1 00:00:09,760 --> 00:00:13,800 Speaker 1: Hello, and welcome to another episode of The Odd Lots Podcast. 2 00:00:13,840 --> 00:00:18,079 Speaker 1: I'm Joe Watson and I'm Tracy Alloway. So, Tracy, guess 3 00:00:18,160 --> 00:00:22,920 Speaker 1: what today we are going to You're already laughing, um, 4 00:00:23,040 --> 00:00:26,080 Speaker 1: because I know what you're gonna say. Today. We are 5 00:00:26,079 --> 00:00:29,360 Speaker 1: going to return to one of your favorite topics. It's 6 00:00:29,400 --> 00:00:31,360 Speaker 1: a theme that we talk about a lot on this show, 7 00:00:31,400 --> 00:00:33,960 Speaker 1: and I know you love it very much. Okay, So 8 00:00:34,040 --> 00:00:37,920 Speaker 1: it's either chess or poker. So you knew that I 9 00:00:37,960 --> 00:00:39,559 Speaker 1: was big facetious when I said it was one of 10 00:00:39,560 --> 00:00:43,480 Speaker 1: your favorite topics. You could tell yeah, Okay, yes, your 11 00:00:43,720 --> 00:00:47,920 Speaker 1: every single episode is all about games. We do talk 12 00:00:47,960 --> 00:00:50,000 Speaker 1: a lot about games for good reason, and we are 13 00:00:50,520 --> 00:00:54,840 Speaker 1: going to be, among other things, talking about poker and 14 00:00:54,920 --> 00:00:58,320 Speaker 1: some of the lessons that you can learn from playing poker. 15 00:00:58,720 --> 00:01:00,720 Speaker 1: And of course, you know, we Joe about how this 16 00:01:00,800 --> 00:01:04,120 Speaker 1: is a frequent topic of our show. But you know 17 00:01:04,160 --> 00:01:06,959 Speaker 1: there's good reason for it. Yeah. People draw quite a 18 00:01:06,959 --> 00:01:10,520 Speaker 1: few parallels between investing and the sort of game theory 19 00:01:10,600 --> 00:01:14,520 Speaker 1: that you see either in chess or in poker, So 20 00:01:14,600 --> 00:01:18,360 Speaker 1: I get why there's a natural fit. I just struggle 21 00:01:18,400 --> 00:01:21,240 Speaker 1: in every single one of these conversations because you start 22 00:01:21,280 --> 00:01:24,000 Speaker 1: talking about a particular hand or a play, either in 23 00:01:24,080 --> 00:01:28,800 Speaker 1: poker or chess. And I am completely mystified, but I'm 24 00:01:28,840 --> 00:01:31,080 Speaker 1: confident that one day we're actually gonna sit down and 25 00:01:31,120 --> 00:01:32,800 Speaker 1: play a game of poker and this is all going 26 00:01:32,840 --> 00:01:36,039 Speaker 1: to become clear to me. But in the meantime, who 27 00:01:36,120 --> 00:01:38,720 Speaker 1: are we going to be discussing the game with? Well, 28 00:01:38,800 --> 00:01:41,080 Speaker 1: I know you claim to be mystified, but I think 29 00:01:41,120 --> 00:01:44,399 Speaker 1: you saw yourself a little short because you ask great 30 00:01:44,440 --> 00:01:46,960 Speaker 1: questions and all these that I wouldn't necessarily think of. 31 00:01:47,200 --> 00:01:50,120 Speaker 1: I have so thrilled about today's episode. We're talking to 32 00:01:50,600 --> 00:01:54,360 Speaker 1: one of my favorite poker players of all time, Annie Duke. 33 00:01:54,480 --> 00:01:56,800 Speaker 1: She's one of the out winding ist players in World 34 00:01:56,840 --> 00:01:59,920 Speaker 1: Series of Poker history. She want a bracelet. In Omaha 35 00:02:00,240 --> 00:02:05,440 Speaker 1: High Load Tournament, she came tenth in the main event. Basically, 36 00:02:05,480 --> 00:02:08,359 Speaker 1: anyone who's ever watched poker for years on TV has 37 00:02:08,400 --> 00:02:11,440 Speaker 1: seen her numerous times. And she is the author of 38 00:02:11,480 --> 00:02:15,639 Speaker 1: a new book called Thinking in Bets, Making Smarter decisions 39 00:02:15,720 --> 00:02:18,519 Speaker 1: when you don't have all the facts. Anyway, she's sitting 40 00:02:18,560 --> 00:02:20,639 Speaker 1: across from me. It's very awkward to talk about someone 41 00:02:20,680 --> 00:02:23,560 Speaker 1: the third person or four feet away. So Andy, thank 42 00:02:23,560 --> 00:02:25,600 Speaker 1: you very much for joining us. Well, thank you for 43 00:02:25,680 --> 00:02:30,200 Speaker 1: having me and actually speaking to me about That's very 44 00:02:30,480 --> 00:02:33,880 Speaker 1: It was getting starry to get very uncomfortable before we 45 00:02:33,919 --> 00:02:37,840 Speaker 1: get into the book, and we obviously talked about lessons 46 00:02:37,880 --> 00:02:40,160 Speaker 1: you learned from playing poker. How did you get into 47 00:02:40,280 --> 00:02:42,960 Speaker 1: playing poker? I think anyone who watched poker for years 48 00:02:43,200 --> 00:02:45,280 Speaker 1: knows your name and is seeing your numerous times, but 49 00:02:45,480 --> 00:02:48,120 Speaker 1: how did you get into play? So it's a little 50 00:02:48,120 --> 00:02:51,120 Speaker 1: bit of a windy path. After college, I went to 51 00:02:51,160 --> 00:02:53,800 Speaker 1: graduate school at the University of Pennsylvania. I was studying 52 00:02:53,800 --> 00:02:57,120 Speaker 1: cognitive science at a National Science Foundation fellowship. I was 53 00:02:57,160 --> 00:03:00,280 Speaker 1: there for five years actually, and completed my mass years, 54 00:03:00,440 --> 00:03:04,200 Speaker 1: was getting my PhD. I was right at the end 55 00:03:04,560 --> 00:03:08,840 Speaker 1: going out to interview for professorships, and when that happened, 56 00:03:08,880 --> 00:03:10,720 Speaker 1: I actually got sick as I was going out for 57 00:03:10,840 --> 00:03:13,320 Speaker 1: my actually my first job interview in my first job 58 00:03:13,360 --> 00:03:15,839 Speaker 1: talk and in academics you can only do that once 59 00:03:15,840 --> 00:03:18,840 Speaker 1: a year. So because I was in the hospital, I 60 00:03:18,880 --> 00:03:22,360 Speaker 1: had to defer basically for a year. So during that 61 00:03:22,480 --> 00:03:26,600 Speaker 1: year I wasn't in school, I didn't have a fellowship, 62 00:03:26,880 --> 00:03:31,360 Speaker 1: I needed money, and my brother, Howard Lederer, was already 63 00:03:31,840 --> 00:03:35,560 Speaker 1: an amazing player. He'd been playing for ten years professional player, 64 00:03:35,600 --> 00:03:37,240 Speaker 1: and actually when I was in graduate school used to 65 00:03:37,240 --> 00:03:39,440 Speaker 1: bring me to Vegas once a year as like a treat, 66 00:03:40,040 --> 00:03:42,560 Speaker 1: as a vacation, and he would give me a tiny 67 00:03:42,560 --> 00:03:44,640 Speaker 1: bit of money to go play. Like it was a 68 00:03:44,640 --> 00:03:46,600 Speaker 1: little boring because he was playing all the time, so 69 00:03:46,680 --> 00:03:48,480 Speaker 1: I would sit and watch him. But sometimes I'd be like, 70 00:03:48,520 --> 00:03:49,960 Speaker 1: can I go play? And he'd go send me to 71 00:03:50,040 --> 00:03:52,040 Speaker 1: some game where you'd buy in for like fifty dollars 72 00:03:52,040 --> 00:03:54,839 Speaker 1: and you know, here, go have fun. So I knew 73 00:03:54,880 --> 00:03:57,680 Speaker 1: a little bit about the game, and he obviously knew 74 00:03:57,720 --> 00:04:00,120 Speaker 1: a lot about the game and suggested to me in 75 00:04:00,160 --> 00:04:03,600 Speaker 1: that year that maybe I could play poker in the 76 00:04:03,640 --> 00:04:06,120 Speaker 1: meantime while I was waiting to go back out onto 77 00:04:06,120 --> 00:04:10,040 Speaker 1: the job market and academics. So you know, the meantime 78 00:04:10,080 --> 00:04:14,320 Speaker 1: turned into twenty years and uh from nine two is 79 00:04:14,320 --> 00:04:17,240 Speaker 1: when I when I first that happened, I turned pro, 80 00:04:17,720 --> 00:04:20,080 Speaker 1: and then I guess two thousand twelve I retired, So 81 00:04:20,200 --> 00:04:22,760 Speaker 1: it was a very long meantime, but that's how I 82 00:04:22,800 --> 00:04:25,039 Speaker 1: got in and he and he, along with a group 83 00:04:25,080 --> 00:04:27,960 Speaker 1: of players that he was very embedded with, became my 84 00:04:28,080 --> 00:04:33,000 Speaker 1: mentors in the game. Do cognitive scientists make good poker players? Like, 85 00:04:33,120 --> 00:04:35,320 Speaker 1: is there a natural edge that you would have over 86 00:04:35,440 --> 00:04:38,320 Speaker 1: other players when it comes to analyzing I guess the 87 00:04:38,720 --> 00:04:41,520 Speaker 1: behavior of other people at the table, so I don't. 88 00:04:41,600 --> 00:04:44,520 Speaker 1: It's a small sample size. I'm not sure how much 89 00:04:45,480 --> 00:04:48,440 Speaker 1: we can say from the data, but I would imagine 90 00:04:48,480 --> 00:04:51,120 Speaker 1: that someone who studied cognitive science would have an edge 91 00:04:51,160 --> 00:04:53,760 Speaker 1: because you you spend a lot of time, obviously in 92 00:04:53,800 --> 00:04:56,880 Speaker 1: cognitive science thinking about how people think. Not only thinking 93 00:04:56,920 --> 00:04:59,080 Speaker 1: about how people think, but thinking about how people learn. 94 00:04:59,640 --> 00:05:02,719 Speaker 1: How are people biased in the way that they process information? 95 00:05:03,480 --> 00:05:07,720 Speaker 1: Hopefully how you can d bias yourself as you process information? 96 00:05:08,040 --> 00:05:10,560 Speaker 1: Just training and a scientist get you to think about 97 00:05:10,600 --> 00:05:13,720 Speaker 1: doing like a b testing or why is this hypothesis 98 00:05:13,720 --> 00:05:15,840 Speaker 1: not true? Which is I think it's very important for 99 00:05:16,320 --> 00:05:18,840 Speaker 1: good decision making and poker, and I think you get 100 00:05:18,920 --> 00:05:21,760 Speaker 1: used to sort of dealing with very noisy data. So 101 00:05:22,040 --> 00:05:24,680 Speaker 1: you know, I'd like to think yes, but you know, 102 00:05:24,720 --> 00:05:27,160 Speaker 1: we haven't really run the experiment too much because I'm 103 00:05:27,160 --> 00:05:29,320 Speaker 1: not sure. There's a lot of other cognitive scientists who 104 00:05:29,320 --> 00:05:33,720 Speaker 1: when and became poker player. Your cognitive science background aside 105 00:05:33,920 --> 00:05:36,479 Speaker 1: you mentioned your brother Howard Letter, are also one of 106 00:05:36,480 --> 00:05:40,640 Speaker 1: the most recognizable poker players in the world from recent years. 107 00:05:41,080 --> 00:05:44,160 Speaker 1: Could you identify something that you and your brother have 108 00:05:44,720 --> 00:05:46,919 Speaker 1: that you would be able to recognize outside the poker player, 109 00:05:47,320 --> 00:05:50,160 Speaker 1: outside the poker table. That's sort of something about your 110 00:05:50,200 --> 00:05:52,240 Speaker 1: characters or something. What what you think of the world 111 00:05:52,520 --> 00:05:56,920 Speaker 1: that worked for both of you is becoming such successful players. Well, 112 00:05:56,960 --> 00:05:58,719 Speaker 1: I would say that the two things that my brother 113 00:05:58,839 --> 00:06:02,159 Speaker 1: and I probably haven't a man that was really helpful 114 00:06:02,440 --> 00:06:06,520 Speaker 1: is neither. I think we're both kind of out at 115 00:06:06,520 --> 00:06:09,760 Speaker 1: the tail in terms of how we emotional react to losing, 116 00:06:10,200 --> 00:06:12,200 Speaker 1: which I think is really actually important. I think that 117 00:06:12,240 --> 00:06:16,039 Speaker 1: we're both relatively emotionally steady. We're not so well. In 118 00:06:16,080 --> 00:06:17,880 Speaker 1: the book, I talked about this thing called tilt, which 119 00:06:17,920 --> 00:06:20,520 Speaker 1: is what the poker players used for getting emotionally unhanged 120 00:06:20,520 --> 00:06:23,240 Speaker 1: in them making bad decisions because of it. Um So 121 00:06:23,279 --> 00:06:26,039 Speaker 1: I think that that's number one. And then I think 122 00:06:26,040 --> 00:06:28,920 Speaker 1: the other thing that we probably are pretty good at, 123 00:06:28,960 --> 00:06:30,880 Speaker 1: and I think it's because of the way that our 124 00:06:31,040 --> 00:06:34,479 Speaker 1: family had discussions when we were growing up, is that, 125 00:06:34,680 --> 00:06:38,560 Speaker 1: um I think we're pretty good at hearing opposing viewpoints 126 00:06:38,560 --> 00:06:41,920 Speaker 1: and sort of dealing with dissent hopefully in a non 127 00:06:41,960 --> 00:06:45,280 Speaker 1: disagreeable way, but really thinking about let's try to get 128 00:06:45,320 --> 00:06:47,640 Speaker 1: to the truth of the matter, as opposed to sort 129 00:06:47,640 --> 00:06:52,279 Speaker 1: of just making everybody feel good about themselves. That's just 130 00:06:52,320 --> 00:06:54,359 Speaker 1: sort of the dinner table kind of conversation that we 131 00:06:54,400 --> 00:06:56,080 Speaker 1: had when we were we were growing up, which I 132 00:06:56,080 --> 00:06:59,520 Speaker 1: think actually ended up being incredibly good training for becoming 133 00:06:59,560 --> 00:07:02,520 Speaker 1: a good player. Wait, can I ask why in in 134 00:07:02,560 --> 00:07:06,560 Speaker 1: my poker ignorance once again, like, why would having those 135 00:07:06,560 --> 00:07:09,080 Speaker 1: sorts of discussions at the dinner table translate into a 136 00:07:09,120 --> 00:07:13,360 Speaker 1: successful poker career? Well, that kind of view about information 137 00:07:13,400 --> 00:07:16,160 Speaker 1: that disagrees with you is really good for poker. But 138 00:07:16,160 --> 00:07:19,360 Speaker 1: but actually, that's one of the central themes of my book, 139 00:07:19,400 --> 00:07:22,280 Speaker 1: Thinking of Bets, is that it's really important for all 140 00:07:22,320 --> 00:07:25,000 Speaker 1: good decision making. We already know what we know, and 141 00:07:25,040 --> 00:07:27,640 Speaker 1: we already have the beliefs that we have. So the 142 00:07:27,720 --> 00:07:30,760 Speaker 1: question is do you approach the world from the standpoint 143 00:07:30,840 --> 00:07:33,280 Speaker 1: of I want to be right versus I want to 144 00:07:33,280 --> 00:07:36,960 Speaker 1: be accurate? And there's a difference. Right, So if I 145 00:07:36,960 --> 00:07:38,920 Speaker 1: want to be right, it means that I have these 146 00:07:38,920 --> 00:07:42,160 Speaker 1: beliefs I have, I've made these decisions, I have these 147 00:07:42,160 --> 00:07:46,120 Speaker 1: strategic viewpoints or this particular analysis, and I want to 148 00:07:46,200 --> 00:07:50,560 Speaker 1: affirm that what I think is true. So that's approaching 149 00:07:50,560 --> 00:07:53,440 Speaker 1: the world from being right. And what's really bad about 150 00:07:53,480 --> 00:07:55,280 Speaker 1: that kind of attitude if I want to be right, 151 00:07:55,400 --> 00:07:59,560 Speaker 1: is that you tend to swat away information that dissents 152 00:07:59,600 --> 00:08:02,200 Speaker 1: with you, information that disagrees with you, information that might 153 00:08:02,200 --> 00:08:05,000 Speaker 1: cause you to calibrate your belief in some way away 154 00:08:05,040 --> 00:08:09,520 Speaker 1: from your prior um. That's not great for learning. And obviously, 155 00:08:09,520 --> 00:08:12,920 Speaker 1: because all of our decisions are informed by our beliefs, 156 00:08:13,120 --> 00:08:15,920 Speaker 1: if we're not calibrating our beliefs while our decisions are 157 00:08:15,920 --> 00:08:18,320 Speaker 1: going to suffer for that. I know we're going to 158 00:08:18,360 --> 00:08:21,760 Speaker 1: talk about the applicability of all this to market, but 159 00:08:21,840 --> 00:08:24,960 Speaker 1: I think right there is one of the most clear lessons. 160 00:08:25,000 --> 00:08:28,000 Speaker 1: And you hear it from traders as you traders former 161 00:08:28,080 --> 00:08:31,560 Speaker 1: thesis about our investors, former thesis about why they think 162 00:08:31,840 --> 00:08:34,880 Speaker 1: some instrument will do X and y. But the really 163 00:08:34,920 --> 00:08:38,079 Speaker 1: good ones discard that belief as soon as the counter 164 00:08:38,240 --> 00:08:42,440 Speaker 1: evidence emerges, whereas the bad ones, you know, really you know, 165 00:08:42,520 --> 00:08:45,280 Speaker 1: discard all the counter evidence because they just are so 166 00:08:45,360 --> 00:08:48,800 Speaker 1: into the idea of having been right in their original 167 00:08:48,960 --> 00:08:53,200 Speaker 1: formation right exactly. So if we approach the world from 168 00:08:53,360 --> 00:08:56,360 Speaker 1: this idea of uh, we want to be accurate, meaning 169 00:08:56,360 --> 00:08:58,840 Speaker 1: that we want to form the most accurate representation of 170 00:08:58,880 --> 00:09:02,600 Speaker 1: the objective true. Now what happens is the information that 171 00:09:02,640 --> 00:09:05,400 Speaker 1: disagrees with us actually we view for a total through 172 00:09:05,440 --> 00:09:08,800 Speaker 1: a totally different frame. It's no longer threatening, it's actually, 173 00:09:08,840 --> 00:09:11,080 Speaker 1: we think about it is incredibly helpful because it helps 174 00:09:11,160 --> 00:09:14,120 Speaker 1: us move toward that goal of accuracy. And if you 175 00:09:14,160 --> 00:09:16,560 Speaker 1: think about it, like, you already have this belief, so 176 00:09:16,640 --> 00:09:18,640 Speaker 1: you can already are you for it? Just fine? You've 177 00:09:18,640 --> 00:09:21,880 Speaker 1: probably already found a lot of information that supports the 178 00:09:21,920 --> 00:09:24,600 Speaker 1: belief you have. So the people who disagree with you, 179 00:09:24,679 --> 00:09:29,280 Speaker 1: who have alternative hypotheses or different perspectives or facts that 180 00:09:29,320 --> 00:09:31,800 Speaker 1: maybe you haven't discovered that would argue against your belief, 181 00:09:31,800 --> 00:09:36,200 Speaker 1: are actually the most valuable. So having a really open 182 00:09:36,320 --> 00:09:40,040 Speaker 1: stance towards that kind of information is what really allows 183 00:09:40,040 --> 00:09:42,080 Speaker 1: you to be a good decision maker. And if you 184 00:09:42,160 --> 00:09:44,600 Speaker 1: don't have that stance, what's going to happen is that 185 00:09:44,800 --> 00:09:47,480 Speaker 1: you're going to swat away all the stuff that disagrees. 186 00:09:47,559 --> 00:09:49,880 Speaker 1: And here's the problem, and this is speaking to people 187 00:09:49,880 --> 00:09:52,400 Speaker 1: who are traders who have that stance of wanting to 188 00:09:52,440 --> 00:09:55,600 Speaker 1: swat away is. It's a particularly bad problem for people 189 00:09:55,600 --> 00:09:59,560 Speaker 1: who are smart, because if you're smart, you're really good 190 00:09:59,559 --> 00:10:01,880 Speaker 1: at space in. You're really good at arguing your case 191 00:10:02,040 --> 00:10:05,600 Speaker 1: or giving a rationale for why you're right, or or 192 00:10:05,840 --> 00:10:08,840 Speaker 1: you're really good at picking apart and discrediting things that 193 00:10:08,920 --> 00:10:11,760 Speaker 1: disagree with you, right like you're like, oh, you know 194 00:10:11,800 --> 00:10:14,160 Speaker 1: the ND it's too small, or your hypothesis is wrong 195 00:10:14,200 --> 00:10:17,920 Speaker 1: for this reason because you're missing this thing or whatever 196 00:10:17,960 --> 00:10:19,920 Speaker 1: it might be. You're so good at spin. I mean, 197 00:10:19,960 --> 00:10:22,000 Speaker 1: if I'm a politician and I'm going to send someone 198 00:10:22,040 --> 00:10:24,480 Speaker 1: in the spin room, I'm sending the super smart guy. 199 00:10:25,320 --> 00:10:28,640 Speaker 1: So what do you do if you're a smart trader 200 00:10:28,840 --> 00:10:32,520 Speaker 1: and you're convinced that you've made the right decision, but 201 00:10:32,679 --> 00:10:36,600 Speaker 1: the market moves against you even though you're right, Like 202 00:10:36,760 --> 00:10:39,480 Speaker 1: you know, for instance, say you're looking at a product 203 00:10:39,559 --> 00:10:41,640 Speaker 1: that you completely disagree with and you think at some 204 00:10:41,720 --> 00:10:43,880 Speaker 1: point is going to end in a very very bad way, 205 00:10:44,000 --> 00:10:48,760 Speaker 1: but it doesn't happen for years. How do you convince 206 00:10:48,840 --> 00:10:51,800 Speaker 1: yourself that something else might be true? Or how do 207 00:10:51,840 --> 00:10:53,680 Speaker 1: you come to grips that even though you're making the 208 00:10:53,760 --> 00:10:56,760 Speaker 1: right decision, the market just isn't playing along with you. 209 00:10:57,200 --> 00:11:00,720 Speaker 1: So I think that the important thing is to realize 210 00:11:00,760 --> 00:11:04,160 Speaker 1: that as we run on our own, it's very very 211 00:11:04,160 --> 00:11:07,480 Speaker 1: hard to overcome our own biases. There this biased way 212 00:11:07,480 --> 00:11:09,240 Speaker 1: that we really kind of want to approach the world 213 00:11:09,240 --> 00:11:12,679 Speaker 1: and process information. But we're pretty good at spotting other 214 00:11:12,760 --> 00:11:15,240 Speaker 1: people's biases, and you can kind of feel that, right 215 00:11:15,280 --> 00:11:17,120 Speaker 1: you can. You can sort of tell like when another 216 00:11:17,160 --> 00:11:20,160 Speaker 1: person like, oh, come on, clearly you're being biased in 217 00:11:20,240 --> 00:11:22,360 Speaker 1: the way that you're thinking about this. So we can 218 00:11:22,440 --> 00:11:24,320 Speaker 1: use this to our advantage and we can form a 219 00:11:24,360 --> 00:11:27,960 Speaker 1: really good decision. Pod. So the idea is get a 220 00:11:28,040 --> 00:11:30,760 Speaker 1: group around you, and you obviously can do this in 221 00:11:30,760 --> 00:11:32,600 Speaker 1: your own enterprise. You can do this with a group 222 00:11:32,679 --> 00:11:36,840 Speaker 1: of friends, whatever it might be. Create a charter, which 223 00:11:36,880 --> 00:11:40,520 Speaker 1: is a commitment to accuracy, accountability to your beliefs, and 224 00:11:40,640 --> 00:11:44,880 Speaker 1: openness to dissent. And then this is the really important thing. 225 00:11:45,240 --> 00:11:48,280 Speaker 1: Try to coordinate yourself off from outcomes as much as possible. 226 00:11:49,080 --> 00:11:51,040 Speaker 1: Because here's the problem. Yes, if we have like a 227 00:11:51,080 --> 00:11:54,040 Speaker 1: big data set that where we can go and look 228 00:11:54,200 --> 00:11:56,480 Speaker 1: and and we can say, well, you know, these are 229 00:11:57,080 --> 00:11:58,880 Speaker 1: what I see in the data, Like we have ten 230 00:11:58,920 --> 00:12:02,079 Speaker 1: thousand coin flips. Great, go look at the ten thousand 231 00:12:02,120 --> 00:12:03,960 Speaker 1: coin flips, and that's going to tell you something about 232 00:12:03,960 --> 00:12:06,720 Speaker 1: the underlying mathematics. But when you're just looking at one 233 00:12:06,800 --> 00:12:09,959 Speaker 1: or two results, it can become extremely difficult. And if 234 00:12:09,960 --> 00:12:13,079 Speaker 1: you know what the outcome is, it actually will really 235 00:12:13,080 --> 00:12:17,120 Speaker 1: in fact your decision making. So as much as you 236 00:12:17,160 --> 00:12:20,480 Speaker 1: can discuss the decisions as to why you why you 237 00:12:20,520 --> 00:12:23,360 Speaker 1: decided to bet on the product or against the product 238 00:12:23,480 --> 00:12:26,160 Speaker 1: or whatever it might be without telling them sort of 239 00:12:26,160 --> 00:12:29,520 Speaker 1: where it's sitting at that point, um, and deconstruct the 240 00:12:29,520 --> 00:12:33,280 Speaker 1: decision without the outcome as much as possible. Either do 241 00:12:33,400 --> 00:12:36,000 Speaker 1: it before, say like if you're a trader, do it 242 00:12:36,040 --> 00:12:38,960 Speaker 1: before the option expires. That would be really good, um, 243 00:12:39,040 --> 00:12:41,319 Speaker 1: and really go through that decision processes you're trying to 244 00:12:41,360 --> 00:12:43,640 Speaker 1: think about further decision making that goes with that, or 245 00:12:43,640 --> 00:12:46,080 Speaker 1: if the option has expired, go talk to people as 246 00:12:46,120 --> 00:12:48,800 Speaker 1: if just don't tell them what how it kind of 247 00:12:48,800 --> 00:12:51,560 Speaker 1: turned out? And I think that then you can really 248 00:12:51,640 --> 00:12:55,000 Speaker 1: check your biases for each other and really have that 249 00:12:55,080 --> 00:12:59,000 Speaker 1: commitment to look, I want to understand why do you 250 00:12:59,040 --> 00:13:01,520 Speaker 1: think I'm wrong? And if you're more willing to ask 251 00:13:01,559 --> 00:13:04,160 Speaker 1: that question as opposed to why do you think I'm right, 252 00:13:04,800 --> 00:13:06,560 Speaker 1: You're going to get a lot farther in these kinds 253 00:13:06,559 --> 00:13:11,240 Speaker 1: of decision groups. And the title of your book thinking 254 00:13:11,320 --> 00:13:14,160 Speaker 1: in bets, making smarter decisions when you when you don't 255 00:13:14,160 --> 00:13:17,960 Speaker 1: have all the facts. I'm sure we've discussed on previous episodes, 256 00:13:18,000 --> 00:13:20,120 Speaker 1: but explained to us, I mean, this is like the 257 00:13:20,200 --> 00:13:22,600 Speaker 1: essential element of poker, which is that you see your 258 00:13:22,640 --> 00:13:26,160 Speaker 1: whole cards, and you see the community cars that are 259 00:13:26,160 --> 00:13:28,080 Speaker 1: in the middle. Let's say you're playing Texas hold them. 260 00:13:28,080 --> 00:13:30,640 Speaker 1: But everyone also has their cards that you don't see, 261 00:13:31,000 --> 00:13:33,439 Speaker 1: and so you don't have all the facts in a 262 00:13:33,480 --> 00:13:36,640 Speaker 1: given hand. So explain to us sort of like the 263 00:13:36,800 --> 00:13:39,480 Speaker 1: essential way that just from a poker perspective that you 264 00:13:39,679 --> 00:13:43,040 Speaker 1: think about the world. So poker is really different than 265 00:13:43,120 --> 00:13:46,200 Speaker 1: a game like chess, right, So so in chess, I 266 00:13:46,240 --> 00:13:49,680 Speaker 1: can see all the pieces. So theoretically, if I have 267 00:13:49,840 --> 00:13:52,199 Speaker 1: enough computing power, and we actually kind of know this 268 00:13:52,600 --> 00:13:55,520 Speaker 1: because of computers now, I mean computers have soft checkers. 269 00:13:55,800 --> 00:13:57,400 Speaker 1: If I have enough computing power, I can just sort 270 00:13:57,400 --> 00:13:59,000 Speaker 1: of compute the game out because I can see if 271 00:13:59,040 --> 00:14:00,600 Speaker 1: I do this move, I know what all your possible 272 00:14:00,640 --> 00:14:03,000 Speaker 1: moves are because I can see your whole position. Not 273 00:14:03,080 --> 00:14:05,760 Speaker 1: only that there's no element of luck, so nobody's like 274 00:14:05,960 --> 00:14:08,360 Speaker 1: rolling a pair of dice and then you know it 275 00:14:08,480 --> 00:14:11,680 Speaker 1: lands eleven and your bishop goes away. Um. So that 276 00:14:11,760 --> 00:14:14,720 Speaker 1: makes it actually a really different decision problem than poker. 277 00:14:14,880 --> 00:14:19,160 Speaker 1: In poker, I can't see everybody else's cards, and there's 278 00:14:19,160 --> 00:14:22,520 Speaker 1: this big luck element, so I have no control over 279 00:14:22,880 --> 00:14:25,880 Speaker 1: the cards that come. So I could have, you know, 280 00:14:25,920 --> 00:14:28,560 Speaker 1: a hand that's to win. We could have all our 281 00:14:28,600 --> 00:14:31,360 Speaker 1: money on the table and you know I lose because 282 00:14:31,360 --> 00:14:35,080 Speaker 1: you hit. So what that means is that in poker, 283 00:14:35,120 --> 00:14:37,080 Speaker 1: your approach has to be twofold. One is to get 284 00:14:37,120 --> 00:14:41,240 Speaker 1: really comfortable with this luck element that on any given try, 285 00:14:41,320 --> 00:14:44,160 Speaker 1: on any given single flip of the coin, you can't 286 00:14:44,160 --> 00:14:46,480 Speaker 1: predict whether it's going to land heads or tails, and 287 00:14:46,520 --> 00:14:48,640 Speaker 1: you don't want to read too much into whether it 288 00:14:48,720 --> 00:14:51,080 Speaker 1: landed heads or tails. Right, So if I know that 289 00:14:51,120 --> 00:14:52,920 Speaker 1: the coin is fair, and I flip a coin three 290 00:14:52,920 --> 00:14:55,520 Speaker 1: times and it lands heads three times, I wouldn't want 291 00:14:55,520 --> 00:14:57,880 Speaker 1: to then want to call tails on the next one, 292 00:14:58,200 --> 00:15:01,200 Speaker 1: just because of those three outcomes. You have to get very, 293 00:15:01,280 --> 00:15:04,280 Speaker 1: very comfortable with that separation between outcomes and decisions and 294 00:15:04,440 --> 00:15:06,920 Speaker 1: understand that you need to pull those apart. That's the 295 00:15:07,000 --> 00:15:09,680 Speaker 1: luck thing that you have to get comfortable with. The 296 00:15:09,800 --> 00:15:12,560 Speaker 1: hidden information thing is that you really have to approach 297 00:15:12,600 --> 00:15:14,560 Speaker 1: the world by trying to get information out of the 298 00:15:14,600 --> 00:15:17,640 Speaker 1: market that you're dealing with. So you have to think about, 299 00:15:17,680 --> 00:15:20,400 Speaker 1: how do I move within this market, how do I 300 00:15:20,520 --> 00:15:23,840 Speaker 1: make tests within this market to get people to narrow 301 00:15:23,920 --> 00:15:26,600 Speaker 1: down from me what their hand is. So the way 302 00:15:26,600 --> 00:15:30,160 Speaker 1: that I bet is all about trying to get you 303 00:15:30,520 --> 00:15:32,440 Speaker 1: to respond in a way that tells me a little 304 00:15:32,440 --> 00:15:34,880 Speaker 1: bit about your whole cards. And then I also use 305 00:15:34,920 --> 00:15:37,320 Speaker 1: a lot of stuff about what I know about human 306 00:15:37,400 --> 00:15:39,720 Speaker 1: nature the way that humans generally bet. So those would 307 00:15:39,720 --> 00:15:41,520 Speaker 1: be say the prior that I come into if I've 308 00:15:41,560 --> 00:15:44,600 Speaker 1: never played with you before, and then as soon as 309 00:15:44,640 --> 00:15:48,440 Speaker 1: I see you start doing things, I adjust the prior immediately, 310 00:15:48,480 --> 00:15:51,600 Speaker 1: so I start updating off of that. So I'll look 311 00:15:51,600 --> 00:15:55,440 Speaker 1: at the frequency with you with which generally you'll play hands, 312 00:15:56,360 --> 00:15:59,600 Speaker 1: whether you know generally raise or fold, are you like 313 00:15:59,640 --> 00:16:03,120 Speaker 1: a geeky player or a straightforward player, and all of 314 00:16:03,120 --> 00:16:05,200 Speaker 1: those things I'm updating as I start to collect data 315 00:16:05,240 --> 00:16:07,720 Speaker 1: on you. Um and then again I have to be 316 00:16:07,760 --> 00:16:09,760 Speaker 1: asking the right questions with my chips in the way 317 00:16:09,800 --> 00:16:12,400 Speaker 1: that I bet, so you really approach the world from 318 00:16:12,440 --> 00:16:14,560 Speaker 1: I don't have a lot of control over the luck element, 319 00:16:14,880 --> 00:16:16,760 Speaker 1: so let me not get too upset about that. What 320 00:16:16,840 --> 00:16:19,520 Speaker 1: I do have control over is this hidden information element. 321 00:16:19,840 --> 00:16:23,320 Speaker 1: So I want to approach the world as very hungry, 322 00:16:23,440 --> 00:16:26,400 Speaker 1: like I want to be hungry to collect information about you, 323 00:16:26,400 --> 00:16:27,880 Speaker 1: and I want to make sure that I'm asking good 324 00:16:27,960 --> 00:16:31,200 Speaker 1: questions to get it. So when it comes to collecting 325 00:16:31,240 --> 00:16:36,240 Speaker 1: that sort of information, can you apply your technique at 326 00:16:36,240 --> 00:16:41,320 Speaker 1: the poker table outside of that realm, like in everyday 327 00:16:41,360 --> 00:16:45,200 Speaker 1: decision making, how would you collect information from the people 328 00:16:45,200 --> 00:16:47,479 Speaker 1: that you're dealing with? How would you ask the questions 329 00:16:47,560 --> 00:16:50,200 Speaker 1: to get the hidden information that you're trying to seek. 330 00:16:50,440 --> 00:16:52,720 Speaker 1: I think it's two fold. I think one is what 331 00:16:52,760 --> 00:16:55,280 Speaker 1: I said before, of approached the world by asking why 332 00:16:55,280 --> 00:16:57,880 Speaker 1: I'm wrong? Right, So if I if I, if I 333 00:16:57,920 --> 00:16:59,680 Speaker 1: have some sort of belief, it would be really good 334 00:16:59,680 --> 00:17:01,520 Speaker 1: trace if I said to you, so can you argue 335 00:17:01,520 --> 00:17:03,560 Speaker 1: against me? Like tell me what I'm missing? Is there 336 00:17:03,600 --> 00:17:05,320 Speaker 1: something you know that I'm missing? So you want to 337 00:17:05,359 --> 00:17:07,879 Speaker 1: say what am I missing? A lot? I think the 338 00:17:07,920 --> 00:17:10,399 Speaker 1: other thing that's actually really important is that you can 339 00:17:10,640 --> 00:17:13,840 Speaker 1: get people to give you information naturally if you express 340 00:17:13,880 --> 00:17:16,800 Speaker 1: yourself in a way that invites it. So there's a 341 00:17:16,840 --> 00:17:19,760 Speaker 1: difference between saying I believe that this is true, and 342 00:17:19,800 --> 00:17:24,280 Speaker 1: I'm sure and I believe this is true, and I'm 343 00:17:25,000 --> 00:17:26,960 Speaker 1: on this or six on this, and let me tell 344 00:17:27,000 --> 00:17:29,480 Speaker 1: you how I came to the sixty percent. So, like 345 00:17:29,480 --> 00:17:31,440 Speaker 1: I'll give you like a super simple example, if I 346 00:17:31,480 --> 00:17:35,760 Speaker 1: announce some movie one best picture. Right, So let's say 347 00:17:35,800 --> 00:17:39,240 Speaker 1: that I'm thinking, oh, some movie like Citizen Kane, Citizen 348 00:17:39,320 --> 00:17:42,919 Speaker 1: Kane one best picture, and I just announced that. You 349 00:17:43,000 --> 00:17:45,440 Speaker 1: might not share information with me for two reasons. One 350 00:17:45,520 --> 00:17:48,399 Speaker 1: is that you may believe that it didn't but you 351 00:17:48,440 --> 00:17:50,720 Speaker 1: may now think I'm wrong because I've expressed myself with 352 00:17:50,760 --> 00:17:53,480 Speaker 1: such great certainty. So you don't share it with me 353 00:17:53,520 --> 00:17:56,199 Speaker 1: because you're afraid sort of being embarrassed, and you're going 354 00:17:56,280 --> 00:17:59,240 Speaker 1: to go look up on Google behind my back later, right, 355 00:17:59,280 --> 00:18:02,040 Speaker 1: so to check on that. Um, the other reason why 356 00:18:02,080 --> 00:18:04,720 Speaker 1: I wouldn't dream of it. The other reason why you 357 00:18:04,800 --> 00:18:06,600 Speaker 1: might not share the information with me is that you're 358 00:18:06,640 --> 00:18:09,200 Speaker 1: worried that I'm wrong. This is particularly problem a problem 359 00:18:09,240 --> 00:18:12,560 Speaker 1: for people in leadership roles. So if a leader expresses 360 00:18:12,560 --> 00:18:14,880 Speaker 1: something with great certainty in the room, as if it's 361 00:18:14,880 --> 00:18:18,080 Speaker 1: a hundred percent sure and they are absolutely right. The 362 00:18:18,119 --> 00:18:21,000 Speaker 1: people in the room will sometimes not share for fear 363 00:18:21,040 --> 00:18:24,400 Speaker 1: of embarrassing the person at the front of the room 364 00:18:24,600 --> 00:18:28,640 Speaker 1: or not being viewed as a team player, so they 365 00:18:28,680 --> 00:18:32,119 Speaker 1: won't share information back with you for a variety of reasons, 366 00:18:32,160 --> 00:18:33,880 Speaker 1: and that's really bad. But if you say, I think 367 00:18:33,880 --> 00:18:36,520 Speaker 1: Citizen came one Best Picture and I'm like six on it, 368 00:18:36,560 --> 00:18:38,639 Speaker 1: I mean, I know it's a good film, but I 369 00:18:38,680 --> 00:18:40,280 Speaker 1: also know that some of the things we think of 370 00:18:40,320 --> 00:18:43,240 Speaker 1: as the greatest films of all time didn't necessarily win 371 00:18:43,280 --> 00:18:46,080 Speaker 1: Best Picture. And that's how I express it to you. Now, 372 00:18:46,119 --> 00:18:48,440 Speaker 1: I've just opened the door wide for you to become 373 00:18:48,480 --> 00:18:51,919 Speaker 1: a collaborator with me. So when you approach the world 374 00:18:52,400 --> 00:18:55,480 Speaker 1: wrapping in this kind of uncertainty, you invite people to 375 00:18:55,480 --> 00:18:58,920 Speaker 1: share information, and that causes you to be an information gatherer. 376 00:18:59,040 --> 00:19:01,800 Speaker 1: We did it win best well? I googled it for 377 00:19:01,840 --> 00:19:05,280 Speaker 1: the book and it did not. It did not, So 378 00:19:05,359 --> 00:19:06,879 Speaker 1: I googled it for the book because it feels like 379 00:19:06,920 --> 00:19:08,800 Speaker 1: it should have, right, um, and I have a bunch 380 00:19:08,840 --> 00:19:11,399 Speaker 1: of examples in there, like nobody's name got changed at 381 00:19:11,440 --> 00:19:13,680 Speaker 1: Ellis Island. I'm sorry you're pretty surprised by that. I 382 00:19:13,720 --> 00:19:17,840 Speaker 1: didn't know that. I didn't either. I'm very curious about ego, 383 00:19:18,240 --> 00:19:21,120 Speaker 1: and so is going back to this idea of getting 384 00:19:21,160 --> 00:19:23,960 Speaker 1: attached to our stories and getting attached to being right. 385 00:19:24,760 --> 00:19:27,239 Speaker 1: Do you here, is there a process for sort of 386 00:19:27,280 --> 00:19:29,919 Speaker 1: divorcing ourselves from our egos so that we're just not 387 00:19:30,080 --> 00:19:34,080 Speaker 1: attached to the idea of being right. So it's a 388 00:19:34,080 --> 00:19:35,960 Speaker 1: little bit of a complicated answer, but let me try 389 00:19:36,000 --> 00:19:38,879 Speaker 1: to do this in a simple way. The answer is no, 390 00:19:40,440 --> 00:19:42,760 Speaker 1: we always want to feel good about ourselves and we 391 00:19:42,840 --> 00:19:45,240 Speaker 1: don't actually want to rip that one away from us. Right. 392 00:19:45,280 --> 00:19:48,200 Speaker 1: It's good to feel good about yourself. The question is 393 00:19:48,240 --> 00:19:50,920 Speaker 1: what is the thing that you're deriving the good feeling from, 394 00:19:50,960 --> 00:19:55,000 Speaker 1: and that's what you can change. So I can go 395 00:19:55,080 --> 00:19:57,560 Speaker 1: around sort of the way that I'm born say I 396 00:19:57,640 --> 00:20:00,520 Speaker 1: derived my good feeling from thinking that things are to 397 00:20:00,600 --> 00:20:03,719 Speaker 1: my credit, bad things aren't my fault, my beliefs are 398 00:20:03,760 --> 00:20:06,919 Speaker 1: generally true, and I'm super smart, So that could be 399 00:20:06,960 --> 00:20:09,280 Speaker 1: the way that I derived my good feeling. Or I 400 00:20:09,280 --> 00:20:12,800 Speaker 1: could shift the rules of the game where for me, 401 00:20:13,119 --> 00:20:17,840 Speaker 1: you know, the win is calibrating my beliefs, admit being 402 00:20:17,840 --> 00:20:22,000 Speaker 1: a really good credit giver, being a really good mistake admitter. Um, 403 00:20:22,040 --> 00:20:23,919 Speaker 1: And if I can make it so that that's what 404 00:20:24,080 --> 00:20:26,560 Speaker 1: feels good to me, that that's where I'm deriving, feeling 405 00:20:26,560 --> 00:20:29,000 Speaker 1: like I'm a good actor in the world and somehow 406 00:20:29,320 --> 00:20:32,840 Speaker 1: I'm doing a better job than other people, then that's 407 00:20:32,880 --> 00:20:34,159 Speaker 1: going to get me to where I need to be. 408 00:20:34,280 --> 00:20:36,080 Speaker 1: And the way that you do that is, remember I said, 409 00:20:36,119 --> 00:20:39,520 Speaker 1: get this, get this decision pod going, and make sure 410 00:20:39,520 --> 00:20:42,399 Speaker 1: that the decision pod is ref reinforcing those kinds of 411 00:20:42,400 --> 00:20:45,080 Speaker 1: behaviors and not reinforcing the stuff that we don't want 412 00:20:45,080 --> 00:20:47,440 Speaker 1: to reinforce. So if you go up and you say, 413 00:20:47,640 --> 00:20:49,679 Speaker 1: I can't believe how unlucky I got in this trade, 414 00:20:49,720 --> 00:20:51,880 Speaker 1: Like this is so ridiculous, Like I was totally right, 415 00:20:52,000 --> 00:20:56,840 Speaker 1: but like I was so unlucky. Hopefully they're saying, why 416 00:20:56,880 --> 00:20:59,239 Speaker 1: are you telling me this? I don't want to hear that, 417 00:20:59,280 --> 00:21:00,879 Speaker 1: Like that's just a hard luck story. And if it 418 00:21:00,960 --> 00:21:03,320 Speaker 1: was really bad luck, there's nothing to be learned from it. 419 00:21:03,359 --> 00:21:06,040 Speaker 1: So you're just complaining, Like, please, don't complain to me. 420 00:21:06,600 --> 00:21:07,879 Speaker 1: But if you go up and you say, you know what, 421 00:21:07,960 --> 00:21:10,520 Speaker 1: I had this position on and I really felt like 422 00:21:10,520 --> 00:21:15,280 Speaker 1: it was right, and you know, it went poorly. And 423 00:21:15,320 --> 00:21:18,119 Speaker 1: now I'm thinking about it and I'm not sure, you know, 424 00:21:18,160 --> 00:21:19,960 Speaker 1: did it go poorly just because of bad luck or 425 00:21:20,000 --> 00:21:22,080 Speaker 1: maybe I'm looking at these places where I think maybe 426 00:21:22,160 --> 00:21:24,840 Speaker 1: I'm the analysis wasn't quite right. Can I discuss that 427 00:21:24,920 --> 00:21:27,760 Speaker 1: with you? Now? You you engage with me and you 428 00:21:27,800 --> 00:21:31,120 Speaker 1: reinforce that behavior. Look what happens. I mean, we're all 429 00:21:31,160 --> 00:21:33,159 Speaker 1: just rats running through may Is hoping to get a 430 00:21:33,160 --> 00:21:35,960 Speaker 1: little bit of some food at the end. What happens 431 00:21:36,000 --> 00:21:40,160 Speaker 1: is that you're only giving me pellets and reinforcement when 432 00:21:40,480 --> 00:21:44,040 Speaker 1: I'm acting like accuracy is the goal, and you're you're 433 00:21:44,080 --> 00:21:46,760 Speaker 1: pushing me away and not reinforcing when I'm acting like 434 00:21:46,880 --> 00:21:49,800 Speaker 1: being right is the goal. And so that's what I 435 00:21:49,800 --> 00:21:52,240 Speaker 1: would say. Don't try to divorce yourself from ego. I 436 00:21:52,240 --> 00:21:54,680 Speaker 1: don't even know what that means. Like we all have ego, 437 00:21:54,720 --> 00:21:57,119 Speaker 1: we all want to feel good about ourselves. Instead change 438 00:21:57,119 --> 00:21:59,760 Speaker 1: what it is that you feel good about. But okay, 439 00:21:59,520 --> 00:22:03,320 Speaker 1: I have a weird question. But isn't poker ultimately a 440 00:22:03,440 --> 00:22:07,000 Speaker 1: sort of results oriented game the same way with trading, 441 00:22:07,160 --> 00:22:10,720 Speaker 1: Like everyone's in it to win it, So you can 442 00:22:10,760 --> 00:22:15,000 Speaker 1: shift the way you view your own success. But ultimately, 443 00:22:15,080 --> 00:22:18,080 Speaker 1: if it doesn't translate into you winning the game or 444 00:22:18,160 --> 00:22:21,520 Speaker 1: the trade, you're not going to be considered a good 445 00:22:21,560 --> 00:22:25,000 Speaker 1: player or a good trader. So how do you overcome that? Well, 446 00:22:25,040 --> 00:22:27,240 Speaker 1: I guess that the question is are you losing on 447 00:22:27,280 --> 00:22:30,920 Speaker 1: a single trade or is it long term? So that's 448 00:22:30,960 --> 00:22:33,240 Speaker 1: the problem that we have is that we're processing these 449 00:22:33,240 --> 00:22:36,399 Speaker 1: results as they come in, one at a time. And 450 00:22:36,760 --> 00:22:39,119 Speaker 1: that's the problem for learning is that if we have 451 00:22:39,240 --> 00:22:42,440 Speaker 1: a bad result come in and we swatted away to luck, 452 00:22:42,880 --> 00:22:44,959 Speaker 1: we're not going to learn any from it, thing from it. 453 00:22:45,000 --> 00:22:46,880 Speaker 1: If we have a good result come in and we 454 00:22:47,080 --> 00:22:49,280 Speaker 1: want to onboard it and take credit for it, because 455 00:22:49,280 --> 00:22:51,439 Speaker 1: that's the bias. It's called self serving bias. You can 456 00:22:51,480 --> 00:22:55,560 Speaker 1: see why. Um, then we're going to reinforce that behavior. 457 00:22:55,600 --> 00:22:58,240 Speaker 1: But that's only based off of one outcome. So we 458 00:22:58,320 --> 00:23:00,800 Speaker 1: have a choice in life. We can say we're going 459 00:23:00,880 --> 00:23:03,119 Speaker 1: to play for the long run and say if my 460 00:23:03,200 --> 00:23:05,280 Speaker 1: decisions in general are better than in the long run, 461 00:23:05,320 --> 00:23:08,880 Speaker 1: I will win. Or we can say in the short run, 462 00:23:08,920 --> 00:23:11,359 Speaker 1: I don't want to feel bad, So as I'm fielding 463 00:23:11,400 --> 00:23:13,600 Speaker 1: these outcomes as they come in, and certainly they come 464 00:23:13,600 --> 00:23:15,720 Speaker 1: in one at a time. Because we don't stand back 465 00:23:15,760 --> 00:23:18,359 Speaker 1: and say, let me wait and aggregate the data before 466 00:23:18,400 --> 00:23:22,040 Speaker 1: I'm processing this stuff. Then what will happen is that 467 00:23:22,040 --> 00:23:25,480 Speaker 1: we'll start to swat away stuff and onboard stuff, not 468 00:23:25,600 --> 00:23:28,119 Speaker 1: really about whether the decisions were good or bad, but 469 00:23:28,200 --> 00:23:30,199 Speaker 1: just sort of based solely on how the outcome was. 470 00:23:30,520 --> 00:23:31,919 Speaker 1: So we want to we want to play for the 471 00:23:31,960 --> 00:23:34,840 Speaker 1: long run and sort of get over this hump of 472 00:23:34,960 --> 00:23:37,960 Speaker 1: wanting to feel good in the short run. Do you think, uh, 473 00:23:38,000 --> 00:23:41,439 Speaker 1: the sort of golden age of poker TV instilled in 474 00:23:41,520 --> 00:23:44,679 Speaker 1: some people some bad lessons because I'm sure they showed 475 00:23:44,720 --> 00:23:47,480 Speaker 1: all the times when you made an incredible call or 476 00:23:47,480 --> 00:23:50,440 Speaker 1: an incredible laydown, or someone put on sunglasses and pushed 477 00:23:50,480 --> 00:23:53,320 Speaker 1: all in, but they didn't show the thirty five hands 478 00:23:53,320 --> 00:23:56,480 Speaker 1: where you probably just folded and folded and folded and folded, 479 00:23:56,800 --> 00:23:59,040 Speaker 1: even though those might have been the hands that really 480 00:23:59,080 --> 00:24:05,600 Speaker 1: separated you from all the other players. I can't agree 481 00:24:05,640 --> 00:24:08,400 Speaker 1: more with what you just said. So there's actually two 482 00:24:08,440 --> 00:24:12,520 Speaker 1: reasons that I think that it kind of messed things up. 483 00:24:12,640 --> 00:24:15,479 Speaker 1: One is this problem of when you're watching TV and 484 00:24:15,560 --> 00:24:18,920 Speaker 1: it's poker, you can see all the cards, so, um, 485 00:24:19,520 --> 00:24:21,520 Speaker 1: it looks like a very different problem than what it 486 00:24:21,520 --> 00:24:23,920 Speaker 1: actually is, because you're like, I can't believe Joe's such 487 00:24:23,960 --> 00:24:26,960 Speaker 1: an idiot. He folded there. Didn't he know he had 488 00:24:27,000 --> 00:24:29,040 Speaker 1: the best hand. It's like, no, he didn't because he 489 00:24:29,080 --> 00:24:31,199 Speaker 1: couldn't see the other player's cards. So it's hard to 490 00:24:31,240 --> 00:24:33,200 Speaker 1: sort of get into your head now and think about 491 00:24:33,240 --> 00:24:35,320 Speaker 1: what are the kinds of things that you're actually considering. 492 00:24:35,560 --> 00:24:37,520 Speaker 1: But poker is really boring to watch on TV if 493 00:24:37,560 --> 00:24:39,560 Speaker 1: you hide all the cards, so you can't do that. 494 00:24:39,880 --> 00:24:41,680 Speaker 1: But yes, the other thing is that they only show 495 00:24:41,720 --> 00:24:45,399 Speaker 1: the very big decisions. But okay, so there you know, 496 00:24:45,600 --> 00:24:48,760 Speaker 1: every whatever, there's a big decision. It's all those little 497 00:24:48,800 --> 00:24:51,879 Speaker 1: executional decisions that you make along the way. And particularly 498 00:24:51,920 --> 00:24:54,680 Speaker 1: something that you said which is really true is when 499 00:24:54,680 --> 00:24:57,520 Speaker 1: are you cutting your losses? So those might look like 500 00:24:57,640 --> 00:25:00,919 Speaker 1: kind of boring decisions from a TV st endpoint, but 501 00:25:00,960 --> 00:25:04,920 Speaker 1: they're incredibly important decisions as you go along. Right, it's 502 00:25:04,960 --> 00:25:08,919 Speaker 1: considering a counterfactual. Well, I want to get rid of this. 503 00:25:09,080 --> 00:25:10,920 Speaker 1: I don't want to play it because if I do, 504 00:25:11,000 --> 00:25:13,840 Speaker 1: it won't go well. And we're not so great at 505 00:25:13,960 --> 00:25:17,600 Speaker 1: understanding how those tiny little decisions where we just cut 506 00:25:17,600 --> 00:25:20,720 Speaker 1: our losses at the right point really make a difference, 507 00:25:20,800 --> 00:25:22,720 Speaker 1: or when we take a little stab at the right point, 508 00:25:22,800 --> 00:25:25,320 Speaker 1: and those are all sort of not shown. Speaking of 509 00:25:25,480 --> 00:25:28,040 Speaker 1: personal experience, I'm not a very good poker player. All 510 00:25:28,080 --> 00:25:30,399 Speaker 1: my worst hands are when I think I'm like, oh, 511 00:25:30,440 --> 00:25:32,200 Speaker 1: I'm just gonna inch in, but I'm not going to 512 00:25:32,240 --> 00:25:34,800 Speaker 1: get attached to this hand. I'm just gonna play like 513 00:25:34,880 --> 00:25:37,800 Speaker 1: the five seven suited because I'm late in the ring, 514 00:25:38,200 --> 00:25:39,879 Speaker 1: and I'll be okay, And the next thing I know, 515 00:25:39,920 --> 00:25:41,560 Speaker 1: I have like half my pott in because I got 516 00:25:41,600 --> 00:25:43,800 Speaker 1: really attached to it. So I always try, you know, 517 00:25:43,880 --> 00:25:46,840 Speaker 1: avoiding those things seems to be a big would help 518 00:25:46,880 --> 00:25:50,520 Speaker 1: me a lot. Yeah, it's really interesting because I cannot 519 00:25:50,520 --> 00:25:52,800 Speaker 1: tell you the number of times that I've been working 520 00:25:52,800 --> 00:25:57,640 Speaker 1: with someone on poker and they will ask me some 521 00:25:57,720 --> 00:26:02,080 Speaker 1: decision that's occurred, say on the fourth card, and they'll say, oh, 522 00:26:02,119 --> 00:26:04,040 Speaker 1: my gosh, I had this really big decision. I didn't 523 00:26:04,040 --> 00:26:06,720 Speaker 1: know what to do, And I'll say to them, hold 524 00:26:06,720 --> 00:26:10,240 Speaker 1: on a second, can we go back like four cards? 525 00:26:10,440 --> 00:26:13,560 Speaker 1: Because I don't understand how you were in this situation 526 00:26:13,600 --> 00:26:15,520 Speaker 1: in the first place. And I think in general, this 527 00:26:15,600 --> 00:26:18,159 Speaker 1: is kind of a problem with our decision making is 528 00:26:18,520 --> 00:26:21,680 Speaker 1: these big moments really kind of stand out for us, 529 00:26:21,960 --> 00:26:24,120 Speaker 1: and so those are the ones we analyze. But very 530 00:26:24,160 --> 00:26:26,480 Speaker 1: often it's these smaller decisions that you made in the 531 00:26:26,480 --> 00:26:28,879 Speaker 1: first place that actually caused you to get into this 532 00:26:28,960 --> 00:26:31,879 Speaker 1: bad situation in the first place. But we ignore those tiny, 533 00:26:31,920 --> 00:26:35,080 Speaker 1: little executional decisions. That's exactly what you were saying, right, 534 00:26:35,119 --> 00:26:37,520 Speaker 1: Like they're not showing on TV, like, oh, look, she 535 00:26:37,640 --> 00:26:40,920 Speaker 1: folded that seven five mm. That's a hand that I 536 00:26:41,000 --> 00:26:43,119 Speaker 1: might have played and take a chance on. Maybe I 537 00:26:43,119 --> 00:26:45,720 Speaker 1: should be thinking about why she's folding that. And that's 538 00:26:45,800 --> 00:26:48,240 Speaker 1: so often the case, like we make some little decision 539 00:26:48,280 --> 00:26:50,399 Speaker 1: that we don't even think it's significant because it seems 540 00:26:50,400 --> 00:26:53,160 Speaker 1: like it's a tiny little bit of risk, so whatever, 541 00:26:53,720 --> 00:26:56,160 Speaker 1: and then later on it turns into this complete disaster 542 00:26:56,600 --> 00:26:59,760 Speaker 1: as you put yourself into a really tough spot. So 543 00:27:00,480 --> 00:27:04,439 Speaker 1: do you ever adapt your decision making process according to 544 00:27:05,160 --> 00:27:07,119 Speaker 1: I guess the mode of the game that you're playing, Like, 545 00:27:07,200 --> 00:27:10,840 Speaker 1: if you're playing online poker, does your decision making change 546 00:27:10,840 --> 00:27:14,919 Speaker 1: at all? Well, so, how I make a decision doesn't change, 547 00:27:14,920 --> 00:27:17,159 Speaker 1: but what the decisions might be would change because I 548 00:27:17,280 --> 00:27:20,000 Speaker 1: change according to how so I really think about poker 549 00:27:20,040 --> 00:27:22,480 Speaker 1: as a market, right, so I'm reacting to the market. 550 00:27:22,560 --> 00:27:25,359 Speaker 1: So the analogy would I be is you you have 551 00:27:25,400 --> 00:27:28,439 Speaker 1: bear markets, you have bowl markets. Now your decision process 552 00:27:28,440 --> 00:27:30,879 Speaker 1: hopefully is the same, but the result in terms of 553 00:27:30,880 --> 00:27:33,600 Speaker 1: the way that you're reacting to that market should change. 554 00:27:34,040 --> 00:27:36,399 Speaker 1: And in poker you have bear markets and bowl markets 555 00:27:36,400 --> 00:27:38,399 Speaker 1: as well. You sit at a table where the players 556 00:27:38,400 --> 00:27:41,560 Speaker 1: can be relatively passive for example, uh, and they're not 557 00:27:41,680 --> 00:27:43,399 Speaker 1: really trying to take on a lot of risk or 558 00:27:43,440 --> 00:27:46,520 Speaker 1: do very much that you would play. Your strategy would 559 00:27:46,560 --> 00:27:49,119 Speaker 1: change in that compared to one where everybody's trying to 560 00:27:49,160 --> 00:27:51,560 Speaker 1: push you around. That's more like a bear market. Everyone's 561 00:27:51,560 --> 00:27:53,880 Speaker 1: trying to push you around and they're going after you, 562 00:27:53,960 --> 00:27:56,119 Speaker 1: and they're all, you know, very excited to be playing. 563 00:27:56,520 --> 00:27:59,800 Speaker 1: So you would how your strategy, how your decision making 564 00:28:00,320 --> 00:28:03,439 Speaker 1: express itself in those two situations would be different, but 565 00:28:03,520 --> 00:28:06,040 Speaker 1: the structure of how you're making the decision would stay 566 00:28:06,040 --> 00:28:08,639 Speaker 1: the same. Well, we've had a lot of volatility lately, 567 00:28:08,720 --> 00:28:11,240 Speaker 1: so I think that's a perfect spot to end it. 568 00:28:11,400 --> 00:28:15,000 Speaker 1: Any Duke, legendary poker player and the author of Thinking 569 00:28:15,040 --> 00:28:17,760 Speaker 1: in Bets, making smarter decisions when you don't have all 570 00:28:17,760 --> 00:28:20,000 Speaker 1: the facts. Thank you so much for joining us. Well, 571 00:28:20,040 --> 00:28:21,720 Speaker 1: thank you and Tracy. I hope I get you to 572 00:28:21,760 --> 00:28:26,119 Speaker 1: play poker. Someday. You'll have to come coach me. I 573 00:28:26,200 --> 00:28:28,400 Speaker 1: really think I need it at this point. That would 574 00:28:28,440 --> 00:28:41,000 Speaker 1: be great, That would be good TV. So Tracy, another 575 00:28:41,280 --> 00:28:45,040 Speaker 1: poker episode in the books, But I really thought that 576 00:28:45,080 --> 00:28:47,600 Speaker 1: one was one of our best, and I loved that 577 00:28:47,680 --> 00:28:50,280 Speaker 1: point about my favorite point and all that I think 578 00:28:50,440 --> 00:28:53,200 Speaker 1: was the sort of the disadvantage of being smart, because 579 00:28:53,200 --> 00:28:56,600 Speaker 1: I think as journalists we are exposed every day to 580 00:28:56,680 --> 00:28:59,280 Speaker 1: ourselves into our colleagues who you know. Of course we 581 00:28:59,320 --> 00:29:01,240 Speaker 1: think we're the smart as people in the world, and 582 00:29:01,280 --> 00:29:03,440 Speaker 1: I think all of us would be a pretty terrible 583 00:29:03,520 --> 00:29:06,240 Speaker 1: actual practitioners of what we do. I have said this 584 00:29:06,320 --> 00:29:09,000 Speaker 1: more than once, um that if I had been trading, 585 00:29:09,040 --> 00:29:11,680 Speaker 1: I would have made some very very stupid calls over 586 00:29:11,720 --> 00:29:14,920 Speaker 1: the years. For instance, I would have sat out the huge, 587 00:29:15,000 --> 00:29:18,880 Speaker 1: huge run up in bitcoin and the short volatility products 588 00:29:18,960 --> 00:29:23,440 Speaker 1: that we are seeing crash in recent days. It's really 589 00:29:23,440 --> 00:29:25,760 Speaker 1: really difficult. As a journalist. You're sort of trained to 590 00:29:25,880 --> 00:29:28,600 Speaker 1: find the smart angle, try to find the thing that 591 00:29:28,640 --> 00:29:31,080 Speaker 1: no one else is seeing. But when it comes to 592 00:29:31,120 --> 00:29:33,360 Speaker 1: the market sometimes if no one else is seeing it, 593 00:29:33,480 --> 00:29:36,400 Speaker 1: there's probably a reason for that, right, Yeah, And our 594 00:29:36,520 --> 00:29:38,800 Speaker 1: job is we just want to be smart every day 595 00:29:38,840 --> 00:29:42,880 Speaker 1: and we want to say the most interesting things. And 596 00:29:43,120 --> 00:29:45,239 Speaker 1: I guess that works in our career, but if we 597 00:29:45,320 --> 00:29:49,400 Speaker 1: had to sort of systematically survive over time, it probably 598 00:29:49,400 --> 00:29:52,640 Speaker 1: wouldn't be as good. It reminds me. Also, you know, 599 00:29:52,680 --> 00:29:55,600 Speaker 1: we talked to Peter Borish a while ago, long time, 600 00:29:55,880 --> 00:29:58,080 Speaker 1: you know, veteran hedge fund trader, and his point that 601 00:29:58,120 --> 00:30:00,400 Speaker 1: he's made several times, it's like all the great raiders 602 00:30:00,400 --> 00:30:03,520 Speaker 1: are really great at selling, like cutting their losses fast. 603 00:30:03,760 --> 00:30:06,040 Speaker 1: And it fits with this idea that, you know, a 604 00:30:06,040 --> 00:30:08,680 Speaker 1: lot of the real money is made, so to speak, 605 00:30:08,960 --> 00:30:13,320 Speaker 1: in folding, in preemptively avoiding bad decisions. Yeah. But this 606 00:30:13,400 --> 00:30:15,440 Speaker 1: is also the point that Annie was making about the 607 00:30:15,480 --> 00:30:19,360 Speaker 1: importance of getting comfortable with luck. And I think in 608 00:30:19,400 --> 00:30:22,959 Speaker 1: the world of finance and markets and investing especially, people 609 00:30:23,200 --> 00:30:27,080 Speaker 1: aren't comfortable enough with that. Everyone wants to say, oh, 610 00:30:27,240 --> 00:30:30,080 Speaker 1: I was really smart. I called this first, I got 611 00:30:30,120 --> 00:30:33,120 Speaker 1: it right, But actually maybe you were just lucky. And 612 00:30:33,160 --> 00:30:35,200 Speaker 1: in that same way, it can go against you, right, 613 00:30:35,240 --> 00:30:37,040 Speaker 1: And if it does go against you, then you have 614 00:30:37,120 --> 00:30:39,560 Speaker 1: to sort of have the self confidence, in the self 615 00:30:39,560 --> 00:30:42,760 Speaker 1: awareness to just cut your losses and walk away. Let's 616 00:30:42,760 --> 00:30:46,760 Speaker 1: walk away. On that note, let's walk away. This has 617 00:30:46,800 --> 00:30:50,200 Speaker 1: been another episode of the Odd Lots podcast. I'm Joe 618 00:30:50,240 --> 00:30:53,239 Speaker 1: Wisenthal and I'm Tracy Alloway. You can follow me on 619 00:30:53,280 --> 00:30:56,120 Speaker 1: Twitter at Tracy Alloway. Oh, and you can follow me 620 00:30:56,240 --> 00:30:59,160 Speaker 1: on Twitter at the Stalwarts. And you can follow Annie 621 00:30:59,160 --> 00:31:02,640 Speaker 1: on Twitter at any Duke. And you should follow our 622 00:31:02,720 --> 00:31:06,600 Speaker 1: Odd Lots producer tofor Foreheads at Foreheads T, as well 623 00:31:06,640 --> 00:31:11,560 Speaker 1: as the Bloomberg head of podcast, Francesco Leaving at Francesco Today. 624 00:31:11,560 --> 00:31:12,360 Speaker 1: Thanks for listening.