Transcript
A (0:00)
You got two things that determine how your life turns out. One is luck, which, sorry, you can say I make my own luck all day. That literally is not a sentence of English that makes sense. The other thing is decision quality. At the core of every single decision is a forecast.
B (0:15)
Everything is a bet, right?
A (0:17)
Here's the set of possible outcomes. There's a payoff associated with each of those outcomes. That's how we calculate the expected value toward your goals. But the explanation that we're jumping to is inaccurate and it's because we don't know how to interrogate the data.
B (0:35)
This one is particularly special. I just had an incredible conversation with my friend Annie Duke, the former world class professional poker player. Most of our conversation was about her new book, but she's written several books on decision making and I recommend all of them. Please enjoy my incredible conversation with Annie Duke. Annie Duke, welcome back to Infinite Loops. It's so good to see you in person.
A (1:04)
In person this time, which is so amazing.
B (1:06)
Last time I was on Zoom, I was reminiscing about. Do you remember when we were at Friend of a Farmer and we were talking about your last book?
A (1:13)
Yeah.
B (1:13)
And that turned out to be so great. But before we started recording, you started telling me about your new book, which absolutely fascinates me. We're not going to ask you the whole thing, but first off, tell our listeners and viewers a little bit about what it's about because I think, boy, is the time right for this book.
A (1:31)
Oh, well, thank you. Yeah. So a lot of what I think about, obviously I'm trying to help people to make more effective decisions. And part of the way that I think about that is that at the core of every single decision is a forecast. So, I mean, if you think about any decision you make, right, like you're considering different options. Should I take this way to work or this way to work?
B (1:55)
Everything is a bet, right?
A (1:57)
What you have to do for any of those options that you're considering is make a forecast of how you know how much that option that you select is going to on average help you gain ground toward your goals in alignment with whatever your values are, right? So obviously when we get into the value thing, we get into utility. But so those forecasts, though, as you're thinking about that, right, Like I'm thinking about this option, here's the set of possible outcomes. There's a payoff associated with each of those outcomes. There's a probabilities of those occurring. That's how we calculate the expected value or ground gained toward your Goals, those forecasts can only be as good as the inputs. How do we think about those inputs? Well, we have experiences that we might learn from. A lot of what my past writing has been about is how the way that we interpret those experiences will be biased in a way to cause us to come to inaccurate conclusions or lessons learned about the experiences that we have. So for example, if you think about the centerpiece of thinking in bats, it's really this resulting problem that makes it so that when we have an experience, things go poorly, that we draw the wrong conclusions about what the decision quality was and then that causes us to make worse decisions going forward. If you think about that, that's really a forecasting problem, that it's changing the way you think about the probability of a bad outcome occurring under the same decision circumstances. So that's kind of like how are we learning from our own experience? But there's this other source of input, there's this other input into the decisions that we make, which is the information that we come across. And we're going to come across that information or data, either because it exists out in the wild, we find that data or that information somewhere else. Like a user on social media might offer it to us, or we might self generate. So we could be doing analyses of our portfolio performance, for example, or we might be looking over all the founders we've ever invested in and trying to signal detect what founders are good or what founders are bad, or we're tracking how our sales are doing given maybe different marketing strategies that we're trying. And this is all data generation and we might be creating data visualizations and things like that, and then we're trying to come to conclusions about what is causing us to observe those things that will then help us to make decisions about how to get better outcomes for ourselves in the future. So we might be looking at, can we come to the conclusion, is there an explanation for what we're seeing in the data that says that the marketing strategies that we're trying are actually improving sales? And if we think that that's true, then we may invest more money in those marketing strategies? As an example, we might see data about the efficacy of vaccines and that might drive decisions for ourselves about whether we ought to get a vaccine or not, depending on what we're looking for in terms of our own health outcomes. As an example, that's this other source of input, separate and apart from our own experience that is driving the types of decisions that we make. Anyway, I was thinking about that particular problem and I said it's interesting. Everybody really seems to be very worried about misinformation in this world. But I come across some work from Duncan Watts, who's at Penn, and he had actually been looking at let's divide it into two worlds, kind of like misinformation, let's call it someone just making something up in line to you.