thank you.
Mike Maples Jr.:
Thanks Annie.
Annie Duke:
Stay safe.
Mike Maples Jr.:
Thanks for listening to the Starting Greatness Podcast. If you've enjoyed this episode or you're new to the show, I hope you listened to our past interviews with legendary founders like Reed Hoffman, Mark Andreessen, The Instagram founders, and Keith Roy. I'd love to have you subscribe wherever you get your podcasts. So you don't miss an episode. And if you liked the show, I'd be grateful. If you leave us a review on Apple podcasts. You can also follow me on Twitter @M2JR and subscribe to our newsletter for exclusive content and events@greatnessdotsubstack.com. Until we catch up again, I hope you'll never let go of your inner power to do great things and whatever matters to you. Thank you for listening.