Beyond Luck—Behavioral Science and the Art of Decision Making with Annie Duke

Cold OPEN: … “…things that we own, we value more than things that we don't own. And one of the things we own is our own ideas.”

Justin Thomson

Welcome to The Angle from T. Rowe Price, a podcast for curious investors. Thank you for joining me today. I'm Justin Thomson, head of the T. Rowe Price Investment Institute. My guest today is Annie Duke – world poker champion, best-selling author, and expert in the science of decision making. Today, we are going to get her unique perspectives on the psychology of high-pressure decision making. 

Welcome Annie, welcome.

Annie Duke

Well, thank you for having me, Justin.

Justin Thomson

In this podcast, we are going to apply what we've learned from poker, and Annie's knowledge of cognitive science to help investors of all stripes make better decisions. Let’s get to the nub of this because we're here to discuss the science of decision making and how we can apply that to investing. And I'm going to start by quoting “Thinking in bets”…’What good poker players and good investors have in common is their comfort with the world being an uncertain or unpredictable place.’ Now the parallels go beyond that. But why don't we start there? And I'm going to I'm going to paraphrase one of my own investment heroes, Howard Marks. How do you get comfortable with being uncomfortable?

Annie Duke

So, yeah, so here, here's really kind of the issue that we all have as decision makers, and this is the core of what I think about. When we make any decision, we are making that decision under conditions of uncertainty, and there's two forms of uncertainty. The first is if you wanted to be academic, you would call it epistemic uncertainty, which just means you don't know very much. So, if you think about what you know, in comparison to all, there is to be known, for most decisions that we make it's a very, very tiny little bit that we know, and so we can feel that when we're, thinking about whether we should choose an option or not, that we wish we knew more. And then obviously, after the fact, how many times have you said, urgh I wish I'd known that, when I was making the decision in the first place. That's that feeling of the not knowing.

And then the second form of uncertainty it's called aleatory, but it just means luck. So, let's imagine that I were omniscient, and I had all of the information at the time of the decision. The world is still probabilistic in nature, so I could know everything, and I could choose the best option, which maybe would mean that 80% of the time I'm going to get a favorable outcome, and 20% of the time I'm not. And it's it's objectively the best option because I'm not I'm I'm on omniscient. I say 20% of the time I'm going to observe a bad outcome and that's going to have to do with luck. And so these are the,  that that's the world that we're living in when we make decisions and you have to get comfortable with that, you have to say, that's fine. I’m not omniscient. I don't have a time machine. Let me make decisions under those conditions.

Justin Thomson

Well, it's interesting. I mean, the point is that investment decisions that are made in complete confidence are usually the ones that don't work and the best ones you make are surrounded in dissonance and jeopardy, and dealing with those probabilities is where you make the best investment decisions.

Annie Duke

Well, I think that when you make, you know, when you make investment decisions with total conviction, you're, it can't be true. I mean, that's really what it comes down to is that it can't be true, because we don't know very much. And there is an influence of luck. And so, any decision, almost any decision we make, not all, is going to have some probability of observing a bad outcome. It could turn out poorly. And you're always going to learn new things after the fact. That's just the fact. So what I think is that, when you're going to make really good decisions is when you really see clearly, when you’re when you get closer to what your reality is in terms of what, what do you now what's the influence of luck. And if you're very close to that reality, you are going to be selecting options under uncertain conditions. And that's okay, because I would rather see the world clearly, and say I'm better, even though I can't get perfect, but I'm going to be better than my competitors at understanding what the range of possibilities are. And if I'm better than my competitors at doing that even though I'm going to be very far from perfect, then in the long run I'm going to do better. If we think about it in poker, if I'm better at figuring out what the range of hands you could be holding, you know, is, then I'm going to do better than the next person.

Justin Thomson

I guess that's where the parallels with investing slightly break down, because in poker, you’re observing other people's biases. In investing, you're really observing your own biases and dealing with the market.

Annie Duke

Yeah. So I would actually quibble with you on that one. So when I'm playing poker, it's it's still a market, right. Like there's a bunch of people in there trading. So, does that look a little bit more like, say, the trading floor in 1982? Yes. Right. In the sense that when people were trading, prior to there being electronic trading, like we have now, and particularly when, for example, if you were an options trader, there might be options that only traded on the Phil X, right, or options that only traded in Chicago or options that only traded in New York. So now you're competing against a market per se. So yeah, I mean, again, like closer to 1982, but still a very good analog for trading in the markets today.

Justin Thomson

Minus the cigars and red braces. That was the vision I had when you mentioned 1982. Anyway, biases. Everybody has them. They may not know it, but everybody has their own investment biases. What were yours and how did you course correct.

Annie Duke

Well, again, like I just want to be really clear. What what we're going to mean by course correct here. Right. You know, I think that people are looking for perfection. And what I think about is let's imagine that my biases are going to cause me to make 100 mistakes, right? If I can course correct. So that I make 92 instead of 100, that if you think about that, iterating over time. Right. Like I'm going to accrue those gains over time, that's enough for me. So let's just make sure
that we understand that course correct is not getting perfectly on course compared to some objective omniscient being, right? That I'm just trying to get better from where I started. So, let's just be very clear about that.

In terms of sort of what's the biggest bias that you're seeing among poker players? I can name two, two of the biggest ones because they're they're competing with each other and it would be hard for me to pick one. The first one would be something called, self-serving bias. And self-serving bias, basically goes like this: when you observe an outcome, there's two reasons that that outcome could be occurring, right. It could be, because of skill, because you've, you know, done something that has caused that outcome to occur. And it could be because of luck. Right? So we could think about, like, if you win the lottery, there's a little bit of skill involved. You have to fill out the, the ticket and you, properly, you have to go pay for it, and then you have to save the ticket. Right. So it's a tiny bit of skill element, but, it would almost be completely luck, right. So we could understand that. If we play chess, there's going to be a tiny bit of luck, but it's almost all skill, right? So we can think about sort of the. But most things are sitting somewhere in between that. Right. That there's going to be some combo of luck and skill that are going to be responsible for the outcome that you're observing.

Self-serving bias is when, you think about yourself and you say, why did this great thing happen to me? And you say, because I'm awesome, because I made great decisions, because I'm a great investor. Because I'm a great poker player. Because ‘insert’. Right. Because I'm great at what I do. And then you get a bad outcome. And you say, well, why did that thing happen? And guess what? We don't say because I made a terrible decision. Because I'm not a good investor, because I'm not a good poker player. We say, oh, because I got unlucky. And you can see why that's called self-serving bias, that we're attributing gains to something that is dispositional about us, that we're good at what we do, and we attribute losses to something that's situational, right? That that just happened to us. It's because of luck. So that's that's the first thing that I would say. It's like kind of, that's a big one. Right.

And then the second one would be classic prospect theory. What are you doing under conditions where you're winning in terms of your risk seeking? What are you doing under conditions where you're losing in terms of your risk seeking. And what's happening is that when people are winning, they tend to want to bring their risk to zero. And the best way to do that is to quit. Right. So they, win a bunch of money at the poker table, and then they want to take those gains off the table irrespective of expectancy, right. Irrespective of whether they're positive expected value. And then when they're in a losing state, remember, until they actually take their chips off the table, put them in the rack and go to the cashier's cage, that is a loss on paper. So, they could get it back as long as they keep playing. So they will keep the risk on trying to get back to zero again, not paying attention to expected value. And those decisions about when you keep playing should be expectancy decisions. They shouldn't be about whether you're winning or losing at the time, whether you have whether you have gains on paper or losses on paper.

Justin Thomson

You generalize the question by saying most poker players have these biases. Were you subject to those biases?

Annie Duke

Of course. Absolutely. And so what did I, what did I do to?

Justin Thomson

Yeah, how did you recognize them?

Annie Duke

How did I recognize them? Some combo of: I went to graduate school for cognitive science. I think I had a leg up. And it's just like, for example, the self-serving bias thing is so obvious, and the the sort of prospect theory, you know, the short loss aversion and, that kind of thing is like also very obvious when you're observing other players. So when you walk down the halls of, you know, a poker tournament, you just hear like, oh man, that guy was playing so bad and he got so lucky, and that's why I lost, you know,or  I’m so brilliant and that's why I'm winning. And you just hear it just over and over and over again. And then you observe people being unwilling to quit when they're when they're in the losses. And, you know, there's a lot of ways in which I'm not smart, but one of the ways in which I am is I assume, like if they're doing it, probably so am I. I'm probably not that different than they are. I'm not that special. Right. Like,
this is probably true of all humans.

And so I started thinking about, well, how can I, how can I actually resolve these issues. So, with the problem of playing too long when you're losing, that was a brute force - stop loss. Stop losses are actually really irrational, but it's better than doing the other things. And we can talk about sort of why you're willing to be irrational in order to stop other irrationalities. But that's what I did. I just had a stop loss.

Justin Thomson

I wonder how you deal with new information. So, in poker you got your initial, your initial, you get you, dealt you initial cards. In investment terms, you have your thesis. At the flop you get more information. In investing it may be a data point like an earnings release or an interest rate outcome. How did you deal with information that contradicted your initial belief or thesis?

Annie Duke

I changed my mind.

Justin Thomson

Why do you think people find that so difficult?

Annie Duke

Yeah. It's interesting. So first of all, I think that to be long term successful in poker, the world forces you to change your mind because otherwise you're going to be out of money. Like the feed, the feedback loops are so short, right? They're like, so short and so tight that if you're not changing your mind, then you'll be broke. I mean, you just won't have any money in six months, right?

So, the question becomes like, how do we sort of think about, well, how did I get to be a good mind changer and then how do we apply that to investment? Right. Because the thing is people don't like to change their mind. And there's a lot of reasons that people don't like to change their mind. One is this, this very well-known problem called the sunk cost fallacy, which is once you have something invested in something you don't want to, you don't want to stop it, right? So it's this idea of like, but I've put so much time and effort and energy into this thing, and I'll have wasted all of that if I shift which, which of course is ignoring opportunity costs, right.

The other one, which I think is really big, is called the endowment effect, which is that things that we own, we value more than things that we don't own. And one of the things we own is our own ideas. So, we can think about if we combine sunk cost and endowment together, we've put time and effort and energy, and we've actually made a bet based on the ideas we have. Right? So there's sunk costs, separate and apart from the money we might be losing. But then we also have ownership over those ideas. And so we are going to value them more highly. We're going to think they're better than they are.

If the world contradicts something that we believe, it creates cognitive dissonance. Right? There's a competition between the world and sort of who we are. What we believe, and when we do that we will very often just sort of explain away the facts in order to protect the belief. And one of the most common ways you see that with, with investors is, “well, now it's really cheap. I can't sell it”.

Justin Thomson

And that is the cardinal sin is, is what I'd call thesis creep. I bought this because it was it was growing nicely. It didn't grow. Stock fell. It's now too cheap to sell those. That that is an investment, no, no.

Annie Duke

Right. Exactly. So so how do you think about solving that. It's again, going back to what you said. You know what Howard Mark said. Right. Like get comfortable in the uncertainty and you have to at the outset say to yourself, well, look, I'm making guesses at things. They’re educated guesses and I think that I'm better than the average bear at making these guesses, which is why I have an edge. Right? Because if I if I didn't believe that, I would be indexing. Right. But I'm not indexing, so so I believe that I am a good trader or investor, right? That I can I can make these decisions better than other people. But I have to recognize that these are still educated guesses. And what I have to think about, is a couple of things. One is that the world just may not unfold in a way that I like. Like I could get unlucky, right? Like so as an example, like if I made some investments that relied on certain fundamentals in the market, in 2019 and they were great investments. And then Covid hit, it's like, what am I going to do? Right? Like, I don't I don't control a pandemic that happens. Right. I can't control Russia invading Ukraine. It's just like stuff happens, right? And so we have to recognize that we're going to see, like, how luck might be influencing the outcome. But but the other thing is that if we're say making an investment that's based on a prediction about interest rates, we have to understand that that's probabilistic, that we're making that without perfect knowledge and that, that's okay, because if we observe that interest rates are going against our thesis, well, then part of your process is that when that happens, you have to let go.

Justin Thomson

You've written a whole book called “Quit”, which is all about rehabilitating the idea that quitting is good strategy - feels like poor tactics, but it's it's it's it's good strategy. And that is, despite everything we're told, received wisdom,
aphorisms. Quitters never win, winners never quit, etcetera, etcetera. We're taught the opposite. Why do people find it so hard to quit?

Annie Duke

Well, a lot of what we've been talking about. Right. Because, being in the losses is just, like, really painful. And we don't want to close out accounts in the losses. It feels bad. And we can take lessons from both behavioral economics and motivational psychology. Right. So we can think about these things like the sunk cost effect, for example. We can think about loss aversion. We can think of, you know, prospect theory and sort of what that that's doing to our decision making. We can think about, ambiguity aversion, which is an interesting one, which is sort of think about it as the devil you know versus the devil you don't. Right. Like we we have an aphorism for that, which is we'd rather stick with something we know, even if it's not working, because we're averse to the stuff that we don't know, to going into this wilderness of sort of the unknown and not having, as much certainty going back to Howard Marks, like, we just don't like to live in uncertainty, so we'd rather take a bad path.

And then from the motivational psychology side, we've got these things like cognitive dissonance. Right? And the way that identity is wrapped into, the decisions we make and when we decide to start things that become sort of part of our identity. So if you quit the thing that you started, what does that mean for who you are? Right. That's like really hard. So we just have you know, what I kind of say is that we just have this debris, like we're just gathering debris along the way as we start things and we engage in things and we start to put resources and money and time and attention and our own identity into things that just make it so hard to get out from under that and be willing to change course. It's just really hard.

Justin Thomson

I, I recognize that in myself, in 28 years of being a portfolio manager, that you, early in your career, you had a tendency to want to be right. And the moment you can distinguish between being intellectually right and winning, is a key part of that journey. The other thing I would say is that not quitting is one thing. Doubling down, trebling down, quadrupling down. Those are the ones that kill your performance. And I was lucky enough to have 22 years of attribution when I stepped down from managing money. And if you looked at that, the ones that absolutely killed you was where you'd continue to defend your position, because that really shows up in the statistics. I want to explore the idea of kill criteria, which I believe is a military term, but let's call it, let's call it a take gain, or more commonly, a stop loss. That cannot just be an arbitrary price. There has to be criteria that you build around it. What what do you think the best criteria for those things are?

Annie Duke

Yeah. So look for for for an expert investor, I actually wouldn't ever recommend a stop loss. And the reason is that, as I told you before, it's.

Justin Thomson

Stop loss being an arbitrary price.

Annie Duke

Yeah. And the reason is that it's totally irrational, right? When I sat down at a poker table and let's say I was playing, a game where the stakes were 400 / 800, it just means that the biggest bet you could make was was $800. My stop loss would be 30 times that number. So $24,000. And there's reasons, the reason why it was 30x was because if you look at the range of outcomes that you can have at a poker table, you want to never lose more than you could win on a very good day the next day.

So I do this brute force thing that's pretty irrational, but it's stopping me from doing this other thing that's going to be way more irrational, which is playing when I'm actually losing, theoretically losing, I’m negative expected value. What would I recommend an investor do? That's, that’s different because you have time, right? So, here's the thing. When investors, decide to put risk on, they have a thesis. And here's the amazing thing. Most of them don't write it down, so they know what it is. They can kind of articulate it, but they don't do two things, they don't say specifically; here's my thesis and then this is the really important thing. Here's what it implies about the world, what the world is going to look like in the future at time x y, z. Right. So there's implications about what that means that the world is going to look like. If you were to do that, which you should, then naturally you develop a set of kill criteria out of that. Here's what I would expect the world to look like. Right.

Justin Thomson

Last one to you Annie is, how is AI changing the science of decision making?

Annie Duke

Well, you know, that, that, it's a very complicated question. I think that there are a lot of people who are doing some very interesting work with AI as a guide to help you structure decision making. So, if you can use AI to create structure around your decision making, like you have an assistant, anAI assistant who's, getting you, forcing you to write your thesis down. Who's asking you for your criteria? Who's suggesting kill criteria? Based on your, thesis, for example. I think that that can be incredibly helpful. Right. The downside of this, is that AI is still, and I think this is something that people really don't understand on a very deep level. AI is still like Twitter or Instagram or TikTok or Snapchat, and because it's still an engagement machine, so trying to get you to engage with it, and what that means is that it's going to tell you things that you want to hear. And I think that people don't understand that. Right. And it's because it's trying to please you and it's trying to please me. And we're different people and it has a different history with us. And so I think that what happens is that people take AI as truth. It's just telling you the truth. And it's not true. It's it's it's it's still biased. It's like giving sort of feeding you your biases back. And so it's up to you as a user to prompt it in a million different ways. Well, tell me why that would be wrong. As an example. Right. Like you have to start getting it to be skeptical of its own answers. One of the things that I think is best to do is actually have structured prompts. So if you're trying to get an answer about something, figure out all the prompts it would get, you know, in the absence of a problem that you think would get it to be the most skeptical and, and get it to the closest to the truth, and then you'd have to use those exact same prompts every single time.

Justin Thomson

So I think the lesson is we, in the same way, need to unbias our decisions. You have to unbiased the questions you're asking.

Annie Duke

Yeah. I mean, in the same way that like back in the in the analog days when I was talking to a mentor that I was trying to get help with a hand with, I realized pretty quickly, I better not tell them what happened, because that I would be biasing them. Right. And so so you have to think about, you know, whoever I am talking to, right? Whoever I'm trying to get the help from, am I helping them to give me the most objective answer? Am I recognizing that they might be biased in some way? What am I doing in the way that I'm querying them to help them to not be biased so that I can get the most truthful answer as opposed to, and this is the important thing, the answer that I like the most. And the fact is that most of us are interacting with other people, and with AI, in a way that gets us the answer that we like the most. And what do we like the most? What feels the most satisfying to us? Things that affirm the things that we already believe to be true.

Justin Thomson

I think that's a great way to end. Annie Duke, thank you so much.

Annie Duke

Well, thank you so much for having me.

Justin Thomson

And thank you for listening to The Angle. We look forward to your company on future episodes. You can find more information about this and other topics on our website. Please rate and subscribe wherever you get your podcasts.

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