1
00:00:02,400 --> 00:00:11,119
Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Master's in

2
00:00:11,200 --> 00:00:16,000
Speaker 1: Business with Barry Ridholds on Bloomberg Radio.

3
00:00:16,320 --> 00:00:20,479
Speaker 2: This week on the podcast finally I get Colin Camera

4
00:00:20,680 --> 00:00:26,040
Speaker 2: in the studio to talk about neuroeconomics, behavioral finance, and

5
00:00:26,640 --> 00:00:29,840
Speaker 2: really all the fascinating things he's been doing at Caltech

6
00:00:30,240 --> 00:00:34,480
Speaker 2: for the past. Gee's been there for almost thirty years.

7
00:00:34,560 --> 00:00:37,199
Speaker 2: Is that about right? He's really an interesting guy, not

8
00:00:37,400 --> 00:00:42,240
Speaker 2: just because he has the mathematical and behavioral finance background,

9
00:00:42,640 --> 00:00:47,120
Speaker 2: but because he essentially asked the question, what's going on

10
00:00:47,200 --> 00:00:51,040
Speaker 2: inside our brains when we make decisions? What's happening before

11
00:00:51,040 --> 00:00:55,320
Speaker 2: we even have a degree of awareness of our own decisions.

12
00:00:56,440 --> 00:01:00,600
Speaker 2: I just find what he does fascinating, not just fmyes,

13
00:01:00,640 --> 00:01:05,920
Speaker 2: but eye tracking and eg and galvanomic responses of the skin,

14
00:01:06,000 --> 00:01:08,160
Speaker 2: and just on and on, all these different ways to

15
00:01:08,240 --> 00:01:11,280
Speaker 2: measure what's going on with your hormones, what's going on

16
00:01:11,319 --> 00:01:18,600
Speaker 2: pharmacologically within your body. It's both fascinating and terrifying because

17
00:01:19,200 --> 00:01:23,240
Speaker 2: you come to realize what you think is a decision

18
00:01:23,319 --> 00:01:27,120
Speaker 2: you're making, very often is a decision your brain is

19
00:01:27,160 --> 00:01:30,640
Speaker 2: making with or without you. I found our conversation to

20
00:01:30,640 --> 00:01:34,280
Speaker 2: be absolutely fascinating, and I think you will also with

21
00:01:34,480 --> 00:01:40,120
Speaker 2: no further ado my sit down with Caltech Colin camera.

22
00:01:40,400 --> 00:01:41,800
Speaker 1: Thanks for having me so.

23
00:01:41,880 --> 00:01:44,600
Speaker 2: I've been looking forward to having this conversation with you

24
00:01:45,120 --> 00:01:48,960
Speaker 2: for a long time, not just because of my interest

25
00:01:49,040 --> 00:01:53,760
Speaker 2: in behavioral finance, but because of the space you occupy

26
00:01:54,040 --> 00:01:57,760
Speaker 2: in neuroeconomics. We'll talk a little bit about that in

27
00:01:57,840 --> 00:02:01,360
Speaker 2: a bit, but let's start with your back, which is

28
00:02:01,480 --> 00:02:04,600
Speaker 2: kind of astonishing. You get a bachelor's in quantitative studies

29
00:02:05,000 --> 00:02:09,360
Speaker 2: from john Hopkins at seventeen, and then an MBA in

30
00:02:09,480 --> 00:02:13,399
Speaker 2: finance and a PhD in decision theory from the University

31
00:02:13,440 --> 00:02:16,920
Speaker 2: of Chicago at twenty one. That's a lot of school

32
00:02:17,000 --> 00:02:20,799
Speaker 2: really quickly. What were the career plans? Were you thinking academia?

33
00:02:20,880 --> 00:02:21,959
Speaker 2: Were you thinking finance?

34
00:02:22,600 --> 00:02:25,320
Speaker 1: I was actually kind of not quite sure, So I

35
00:02:25,360 --> 00:02:28,520
Speaker 1: got in. I went to Chicago Grad School for PhD

36
00:02:29,720 --> 00:02:33,480
Speaker 1: in the now Booth School of Business. Because I had

37
00:02:33,680 --> 00:02:36,519
Speaker 1: learned a little bit about finance. I took an independent

38
00:02:36,560 --> 00:02:40,360
Speaker 1: study from Carl Christ who's a famous econometrician at Johns

39
00:02:40,360 --> 00:02:46,080
Speaker 1: Hopkins when Gene Fama's book Foundations of Finance had just

40
00:02:46,160 --> 00:02:48,560
Speaker 1: come out. In fact, I literally worked in the college

41
00:02:48,560 --> 00:02:51,679
Speaker 1: bookstore part time, and I remember unpacking the box to

42
00:02:51,800 --> 00:02:54,320
Speaker 1: have this Fama book, and so I immediately bought one,

43
00:02:54,440 --> 00:02:56,160
Speaker 1: and you know, I was going to do this independent

44
00:02:56,200 --> 00:02:58,400
Speaker 1: study and read through. And by the way, it really

45
00:02:58,480 --> 00:03:01,760
Speaker 1: is some books often called Foundations of Blank. It really

46
00:03:01,800 --> 00:03:04,800
Speaker 1: was Foundations, right, you know, it was the It was

47
00:03:04,800 --> 00:03:07,560
Speaker 1: a summary in the nineteen seventy six, right, very early days.

48
00:03:09,600 --> 00:03:11,920
Speaker 1: And so Carl christ had said, well, you should think

49
00:03:11,919 --> 00:03:16,520
Speaker 1: about Chicago. That's a powerhouse place for finance. And so

50
00:03:16,600 --> 00:03:19,640
Speaker 1: I started studying finance there and passed the prelam which

51
00:03:19,680 --> 00:03:23,280
Speaker 1: is no which is no small feat that's very selective.

52
00:03:23,960 --> 00:03:29,800
Speaker 1: And then I got interested in behavioral science because finance

53
00:03:29,919 --> 00:03:33,520
Speaker 1: was really obsessed with market efficiency and you know, there

54
00:03:33,560 --> 00:03:37,040
Speaker 1: was no behavioral science, behavioral finance in sight at that time.

55
00:03:37,040 --> 00:03:38,840
Speaker 1: But there were other folks at at Chicago.

56
00:03:39,120 --> 00:03:42,840
Speaker 2: Well, if I recall correctly, Dick Taylor was there early

57
00:03:42,920 --> 00:03:47,680
Speaker 2: in the behavioral finance or did he end up there later, Yeah,

58
00:03:47,720 --> 00:03:48,360
Speaker 2: he came later.

59
00:03:48,480 --> 00:03:51,200
Speaker 1: He came later. So when I came in the late seventies,

60
00:03:53,200 --> 00:03:56,960
Speaker 1: a lot of Nobel Prize winners were their Fama, Miller Shoals.

61
00:03:57,520 --> 00:04:00,320
Speaker 1: I think Fisher Black might have just left for MIT

62
00:04:00,720 --> 00:04:05,280
Speaker 1: and when I came, but it was pre Andre Schleifer

63
00:04:05,360 --> 00:04:08,720
Speaker 1: and Ravishni who did a lot of interesting behavioral finance,

64
00:04:08,720 --> 00:04:11,840
Speaker 1: and then Dick Taylor came, I think around nineteen ninety five,

65
00:04:11,920 --> 00:04:12,440
Speaker 1: nineteen to.

66
00:04:12,320 --> 00:04:15,480
Speaker 2: Six, and you were at cal Tech by then, right,

67
00:04:15,640 --> 00:04:16,200
Speaker 2: just correct?

68
00:04:16,200 --> 00:04:18,320
Speaker 1: So yeah, so Dick and I had just passed like

69
00:04:18,360 --> 00:04:21,839
Speaker 1: ships in the night, and I regard that sometimes not

70
00:04:21,920 --> 00:04:23,960
Speaker 1: having just stayed and you know it's been part of

71
00:04:23,960 --> 00:04:25,039
Speaker 1: a new vanguard.

72
00:04:26,320 --> 00:04:29,160
Speaker 2: Well, but you actually are part of a new vanguard

73
00:04:29,160 --> 00:04:31,760
Speaker 2: because the work you do in neuroeconomics, which we're going

74
00:04:31,839 --> 00:04:35,920
Speaker 2: to get into, especially fMRIs and all the other things

75
00:04:35,920 --> 00:04:40,600
Speaker 2: you've done more or less created that space. I mean,

76
00:04:40,600 --> 00:04:45,000
Speaker 2: that's pretty foundational. Behavioral finance has a number of fathers,

77
00:04:45,040 --> 00:04:50,920
Speaker 2: including Dick Taylor and Danny Kahneman. So well, let's circle

78
00:04:51,040 --> 00:04:53,960
Speaker 2: back to the neuroeconomics in a little bit. But I

79
00:04:54,000 --> 00:04:58,160
Speaker 2: want to ask what led you into decision making research?

80
00:04:58,200 --> 00:05:01,440
Speaker 2: How did you find yourself taking the background you had

81
00:05:02,600 --> 00:05:08,479
Speaker 2: in quantitative studies and your PhD and m b a

82
00:05:08,720 --> 00:05:10,200
Speaker 2: and and go into decision making.

83
00:05:11,200 --> 00:05:13,880
Speaker 1: So I some of it was when I was in

84
00:05:13,920 --> 00:05:16,719
Speaker 1: college at Johns Hopkins. I studied physics and math that

85
00:05:16,800 --> 00:05:20,320
Speaker 1: was too abstract, and number theory was just too mind blowing,

86
00:05:20,640 --> 00:05:22,200
Speaker 1: you know for me, like I'm just not going to

87
00:05:22,560 --> 00:05:25,760
Speaker 1: work at that level. And then I studied psychology and

88
00:05:25,800 --> 00:05:28,440
Speaker 1: that seemed like just kind of a list of things

89
00:05:28,480 --> 00:05:31,880
Speaker 1: that happened to people, but there was no unifying wish squishy.

90
00:05:32,520 --> 00:05:35,840
Speaker 1: And then economics, which I really only took a little

91
00:05:35,839 --> 00:05:38,120
Speaker 1: bit of a lot fewer than my peers I later

92
00:05:38,160 --> 00:05:41,040
Speaker 1: competed with in grad school, was kind of in between,

93
00:05:41,080 --> 00:05:42,800
Speaker 1: like the Three Little Bears, you know, there was I

94
00:05:42,839 --> 00:05:45,479
Speaker 1: love that, and there was people right you know, physics

95
00:05:45,480 --> 00:05:47,479
Speaker 1: didn't have people, psychology didn't have math.

96
00:05:47,600 --> 00:05:49,279
Speaker 2: Economics was kind of the right mix.

97
00:05:49,160 --> 00:05:51,920
Speaker 1: Exactly exactly. And I think a lot of a lot

98
00:05:51,920 --> 00:05:54,520
Speaker 1: of social scientists may feel that way, and the people

99
00:05:54,520 --> 00:05:57,000
Speaker 1: who like math lest stay in psychology or go to

100
00:05:57,040 --> 00:06:00,520
Speaker 1: sociology or something where the mathematical structure isn't You found

101
00:06:00,520 --> 00:06:02,000
Speaker 1: the canon and the foundation.

102
00:06:03,440 --> 00:06:06,159
Speaker 2: So what led you into game theory? You end up

103
00:06:06,200 --> 00:06:08,880
Speaker 2: writing a book Behavioral game Theory that was published in

104
00:06:09,160 --> 00:06:13,320
Speaker 2: three How does that relate to economics and decision making

105
00:06:13,600 --> 00:06:14,520
Speaker 2: and investing?

106
00:06:15,440 --> 00:06:18,960
Speaker 1: So in graduate school when I pivoted away from finance,

107
00:06:19,560 --> 00:06:24,599
Speaker 1: there was a couple of psychologist Hilly Einhern and Robin Hogarth,

108
00:06:24,600 --> 00:06:27,320
Speaker 1: who were interested in judgment decision making. They were doing

109
00:06:27,400 --> 00:06:30,120
Speaker 1: things very similar to konomen and diversity. It was sort

110
00:06:30,120 --> 00:06:34,200
Speaker 1: of somewhat mathematical attempts to understand actual human decision making,

111
00:06:34,240 --> 00:06:38,200
Speaker 1: not really stylized like Bay's rule and optimization. You know,

112
00:06:38,200 --> 00:06:40,279
Speaker 1: those are good things to know, but they were interested

113
00:06:40,279 --> 00:06:43,520
Speaker 1: in deviations from those and what that might tell us

114
00:06:43,520 --> 00:06:45,640
Speaker 1: and what the practical value. So that's what I ended

115
00:06:45,680 --> 00:06:48,200
Speaker 1: up doing in grad school. Game theory came a little

116
00:06:48,200 --> 00:06:52,920
Speaker 1: bit later because at Chicago at that time, in the

117
00:06:53,040 --> 00:06:55,719
Speaker 1: late seventies, there was hardly any interest in game theory

118
00:06:55,760 --> 00:06:59,520
Speaker 1: for peculiar reasons. They were, you know, the economic world

119
00:06:59,600 --> 00:07:02,799
Speaker 1: was dominated by price theory supplying demand like Gary Becker,

120
00:07:02,839 --> 00:07:04,640
Speaker 1: you know, there was a lot going on. Game theory

121
00:07:04,720 --> 00:07:07,840
Speaker 1: just was not flourishing there. But my first job was

122
00:07:07,839 --> 00:07:11,440
Speaker 1: as an assistant professor in Northwestern and that happened to be,

123
00:07:12,280 --> 00:07:16,600
Speaker 1: through just historical coincidence, a hotbed of great game theory.

124
00:07:16,640 --> 00:07:19,960
Speaker 1: Paul Milgram was there, Banked Holmestrom was there, Robert Weber,

125
00:07:20,000 --> 00:07:25,000
Speaker 1: who worked on lots of things on auction theory, Dave Barren,

126
00:07:25,000 --> 00:07:28,120
Speaker 1: who was interested in political economy, and you know, political

127
00:07:28,160 --> 00:07:32,680
Speaker 1: systems as games. So Milgrim and Holstrom went on to

128
00:07:32,720 --> 00:07:35,760
Speaker 1: win Nobel prizes and went to other places. So it

129
00:07:35,800 --> 00:07:39,440
Speaker 1: was sort of this incubator place that then, you know,

130
00:07:40,080 --> 00:07:44,280
Speaker 1: like a incubator like Hewlett Packard and things like that,

131
00:07:44,320 --> 00:07:46,800
Speaker 1: where people then went off to do other stuff. And

132
00:07:46,880 --> 00:07:49,400
Speaker 1: so I basically learned game theory in my first job

133
00:07:49,440 --> 00:07:53,720
Speaker 1: as AISTM professor, and that game theory is similar to

134
00:07:54,120 --> 00:07:57,800
Speaker 1: behavor economics. The standard theory that everyone teaches in every

135
00:07:57,800 --> 00:08:03,200
Speaker 1: introductory course is people arend and make the best choices

136
00:08:03,240 --> 00:08:05,920
Speaker 1: given what they think others will do, and they're correct

137
00:08:06,040 --> 00:08:08,600
Speaker 1: guessing about what others do. Like a bunch of people

138
00:08:08,600 --> 00:08:10,760
Speaker 1: who played poker with each other, you know, every Friday

139
00:08:10,840 --> 00:08:13,640
Speaker 1: night for decades, they kind of know what the tells are.

140
00:08:14,520 --> 00:08:17,280
Speaker 1: But we were interested in what happens before you get

141
00:08:17,280 --> 00:08:19,760
Speaker 1: to this kind of what's called Nash equilibrium, you know,

142
00:08:19,760 --> 00:08:21,880
Speaker 1: where everyone is guessed correctly what everyone's going to do.

143
00:08:23,080 --> 00:08:25,000
Speaker 1: And so to me, there was a huge room for

144
00:08:26,200 --> 00:08:29,760
Speaker 1: understanding the psychology of strategic thinking in game theory.

145
00:08:29,880 --> 00:08:33,880
Speaker 2: So that's really interesting to me. I always found the

146
00:08:33,920 --> 00:08:40,400
Speaker 2: traditional economic homo economists of humans as rational calculating profit

147
00:08:40,520 --> 00:08:46,480
Speaker 2: maximizing actors is just complete contradiction of real life experience.

148
00:08:47,240 --> 00:08:50,559
Speaker 2: How did you go from your initial interest in behavioral

149
00:08:50,600 --> 00:08:57,400
Speaker 2: finance into neuroeconomics, where you're looking at the biological underpinnings

150
00:08:57,559 --> 00:08:59,960
Speaker 2: of what happens as people make decisions.

151
00:09:00,360 --> 00:09:02,439
Speaker 1: Yeah, So the neuroeconomics to me was sort of a

152
00:09:02,520 --> 00:09:06,000
Speaker 1: natural extension of behavior economics, which was We're going to

153
00:09:06,360 --> 00:09:09,200
Speaker 1: grab from any interesting data and different ways of thinking

154
00:09:09,200 --> 00:09:12,600
Speaker 1: about humans outside of standard economics and kind of pull

155
00:09:12,600 --> 00:09:14,640
Speaker 1: it in and try to, you know, generate a kind

156
00:09:14,640 --> 00:09:16,959
Speaker 1: of hybrid. It was almost like an import export business.

157
00:09:16,960 --> 00:09:19,520
Speaker 1: And I'm going to import some psychology or dicktale or

158
00:09:19,559 --> 00:09:22,240
Speaker 1: imported from konomon and what is this going to tell

159
00:09:22,280 --> 00:09:25,320
Speaker 1: us about fairness and reference points and loss aversion what

160
00:09:25,440 --> 00:09:27,680
Speaker 1: have you? And neur economics seem to me like just

161
00:09:27,720 --> 00:09:30,720
Speaker 1: another thing to do. Part of it is my personality

162
00:09:30,760 --> 00:09:33,840
Speaker 1: is kind of like intellectual entrepreneurship. So I liked you know,

163
00:09:33,880 --> 00:09:36,160
Speaker 1: doing different things. You know, over the years, I've worked

164
00:09:36,200 --> 00:09:38,800
Speaker 1: on lots of different methods and with different groups of people,

165
00:09:38,840 --> 00:09:41,720
Speaker 1: and neureconomics was just a chance to do something even

166
00:09:41,760 --> 00:09:43,559
Speaker 1: more dramatic.

167
00:09:43,440 --> 00:09:48,160
Speaker 2: And tell us about your patent on active learning decision engines.

168
00:09:48,600 --> 00:09:49,960
Speaker 2: What on earth is that?

169
00:09:50,200 --> 00:09:53,280
Speaker 1: So active learning is the commuter scientist term is sometimes

170
00:09:53,280 --> 00:09:57,120
Speaker 1: called dynamic adaptive learning. For basically, like if I was

171
00:09:57,160 --> 00:10:00,440
Speaker 1: going to try to figure out how much you like risk,

172
00:10:00,600 --> 00:10:03,160
Speaker 1: like you were a client, and if a financial advisor

173
00:10:03,240 --> 00:10:06,080
Speaker 1: is asking you know, I might start by saying, well,

174
00:10:06,440 --> 00:10:09,200
Speaker 1: here's a portfolio. Is this too risky or not risky enough?

175
00:10:09,720 --> 00:10:12,040
Speaker 1: And if you say, nah, that's not risky enough, you know,

176
00:10:12,080 --> 00:10:14,960
Speaker 1: I'd rather go for more, and then I would give

177
00:10:15,000 --> 00:10:16,680
Speaker 1: you a better one that's a little has a little

178
00:10:16,720 --> 00:10:19,960
Speaker 1: more risk in it. And in chemistry it's called titration.

179
00:10:20,320 --> 00:10:22,760
Speaker 1: You know, you kind of change the mixture of the chemicals.

180
00:10:23,280 --> 00:10:25,840
Speaker 1: And so for each person, you're asking them a dynamic,

181
00:10:26,760 --> 00:10:29,360
Speaker 1: customized set of questions to get to the best answer

182
00:10:29,400 --> 00:10:33,200
Speaker 1: as quickly as possible, and that's called active learning. So

183
00:10:33,360 --> 00:10:36,560
Speaker 1: one of my colleagues at Caltech at that time, Andreas Krauss,

184
00:10:37,160 --> 00:10:39,920
Speaker 1: was studying he was a Gonna Better scientist. So they're

185
00:10:39,960 --> 00:10:43,160
Speaker 1: always on the frontier of how to get the truth

186
00:10:43,480 --> 00:10:47,600
Speaker 1: faster and subject to computational constraints, like you know, because

187
00:10:47,600 --> 00:10:50,360
Speaker 1: sometimes it's not just a question. I'm getting there, but

188
00:10:50,480 --> 00:10:52,200
Speaker 1: can you do it in real time so you don't

189
00:10:52,240 --> 00:10:55,040
Speaker 1: have to wait half an hour, you know, to ask

190
00:10:55,080 --> 00:10:59,320
Speaker 1: the next highly unformative question. And so the patent was

191
00:10:59,360 --> 00:11:02,800
Speaker 1: just a a method that Andreas and another guy who

192
00:11:02,920 --> 00:11:05,480
Speaker 1: now works like Google, I believe Daniel Goldman and me

193
00:11:05,800 --> 00:11:09,520
Speaker 1: had worked on to apply this in a particular way.

194
00:11:09,600 --> 00:11:11,480
Speaker 1: And so it was basically a software pattern. There was

195
00:11:11,480 --> 00:11:13,080
Speaker 1: an Advazon pattern on an algorithm.

196
00:11:13,240 --> 00:11:17,440
Speaker 2: So you're asking people questions, how do you know they're

197
00:11:17,480 --> 00:11:21,600
Speaker 2: giving you honest answers? And I asked that question for

198
00:11:21,720 --> 00:11:24,680
Speaker 2: very specific reasons that will be evident in a moment.

199
00:11:25,080 --> 00:11:27,679
Speaker 2: How do you know the answers are legitimate?

200
00:11:27,760 --> 00:11:31,800
Speaker 1: Okay, So in experiment economics, one of the main rules,

201
00:11:31,840 --> 00:11:35,120
Speaker 1: like a commandment, is we almost always pay people unless

202
00:11:35,160 --> 00:11:37,400
Speaker 1: we can't, like with children sometimes or what have you.

203
00:11:38,040 --> 00:11:40,360
Speaker 1: We almost always pay people money or something we know

204
00:11:40,400 --> 00:11:43,440
Speaker 1: they value based on the decisions they made. So when

205
00:11:43,480 --> 00:11:46,640
Speaker 1: we do these kind of risk assessments, again not with clients,

206
00:11:46,640 --> 00:11:48,760
Speaker 1: but say in a simple experiment for modest amounts of

207
00:11:48,760 --> 00:11:51,280
Speaker 1: money twenty bucks, fifty bucks, what we'll do is we

208
00:11:51,360 --> 00:11:52,719
Speaker 1: say at the end, we're going to pick one of

209
00:11:52,760 --> 00:11:54,280
Speaker 1: the things you said you wanted, and we're going to

210
00:11:54,320 --> 00:11:57,840
Speaker 1: actually play that for money. And if you if you

211
00:11:57,840 --> 00:11:59,280
Speaker 1: don't tell us what you really wanted, you're gonna get

212
00:11:59,320 --> 00:11:59,880
Speaker 1: stuck with something.

213
00:12:00,600 --> 00:12:04,280
Speaker 2: So you're creating an incentive for them to be somewhat honest.

214
00:12:04,520 --> 00:12:04,800
Speaker 1: Correct.

215
00:12:05,040 --> 00:12:08,160
Speaker 2: The reason I ask we're recording this about two weeks

216
00:12:08,200 --> 00:12:12,000
Speaker 2: before the twenty twenty four presidential election. I wrote something

217
00:12:12,000 --> 00:12:16,000
Speaker 2: a month ago about why polling errors are really a

218
00:12:16,040 --> 00:12:20,080
Speaker 2: behavioral problem because when you ask people who you're going

219
00:12:20,160 --> 00:12:22,640
Speaker 2: to vote for, what you're really asking is not just

220
00:12:22,720 --> 00:12:25,560
Speaker 2: their preference, but hey, you're gonna get your lazy butt

221
00:12:26,000 --> 00:12:27,920
Speaker 2: off the couch and go to the library and vote.

222
00:12:28,280 --> 00:12:31,520
Speaker 2: And I assumed, hey, there's an era of five, six

223
00:12:31,640 --> 00:12:34,360
Speaker 2: seven percent built into that, and that's why polls are

224
00:12:34,360 --> 00:12:39,679
Speaker 2: so bad. Researching your work about hypothetical bias, I was

225
00:12:39,800 --> 00:12:43,160
Speaker 2: shocked the data that you came is when you ask

226
00:12:43,200 --> 00:12:45,600
Speaker 2: people if they're going to vote, about seventy percent say

227
00:12:45,640 --> 00:12:49,360
Speaker 2: they will, In reality, just forty five percent of them do.

228
00:12:49,840 --> 00:12:54,000
Speaker 2: That's a massive error of twenty five percent. What value

229
00:12:54,040 --> 00:12:56,520
Speaker 2: is there in polls when people have no idea what

230
00:12:56,559 --> 00:12:57,360
Speaker 2: they're really going to do?

231
00:12:57,960 --> 00:13:00,360
Speaker 1: Yeah, So I mean I think the best post are

232
00:13:01,280 --> 00:13:03,480
Speaker 1: know that, and so they try to phrase the question

233
00:13:03,640 --> 00:13:06,560
Speaker 1: or gather some other data. But this is often called

234
00:13:06,559 --> 00:13:09,760
Speaker 1: acquiescence or yes bias. Right, so you say, people, are

235
00:13:09,800 --> 00:13:11,840
Speaker 1: you planning to vote? Oh, yeah, I'm planning to vote, Well,

236
00:13:11,880 --> 00:13:13,320
Speaker 1: you're going to Are you going to not vote because

237
00:13:13,320 --> 00:13:14,840
Speaker 1: it's too Yeah, I may not vote.

238
00:13:14,880 --> 00:13:16,880
Speaker 2: What happens if it rains, what happens if you're busy.

239
00:13:17,520 --> 00:13:19,040
Speaker 1: So you can often get numbers in it up to

240
00:13:19,040 --> 00:13:21,199
Speaker 1: more than one hundred percent. Are you having to vote? No,

241
00:13:21,360 --> 00:13:24,240
Speaker 1: you have seventy percent. Yeah, I probably won't vote fifty

242
00:13:24,240 --> 00:13:26,280
Speaker 1: five percent. That's one hundred and twenty five percent current.

243
00:13:26,440 --> 00:13:29,880
Speaker 1: The math doesn't math, and you see it. Particularly. One

244
00:13:29,880 --> 00:13:32,640
Speaker 1: of the things we study was product purchases. So when

245
00:13:32,640 --> 00:13:34,599
Speaker 1: you show people new products and say, you know, you

246
00:13:34,600 --> 00:13:37,000
Speaker 1: think you'd be interested in this, you get way too

247
00:13:37,040 --> 00:13:40,240
Speaker 1: many yes's. And that's one recent new products fail. It

248
00:13:40,320 --> 00:13:43,559
Speaker 1: is because somebody who's the product champion inside the firm,

249
00:13:43,720 --> 00:13:45,960
Speaker 1: like in a consumer products company, looks at this polling

250
00:13:46,040 --> 00:13:48,440
Speaker 1: date and says, see, see you know, give me money

251
00:13:48,480 --> 00:13:51,960
Speaker 1: to roll this out in a test market. So what

252
00:13:52,040 --> 00:13:53,680
Speaker 1: one of the things we have done is to try

253
00:13:53,720 --> 00:13:56,200
Speaker 1: to see if we didn't, we'd wro'te a few papers

254
00:13:56,240 --> 00:13:58,480
Speaker 1: on this, but I don't feel like we exactly crack

255
00:13:58,559 --> 00:14:01,920
Speaker 1: the nut was to see if a combination of what

256
00:14:02,000 --> 00:14:04,600
Speaker 1: people look at if you measure where their eyes are looking,

257
00:14:05,320 --> 00:14:08,080
Speaker 1: like how often they look back and forth between a

258
00:14:08,120 --> 00:14:12,160
Speaker 1: price and a product, and maybe brain signals can help

259
00:14:12,240 --> 00:14:14,720
Speaker 1: us predict when they say, yeah, I'm going to vote,

260
00:14:14,760 --> 00:14:15,800
Speaker 1: are they really going to vote or not?

261
00:14:16,160 --> 00:14:20,800
Speaker 2: And neuroeconomics, as as I've learned about it through you

262
00:14:21,480 --> 00:14:24,080
Speaker 2: is you're putting people in a functional MRI machine. You're

263
00:14:24,120 --> 00:14:27,360
Speaker 2: asking them a series of questions, and you're identifying what

264
00:14:27,560 --> 00:14:29,800
Speaker 2: parts of the brain are actually lighting up.

265
00:14:29,920 --> 00:14:32,480
Speaker 1: Correct exactly so that so and and by the way,

266
00:14:32,480 --> 00:14:37,480
Speaker 1: ephraimri is glamorous and fantastic, but there's lots of other

267
00:14:37,480 --> 00:14:39,640
Speaker 1: methode that they are used as well. You know, it's

268
00:14:39,720 --> 00:14:42,760
Speaker 1: unnatural because people are in this tube. It's very loud.

269
00:14:43,120 --> 00:14:44,840
Speaker 1: You know, if you want to study a phobic, if

270
00:14:44,840 --> 00:14:46,880
Speaker 1: you want to study close to probi, you cannot, you know,

271
00:14:46,920 --> 00:14:50,240
Speaker 1: because the closer roobics won't go in there. But it

272
00:14:50,280 --> 00:14:52,240
Speaker 1: does give you a picture of the whole brain. And

273
00:14:52,280 --> 00:14:55,560
Speaker 1: in the in the case of the we need some

274
00:14:55,600 --> 00:14:58,320
Speaker 1: experiments where we show people to consumer good and in

275
00:14:58,360 --> 00:15:00,920
Speaker 1: one condition. The first part of the experiment we say,

276
00:15:01,040 --> 00:15:02,880
Speaker 1: you don't have to actually buy this, but just tell us,

277
00:15:02,920 --> 00:15:04,640
Speaker 1: you know if it was on sale for this price,

278
00:15:04,800 --> 00:15:08,560
Speaker 1: like yes, no, strong, yes, weak yess. So we get

279
00:15:08,560 --> 00:15:11,560
Speaker 1: a four point scale and then we surprise them and say,

280
00:15:11,760 --> 00:15:13,640
Speaker 1: now we're going to show you some different products and

281
00:15:13,720 --> 00:15:15,840
Speaker 1: these are going to actually buy. So if you say

282
00:15:15,920 --> 00:15:18,560
Speaker 1: yes and we choose that one out of this.

283
00:15:18,760 --> 00:15:19,720
Speaker 2: Bin, you get it.

284
00:15:19,960 --> 00:15:21,960
Speaker 1: You have you have to buy it. Well, we give

285
00:15:22,000 --> 00:15:24,040
Speaker 1: you some money and we're going to take the price

286
00:15:24,080 --> 00:15:27,080
Speaker 1: out and give you the residual money and the product,

287
00:15:27,120 --> 00:15:29,160
Speaker 1: and you're going to leave here with this product or

288
00:15:29,240 --> 00:15:30,920
Speaker 1: I think some of them we have we mail it

289
00:15:30,960 --> 00:15:33,600
Speaker 1: to them at Amazon is something we actually had products

290
00:15:33,640 --> 00:15:37,680
Speaker 1: there in a box. And so the question is what's

291
00:15:37,720 --> 00:15:40,240
Speaker 1: going on in the brain when they're seriously thinking about

292
00:15:40,280 --> 00:15:44,240
Speaker 1: buying something for real versus hypothetical, which is like a survey, right,

293
00:15:45,400 --> 00:15:47,520
Speaker 1: And what we found was the tricky part is to

294
00:15:48,280 --> 00:15:53,080
Speaker 1: predict when people say yes, hypothetical, but the brain says no,

295
00:15:53,880 --> 00:15:55,360
Speaker 1: you know, can you can you see a brain?

296
00:15:55,600 --> 00:15:56,520
Speaker 2: Can you identify that?

297
00:15:56,920 --> 00:15:58,840
Speaker 1: Uh? Modestly well?

298
00:15:59,160 --> 00:15:59,280
Speaker 2: Right?

299
00:15:59,520 --> 00:16:02,920
Speaker 1: And it turns out the most. There's two interesting markers.

300
00:16:02,960 --> 00:16:05,960
Speaker 1: One is there's a very old area in the brain,

301
00:16:06,040 --> 00:16:10,440
Speaker 1: old you know, evolutionary yes, called the mid brain, which

302
00:16:10,480 --> 00:16:14,280
Speaker 1: is actually where all of the dopamina drid neurons live

303
00:16:14,600 --> 00:16:18,240
Speaker 1: and then and then connect to middle areas of the

304
00:16:18,240 --> 00:16:21,440
Speaker 1: brain called basoganglia that are kind of computing reward and value.

305
00:16:21,480 --> 00:16:24,200
Speaker 1: And then frontal cortex, which is really putting together the

306
00:16:24,240 --> 00:16:27,560
Speaker 1: modern the modern exactly like it's like a thinking cap

307
00:16:27,600 --> 00:16:30,960
Speaker 1: on top of the monkey brain. And in the mid

308
00:16:31,000 --> 00:16:36,600
Speaker 1: brain there's a stronger signal when they say yes and

309
00:16:36,640 --> 00:16:40,720
Speaker 1: they actually do do yes hypothetical and it's a yes reel.

310
00:16:41,080 --> 00:16:43,800
Speaker 1: There's a stronger signal than when they say yes hypothetical

311
00:16:44,080 --> 00:16:47,080
Speaker 1: no real. So it's almost like way upstream in the

312
00:16:47,080 --> 00:16:52,680
Speaker 1: brain if if if in that region they say yes,

313
00:16:52,720 --> 00:16:55,640
Speaker 1: I'm gonna buy it hypothetically, there's enough activity they're gonna

314
00:16:55,640 --> 00:16:55,920
Speaker 1: buy it.

315
00:16:56,400 --> 00:16:59,880
Speaker 2: So my general sense of this, and I'm curious as

316
00:17:00,080 --> 00:17:03,240
Speaker 2: to how you what the reality is, my sense of

317
00:17:03,280 --> 00:17:06,840
Speaker 2: it is, on the one hand, people are social animals

318
00:17:06,840 --> 00:17:09,919
Speaker 2: and they want to be agreeable and say yes to people.

319
00:17:10,680 --> 00:17:13,040
Speaker 2: On the other hand, we really don't know what the

320
00:17:13,080 --> 00:17:16,359
Speaker 2: hell we want, especially if you're talking about something six

321
00:17:16,440 --> 00:17:19,760
Speaker 2: months from now. I guess the tricky part is how

322
00:17:19,760 --> 00:17:22,680
Speaker 2: do you get people in MRI machines when you have

323
00:17:22,720 --> 00:17:24,639
Speaker 2: a question for them. We can't even get people to

324
00:17:24,640 --> 00:17:28,240
Speaker 2: pick up their phone to answer polls. How difficult is

325
00:17:28,280 --> 00:17:31,399
Speaker 2: it to get subjects to go through this process? Or

326
00:17:31,400 --> 00:17:34,840
Speaker 2: are these all mostly undergraduates and you know their lab rats?

327
00:17:34,880 --> 00:17:35,680
Speaker 2: You can do whatever you want.

328
00:17:35,760 --> 00:17:39,600
Speaker 1: Some of them are undergraduates, although in Caltech they're very

329
00:17:39,680 --> 00:17:43,640
Speaker 1: unusual human beings because they're actually useful. They're very useful

330
00:17:44,119 --> 00:17:47,120
Speaker 1: labrats who pay for economics because the media and matthe

331
00:17:47,240 --> 00:17:51,639
Speaker 1: Is et Is eight hundred, they're the most mathematically skilled except.

332
00:17:51,400 --> 00:17:52,879
Speaker 2: For that's a perfect score, isn't it?

333
00:17:52,920 --> 00:17:55,480
Speaker 1: Like exactly, that's the perfect score, like Harvey mud Mit.

334
00:17:55,640 --> 00:17:58,800
Speaker 1: There are other places that have, you know, similarly hyper

335
00:17:58,800 --> 00:18:02,479
Speaker 1: analytical kids. So if like if they can't do something

336
00:18:02,600 --> 00:18:07,359
Speaker 1: like a computation easily, nobody can. So it's very useful

337
00:18:07,520 --> 00:18:10,720
Speaker 1: establishing like bounds on rationality. You know that people. We

338
00:18:10,760 --> 00:18:13,320
Speaker 1: often get critiques like well, you wouldn't get bubbles if

339
00:18:13,359 --> 00:18:16,240
Speaker 1: people were smart enough, Like, well, we have the smartest

340
00:18:16,240 --> 00:18:17,320
Speaker 1: people and you get bubbles.

341
00:18:18,520 --> 00:18:20,879
Speaker 2: It's got less to do with the frontal cortex and

342
00:18:20,920 --> 00:18:24,199
Speaker 2: intelligence and everything with that, the limbic system and the

343
00:18:24,240 --> 00:18:25,000
Speaker 2: lizard brain back.

344
00:18:25,080 --> 00:18:27,120
Speaker 1: Yes, exactly, so they have they have all the things

345
00:18:27,200 --> 00:18:29,000
Speaker 1: in the brain they have, they have other skills that

346
00:18:29,040 --> 00:18:33,479
Speaker 1: are cordically expressed. But so in a lot of these

347
00:18:33,600 --> 00:18:36,440
Speaker 1: MRI studies we also use. We work pretty hard actually

348
00:18:36,440 --> 00:18:39,960
Speaker 1: to get regular folks from the community who and who

349
00:18:40,200 --> 00:18:42,800
Speaker 1: you know are different ages. You know, we we don't

350
00:18:42,800 --> 00:18:45,359
Speaker 1: really have a representative sample, although you could, you could

351
00:18:45,359 --> 00:18:49,200
Speaker 1: try to get pretty close in southern California, and then

352
00:18:49,240 --> 00:18:51,399
Speaker 1: we we we almost always never do a study this

353
00:18:51,680 --> 00:18:54,600
Speaker 1: just take outing undergrads because we worry about the robustness

354
00:18:54,600 --> 00:18:57,080
Speaker 1: across right, It is true in the case of something

355
00:18:57,200 --> 00:18:59,960
Speaker 1: like trying to get brain signals to break when people

356
00:19:00,000 --> 00:19:03,560
Speaker 1: will actually buy products. The other type of study we've

357
00:19:03,640 --> 00:19:05,719
Speaker 1: used to involves eye tracking and things like that, and

358
00:19:05,760 --> 00:19:09,480
Speaker 1: it turns out that when when you ask people hypothetical questions,

359
00:19:09,520 --> 00:19:11,119
Speaker 1: would you buy that? You don't really have to buy this,

360
00:19:11,240 --> 00:19:13,320
Speaker 1: but would you, they just don't look at the price

361
00:19:13,359 --> 00:19:16,960
Speaker 1: that much, and when they're really shopping, they really look

362
00:19:17,000 --> 00:19:19,840
Speaker 1: at the price. So one way to tell whether people

363
00:19:19,880 --> 00:19:23,720
Speaker 1: are being serious in expressing a genuine what I'm going

364
00:19:23,720 --> 00:19:26,320
Speaker 1: to really do, it is just something like how much

365
00:19:26,359 --> 00:19:28,080
Speaker 1: time they spend looking at the price and looking back

366
00:19:28,119 --> 00:19:31,520
Speaker 1: and forth. And there may be other like if if

367
00:19:32,840 --> 00:19:35,280
Speaker 1: if it was consumer products company was trying to use

368
00:19:35,560 --> 00:19:38,600
Speaker 1: FROMRI or other methods. There are others that are much

369
00:19:38,640 --> 00:19:41,520
Speaker 1: more portable, like EEG, and you can get a pair

370
00:19:41,520 --> 00:19:43,919
Speaker 1: of glasses you walk around and it, you know, it

371
00:19:44,000 --> 00:19:46,159
Speaker 1: records where your eyes looking. So there are there are

372
00:19:46,160 --> 00:19:49,119
Speaker 1: things you can do outside of the confines of a

373
00:19:49,119 --> 00:19:52,320
Speaker 1: campus lab. I think we would just look for things

374
00:19:52,359 --> 00:19:56,800
Speaker 1: that are that are easy, easily seen biomarkers of this

375
00:19:56,960 --> 00:19:59,400
Speaker 1: mid brain activity of f MRI, because we're never gonna

376
00:19:59,400 --> 00:20:01,320
Speaker 1: be able to do that, you know, at scale in

377
00:20:01,400 --> 00:20:03,000
Speaker 1: a shopping mall or something.

378
00:20:03,160 --> 00:20:04,919
Speaker 2: So let's go through each of these. We know what

379
00:20:05,119 --> 00:20:10,280
Speaker 2: fMRI is, right, you're in an MRI machine. EEG and SCR.

380
00:20:10,400 --> 00:20:11,359
Speaker 2: Tell us what those do.

381
00:20:11,680 --> 00:20:15,280
Speaker 1: So eg's electro and cephalography and it's basically all the

382
00:20:15,320 --> 00:20:19,760
Speaker 1: little things on your electrodes. If your ball like me,

383
00:20:19,880 --> 00:20:23,359
Speaker 1: that's good for seasons. You know, if you're a supermodel

384
00:20:23,359 --> 00:20:27,400
Speaker 1: with big puffy Texas beauty pageant hair, then no good,

385
00:20:27,520 --> 00:20:27,880
Speaker 1: no good.

386
00:20:28,520 --> 00:20:31,560
Speaker 2: So you're measuring electrical activity in the brain, and you

387
00:20:31,600 --> 00:20:35,240
Speaker 2: could really specify where it is by you know, just

388
00:20:35,640 --> 00:20:39,240
Speaker 2: triangulating with all the different leads that you have spect.

389
00:20:39,200 --> 00:20:42,240
Speaker 1: Exactly so the you know, you can put sixteen to

390
00:20:42,280 --> 00:20:45,280
Speaker 1: one hundred and twenty eight different electrodes. The signals are

391
00:20:45,440 --> 00:20:48,840
Speaker 1: very weak, but the advantage of EG is it's really fast.

392
00:20:49,280 --> 00:20:51,399
Speaker 1: So if you want to study something like thinking fast

393
00:20:51,440 --> 00:20:53,359
Speaker 1: and slow, you know, like if I show you a

394
00:20:53,359 --> 00:20:55,600
Speaker 1: picture of a person, you have a snap reaction that

395
00:20:55,680 --> 00:20:57,800
Speaker 1: they're scary or they're someone you want to vote for,

396
00:20:58,359 --> 00:21:01,359
Speaker 1: then FRI is too slow because it measures these blood

397
00:21:01,359 --> 00:21:03,480
Speaker 1: flow signals that take like one or two seconds to

398
00:21:03,520 --> 00:21:03,920
Speaker 1: show up.

399
00:21:04,240 --> 00:21:08,400
Speaker 2: But like one, one or two seconds is too slow for.

400
00:21:08,920 --> 00:21:11,959
Speaker 1: You know, a lot is going on in the in

401
00:21:12,320 --> 00:21:14,040
Speaker 1: the first two seconds where people are thinking out of

402
00:21:14,040 --> 00:21:18,320
Speaker 1: a decision that's really interesting necessarily you know, which mortgage

403
00:21:18,359 --> 00:21:20,240
Speaker 1: to finance, their refinance their house in.

404
00:21:20,320 --> 00:21:24,560
Speaker 2: Or literally system one thinking fast is exactly.

405
00:21:24,280 --> 00:21:27,480
Speaker 1: So it's the term psychologist. Social psychology use is also

406
00:21:27,480 --> 00:21:30,440
Speaker 1: called thin slicing, which is that and the thin slices

407
00:21:30,520 --> 00:21:34,520
Speaker 1: on the order of meaning a very aggregate, somewhat confident

408
00:21:34,600 --> 00:21:38,440
Speaker 1: judgment is made within you know, ten seconds, thirty seconds,

409
00:21:38,680 --> 00:21:42,520
Speaker 1: there's a big literature, and we're interviewing about this that,

410
00:21:42,960 --> 00:21:45,600
Speaker 1: you know, face to base interviewing. Unless you're really trained

411
00:21:45,640 --> 00:21:49,280
Speaker 1: to have a comparable interview for different people, you know,

412
00:21:49,359 --> 00:21:51,800
Speaker 1: the first couple of minutes of the interview, you're kind

413
00:21:51,800 --> 00:21:54,200
Speaker 1: of making up your mind. At least a lot of

414
00:21:54,200 --> 00:21:55,200
Speaker 1: studies indicate that.

415
00:21:55,280 --> 00:21:58,080
Speaker 2: And SCR is what so SCR.

416
00:21:57,800 --> 00:22:02,199
Speaker 1: Skin conducted response, also called galvanic skin response. And so

417
00:22:02,280 --> 00:22:06,960
Speaker 1: basically it turns out when people are aroused in any

418
00:22:07,080 --> 00:22:08,960
Speaker 1: any direction, it doesn't tell you good or bab, but

419
00:22:09,000 --> 00:22:12,000
Speaker 1: it just tells you arousal. You have this detectable increase

420
00:22:12,119 --> 00:22:14,960
Speaker 1: in sweating you can measure in the fingers.

421
00:22:15,720 --> 00:22:19,440
Speaker 2: So and in all of these things, you're actually taking measurements,

422
00:22:19,560 --> 00:22:23,479
Speaker 2: not asking people things. And one of the quotes that

423
00:22:23,520 --> 00:22:26,960
Speaker 2: caught my attention. Since most of our brain activity goes

424
00:22:27,000 --> 00:22:31,479
Speaker 2: on without our awareness subconsciously, we cannot solely rely on

425
00:22:31,640 --> 00:22:37,440
Speaker 2: individual's accounts when analyzing their behavior. How important is the

426
00:22:37,440 --> 00:22:41,800
Speaker 2: concept of the subconscious to neuroeconomics.

427
00:22:41,480 --> 00:22:44,120
Speaker 1: It's pretty important. So the saying we use is sometimes

428
00:22:44,160 --> 00:22:46,119
Speaker 1: you want to ask the brain rather than ask the person,

429
00:22:46,240 --> 00:22:50,240
Speaker 1: uh huh. And there's some there's some extreme ways in

430
00:22:50,240 --> 00:22:52,840
Speaker 1: which that works. For example, if I show a face

431
00:22:52,880 --> 00:22:56,800
Speaker 1: of somebody who's expressing fear but only for thirty milliseconds,

432
00:22:56,800 --> 00:23:00,239
Speaker 1: which is one movie frame right right, and then a

433
00:23:00,280 --> 00:23:03,360
Speaker 1: mask when you're meeting another face right on top that's neutral,

434
00:23:04,280 --> 00:23:07,080
Speaker 1: or in another condition, I show a happy face, very enthusiastic,

435
00:23:07,160 --> 00:23:09,720
Speaker 1: and then neutral mask. If you ask people did you

436
00:23:09,760 --> 00:23:13,520
Speaker 1: see a happi or fearful face, they say, like, I

437
00:23:13,560 --> 00:23:15,560
Speaker 1: have no idea, I didn't see I didn't see either one.

438
00:23:15,960 --> 00:23:18,159
Speaker 1: But if you look at amigdal activity, which is a

439
00:23:18,200 --> 00:23:22,879
Speaker 1: region that's known to be rapidly detecting potential threats and

440
00:23:23,200 --> 00:23:27,240
Speaker 1: including fear, the amignal activity will respond to fear, not

441
00:23:28,520 --> 00:23:31,760
Speaker 1: in thirty milliseconds, not not happiness in the same way.

442
00:23:31,880 --> 00:23:35,160
Speaker 1: So the brain knows, it's just that it doesn't get

443
00:23:35,160 --> 00:23:40,040
Speaker 1: to the like the publicists desk, you know, good consciousness.

444
00:23:40,080 --> 00:23:41,800
Speaker 2: So I'm so glad you said it that way. So

445
00:23:41,880 --> 00:23:44,840
Speaker 2: don't ask the person, ask the brain. How do you

446
00:23:44,960 --> 00:23:48,159
Speaker 2: think of the different parts of the brain. So obviously

447
00:23:48,680 --> 00:23:51,720
Speaker 2: the amygdala and any of the is it fair to

448
00:23:51,720 --> 00:23:55,760
Speaker 2: say that's part of the limbic system. Yes, So when

449
00:23:55,760 --> 00:23:59,600
Speaker 2: you're talking about the publicist. What portion of the brain

450
00:23:59,720 --> 00:24:02,080
Speaker 2: we just discussing, Well.

451
00:24:01,880 --> 00:24:05,359
Speaker 1: In terms of sheer territory, it's probably not very much.

452
00:24:07,520 --> 00:24:11,480
Speaker 1: Four brain, hind brain were prefrontal cortex would be. And

453
00:24:13,760 --> 00:24:16,600
Speaker 1: there's a lot of sensory procection that's going on, you know,

454
00:24:16,880 --> 00:24:19,960
Speaker 1: pre conscious or like before we could say, you know,

455
00:24:21,040 --> 00:24:23,920
Speaker 1: motion to something or use words to explain what's going on.

456
00:24:24,480 --> 00:24:27,600
Speaker 1: I think it's it's it's genuinely hard to pin down

457
00:24:27,600 --> 00:24:29,919
Speaker 1: a number. Like you know, if I read, for example,

458
00:24:29,920 --> 00:24:32,960
Speaker 1: it's ninety percent subconscious and ten percent conscious, I don't

459
00:24:32,960 --> 00:24:37,000
Speaker 1: know if that's right, and it may vary across life cycle.

460
00:24:39,760 --> 00:24:43,000
Speaker 1: So you know, we usually were reluctant to pin down

461
00:24:43,040 --> 00:24:44,640
Speaker 1: a number. I think it's fair to say that there's

462
00:24:44,640 --> 00:24:46,359
Speaker 1: a lot of things that are going on we usually

463
00:24:46,400 --> 00:24:50,040
Speaker 1: say implicitly that are not People aren't explicitly aware of

464
00:24:50,280 --> 00:24:52,040
Speaker 1: enough enough to make it very interesting.

465
00:24:52,040 --> 00:24:54,840
Speaker 2: So whenever I hear people talk about, you know, things

466
00:24:54,880 --> 00:24:57,280
Speaker 2: happening within the brain that you're not aware of, I

467
00:24:57,280 --> 00:25:01,479
Speaker 2: always think of the split brain experiments and tell us

468
00:25:01,480 --> 00:25:04,600
Speaker 2: a little bit, what does that reveal about our decision

469
00:25:04,600 --> 00:25:05,360
Speaker 2: making process.

470
00:25:05,359 --> 00:25:08,800
Speaker 1: So the split brain was actually first explored by Roger

471
00:25:08,840 --> 00:25:12,760
Speaker 1: Sperry at Caltech actually in his student Mike Zaniga, you know,

472
00:25:12,880 --> 00:25:16,200
Speaker 1: made a big chunk of career over out of it.

473
00:25:16,600 --> 00:25:19,480
Speaker 1: And so the split brain patients means they don't have

474
00:25:19,600 --> 00:25:21,800
Speaker 1: much communication between left and right hemispheres.

475
00:25:21,840 --> 00:25:27,880
Speaker 2: Corpus colosum is that right, So these are the one

476
00:25:27,920 --> 00:25:31,720
Speaker 2: I remember was it was some seizure or epilepsy, and

477
00:25:32,000 --> 00:25:36,160
Speaker 2: they found cutting that stop the seizures. But then your

478
00:25:36,240 --> 00:25:39,600
Speaker 2: left brain and your right brain don't really communicate anymore exactly.

479
00:25:39,960 --> 00:25:42,760
Speaker 1: So for example, so if you have a breakdown of

480
00:25:42,760 --> 00:25:47,679
Speaker 1: corpus closum, the right and left aren't really communicating despite

481
00:25:47,720 --> 00:25:50,760
Speaker 1: the right brain left brain. Most modern ner as signists

482
00:25:50,760 --> 00:25:53,720
Speaker 1: don't think there's that much specialization. There's some interesting kinds,

483
00:25:53,760 --> 00:25:57,919
Speaker 1: but one kind that's pretty rugged is languages mostly in

484
00:25:58,000 --> 00:26:00,520
Speaker 1: the left brain and regions called broke because area of

485
00:26:00,520 --> 00:26:03,120
Speaker 1: Wernicke's area. And we know that because you know, when

486
00:26:03,160 --> 00:26:05,240
Speaker 1: you have specialized damage in that area, you can see

487
00:26:05,240 --> 00:26:08,240
Speaker 1: people start to talk differently, like they can remember they

488
00:26:08,240 --> 00:26:09,119
Speaker 1: can't remember words.

489
00:26:09,119 --> 00:26:12,320
Speaker 2: But the aphasia, yeah, I remember reading about people who

490
00:26:12,320 --> 00:26:15,840
Speaker 2: can speak, could write, but couldn't read. Just all sorts

491
00:26:15,880 --> 00:26:18,600
Speaker 2: of wacky things happen when when those two areas are

492
00:26:18,640 --> 00:26:19,639
Speaker 2: down correct exactly.

493
00:26:19,680 --> 00:26:22,399
Speaker 1: So there are these very localized, pretty well understood A phaseias

494
00:26:22,400 --> 00:26:26,200
Speaker 1: that have to do with local damage. So there's there's

495
00:26:26,200 --> 00:26:28,280
Speaker 1: often what we call plasticity where another part of the

496
00:26:28,320 --> 00:26:30,480
Speaker 1: brain will take over. So if you had some damage

497
00:26:30,640 --> 00:26:32,760
Speaker 1: as a young child, it might be that the A phaseia,

498
00:26:33,320 --> 00:26:35,720
Speaker 1: you know, another another part of their brain like takes

499
00:26:35,720 --> 00:26:38,320
Speaker 1: over that function. But if it happens later in life,

500
00:26:38,440 --> 00:26:42,600
Speaker 1: not so anyway. So language is somewhat specialized the left region. So,

501
00:26:42,720 --> 00:26:47,359
Speaker 1: for example, if someone with a and the sensory systems

502
00:26:47,359 --> 00:26:49,920
Speaker 1: are contralateral, so the right side of the brain sees

503
00:26:49,960 --> 00:26:52,320
Speaker 1: the left side of a picture, left side sees the

504
00:26:52,359 --> 00:26:55,520
Speaker 1: right side. So suppose I show you on the left

505
00:26:55,520 --> 00:26:59,760
Speaker 1: of a picture a picture of a friend of yours,

506
00:27:00,359 --> 00:27:03,960
Speaker 1: and I asked the person, if you see this friend

507
00:27:04,000 --> 00:27:06,480
Speaker 1: of yours, what might what what gesture might you do?

508
00:27:06,560 --> 00:27:08,480
Speaker 1: Or what might you if you see a friend here

509
00:27:08,560 --> 00:27:10,760
Speaker 1: as opposed to a house or a shovel, what would

510
00:27:10,760 --> 00:27:14,280
Speaker 1: you do? And the person waves their hand and then

511
00:27:14,359 --> 00:27:16,520
Speaker 1: you ask them why did you wave your hand? Now,

512
00:27:16,560 --> 00:27:18,639
Speaker 1: the left side of the brain has to answer the

513
00:27:18,720 --> 00:27:21,640
Speaker 1: question because that's the language area. But the left side

514
00:27:21,640 --> 00:27:24,439
Speaker 1: doesn't know that the right side saw a friend and

515
00:27:24,480 --> 00:27:28,200
Speaker 1: that's why they waved. So the left side makes stuff.

516
00:27:27,960 --> 00:27:31,840
Speaker 2: Up, confabulates an explanation for why they're waiting exactly.

517
00:27:31,880 --> 00:27:33,880
Speaker 1: It's like the publicist for you know, for a very

518
00:27:33,920 --> 00:27:37,359
Speaker 1: guilty person and or Mike is not get calls it

519
00:27:37,359 --> 00:27:41,359
Speaker 1: the interpreter. So the interpreter says, I don't really know why,

520
00:27:41,480 --> 00:27:43,760
Speaker 1: so I'll kind of make give a plausible answer, and

521
00:27:43,800 --> 00:27:46,440
Speaker 1: they'll say something like, oh, I saw somebody I knew

522
00:27:46,920 --> 00:27:51,160
Speaker 1: walking by out the window outside. So that's an example

523
00:27:51,160 --> 00:27:54,119
Speaker 1: of where we know what the brain saw and why

524
00:27:54,680 --> 00:27:56,960
Speaker 1: the wave occurred, but the left part of the brain

525
00:27:56,960 --> 00:27:58,240
Speaker 1: doesn't know that.

526
00:27:58,400 --> 00:28:01,840
Speaker 2: That's really that's really fascinating. Let's stay with the idea

527
00:28:01,920 --> 00:28:05,440
Speaker 2: of tracking eye movement. So you could do this with glasses.

528
00:28:05,480 --> 00:28:08,400
Speaker 2: You can do with this with a computer. When you're

529
00:28:08,440 --> 00:28:12,400
Speaker 2: tracking eye movement, asking people about, hey, would you purchase

530
00:28:12,480 --> 00:28:15,680
Speaker 2: this product? How big of a tael is it? When

531
00:28:15,720 --> 00:28:18,040
Speaker 2: they look at the price and is it something they

532
00:28:18,200 --> 00:28:20,800
Speaker 2: just kind of glance at or is it a repeated

533
00:28:20,960 --> 00:28:23,359
Speaker 2: and obvious they're focusing on the cost.

534
00:28:23,720 --> 00:28:26,119
Speaker 1: There's there's sort of two interesting markers. For number one,

535
00:28:26,160 --> 00:28:28,159
Speaker 1: it's not that big of a tell. So if we

536
00:28:28,240 --> 00:28:30,879
Speaker 1: try to predict whether they're going to actually buy something,

537
00:28:31,000 --> 00:28:34,280
Speaker 1: we might get say forty two percent right, and with

538
00:28:34,400 --> 00:28:37,800
Speaker 1: the eye tracking data it might get up to like

539
00:28:37,960 --> 00:28:42,600
Speaker 1: fifty four, you know. So as academics we think that's

540
00:28:42,720 --> 00:28:44,840
Speaker 1: kind of a modest effect size. Now, if you're running

541
00:28:44,840 --> 00:28:47,200
Speaker 1: a business and you want a two percent lift and

542
00:28:47,240 --> 00:28:51,160
Speaker 1: purchase maybe a billion dollars, right, So sometimes we're a

543
00:28:51,160 --> 00:28:53,880
Speaker 1: little cautious as academics about is this a big deal

544
00:28:53,960 --> 00:28:55,880
Speaker 1: or not? I'm going to where's some of these things?

545
00:28:55,880 --> 00:28:57,560
Speaker 1: The same in the world of nudges and so on.

546
00:28:57,640 --> 00:29:00,680
Speaker 1: Sometimes a small you know what, a half increase and

547
00:29:00,720 --> 00:29:04,280
Speaker 1: get out the vote. If we could do that, you know, scientifically,

548
00:29:04,360 --> 00:29:08,200
Speaker 1: may well decide an election. Right anyway, So the lift

549
00:29:08,240 --> 00:29:10,800
Speaker 1: is not that big, but the two taels are basically

550
00:29:10,880 --> 00:29:14,440
Speaker 1: looking at the price, and the other is refixation, which

551
00:29:14,480 --> 00:29:17,680
Speaker 1: basically means not just looking once but going back and forth.

552
00:29:18,080 --> 00:29:21,520
Speaker 1: You know. It's the it's the rapid brain equivalent on

553
00:29:21,600 --> 00:29:24,520
Speaker 1: a one or two second basis of say a couple

554
00:29:24,560 --> 00:29:26,800
Speaker 1: who's shopping for a house, going to look at a

555
00:29:26,800 --> 00:29:29,240
Speaker 1: second time and a third time, you know, the repeated looking.

556
00:29:29,720 --> 00:29:33,160
Speaker 2: Right, usually a good signal exactly tells you the serious Huh.

557
00:29:33,200 --> 00:29:37,600
Speaker 2: That's really interesting. So give us some examples of what

558
00:29:37,680 --> 00:29:41,120
Speaker 2: the studies or the experiments look like. When you're doing

559
00:29:41,160 --> 00:29:44,240
Speaker 2: eye tracking, What are you trying to What parts of

560
00:29:44,280 --> 00:29:46,400
Speaker 2: the brain are you looking at? Or is it just

561
00:29:46,880 --> 00:29:50,040
Speaker 2: the eye tracking? Is it is this by itself or

562
00:29:50,040 --> 00:29:54,320
Speaker 2: can you combine this with other types of neuroeconomics.

563
00:29:54,480 --> 00:29:57,320
Speaker 1: Yeah, So, actually the eye trackers we use, which are

564
00:29:57,640 --> 00:30:02,840
Speaker 1: commercially made for some iis basically and sometimes for clinical use,

565
00:30:03,480 --> 00:30:06,320
Speaker 1: they use cameras to look at what the where the

566
00:30:06,320 --> 00:30:08,120
Speaker 1: eye is looking and they sync that up with where

567
00:30:08,120 --> 00:30:13,160
Speaker 1: on the computer screen you're looking. And so besides the

568
00:30:13,320 --> 00:30:16,040
Speaker 1: location of where the eyes are looking, you also measure

569
00:30:16,080 --> 00:30:20,240
Speaker 1: pupil dilation. And pupil dilation turns out to be, you know,

570
00:30:20,280 --> 00:30:21,800
Speaker 1: the eyes that they went into the soul, so that

571
00:30:21,920 --> 00:30:25,960
Speaker 1: the pupils actually generate a lot of information. Although it's

572
00:30:26,160 --> 00:30:29,280
Speaker 1: it's crude, what the people dilation is telling you is

573
00:30:29,320 --> 00:30:32,400
Speaker 1: about cognitive difficulty. Am I having a hard time thinking

574
00:30:32,400 --> 00:30:36,600
Speaker 1: about this? And arousal, which again may be negative or positive.

575
00:30:36,640 --> 00:30:39,600
Speaker 2: It's like, so wide pupil is you aroused.

576
00:30:41,080 --> 00:30:46,160
Speaker 1: Exactly exactly? And so I think if you train yourself

577
00:30:46,240 --> 00:30:49,240
Speaker 1: and maybe depending on the color of the eyes, you

578
00:30:49,320 --> 00:30:51,920
Speaker 1: might be able to tell, like a poker player might

579
00:30:51,960 --> 00:30:55,719
Speaker 1: be able to train themselves with a to notice pupil dilation,

580
00:30:56,080 --> 00:30:58,360
Speaker 1: but just in case. That's why poker players often will

581
00:30:58,360 --> 00:31:03,360
Speaker 1: wear glasses because these unglasses, right, Because the idea is,

582
00:31:03,360 --> 00:31:05,320
Speaker 1: if you look at your cards and you have two

583
00:31:05,400 --> 00:31:09,480
Speaker 1: ass your people will dilate like and it might be

584
00:31:09,520 --> 00:31:11,280
Speaker 1: hard to see with the naked eye, but the machines

585
00:31:11,320 --> 00:31:13,000
Speaker 1: we use can definitely see it. That would be a

586
00:31:13,000 --> 00:31:15,800
Speaker 1: big jump, you know, a big tell. And so we're

587
00:31:15,840 --> 00:31:19,280
Speaker 1: able to use people dilation and I tracking to judge

588
00:31:19,280 --> 00:31:22,240
Speaker 1: things like cognitive difficulty. A lot of the early studdies

589
00:31:22,280 --> 00:31:25,240
Speaker 1: actually were used in game theory because in game theory,

590
00:31:25,280 --> 00:31:28,120
Speaker 1: the assumption is if I might want to see what

591
00:31:28,160 --> 00:31:31,560
Speaker 1: my opponent's payoff is in order to decide what they're

592
00:31:31,560 --> 00:31:34,600
Speaker 1: going to do. And if you ask people what are

593
00:31:34,640 --> 00:31:36,800
Speaker 1: you looking at on this computer screen? You know, there's

594
00:31:36,960 --> 00:31:39,440
Speaker 1: there's a four by four matrix of numbers, and I'm

595
00:31:39,440 --> 00:31:41,440
Speaker 1: trying to think of what you're going to do. There's

596
00:31:41,440 --> 00:31:43,680
Speaker 1: a lot to look at. And if you ask people

597
00:31:43,680 --> 00:31:45,560
Speaker 1: for a self report, they're not going to tell you

598
00:31:45,560 --> 00:31:47,320
Speaker 1: exactly what their eyes are during the whole time, they're

599
00:31:47,320 --> 00:31:51,640
Speaker 1: probably looking at forty two different things, sometimes very quickly.

600
00:31:51,920 --> 00:31:54,280
Speaker 1: Sometimes they're going back and looking again and again and again.

601
00:31:54,720 --> 00:31:57,560
Speaker 1: They just don't have conscious access to that process the

602
00:31:57,600 --> 00:31:58,800
Speaker 1: way that the eye tracking does.

603
00:31:59,280 --> 00:32:03,400
Speaker 2: So that's really fascinating that speaking to the brain but

604
00:32:03,560 --> 00:32:06,480
Speaker 2: not the person gives you a whole lot more insight

605
00:32:06,680 --> 00:32:12,440
Speaker 2: into the decision making process. Speaking generally, what does this

606
00:32:12,640 --> 00:32:19,800
Speaker 2: tell us about people, as you know, rational profit seeking

607
00:32:20,160 --> 00:32:23,720
Speaker 2: actors in the world of finance and investing.

608
00:32:24,400 --> 00:32:27,840
Speaker 1: I think it's useful to think about, say, young naive investors,

609
00:32:28,000 --> 00:32:29,959
Speaker 1: or that they may to be young, but people who

610
00:32:30,040 --> 00:32:33,360
Speaker 1: have less knowledge about the markets, and people who spend

611
00:32:33,400 --> 00:32:38,200
Speaker 1: a lot more time thinking about estimating fundamentals reading ten K's,

612
00:32:39,480 --> 00:32:43,320
Speaker 1: you know, having years of trading experience. Because another important

613
00:32:43,320 --> 00:32:46,680
Speaker 1: facts which we try to keep track of and biro

614
00:32:46,720 --> 00:32:51,120
Speaker 1: economics is that a lot of decisions and structures people

615
00:32:51,160 --> 00:32:54,080
Speaker 1: have to make are not things that we're necessarily evolved

616
00:32:54,120 --> 00:32:57,600
Speaker 1: to be particularly good at. But people are also extremely

617
00:32:57,640 --> 00:33:01,040
Speaker 1: good at learning and able, you know, to like collect

618
00:33:01,040 --> 00:33:05,920
Speaker 1: memories and distill things into into knowledge. So let me

619
00:33:05,960 --> 00:33:07,840
Speaker 1: turn to the concept of price bubbles because I think

620
00:33:07,840 --> 00:33:10,000
Speaker 1: that's a useful one. So we have a couple of

621
00:33:10,160 --> 00:33:12,840
Speaker 1: one fMRI study on price bubbles, and we have some

622
00:33:12,880 --> 00:33:16,040
Speaker 1: new stuff that includes skin conductive's measurement to see if

623
00:33:16,320 --> 00:33:17,960
Speaker 1: you know, can you kind of predict when a crash

624
00:33:18,040 --> 00:33:21,520
Speaker 1: is coming from people's hands, you know, reflecting nervousness. It

625
00:33:21,760 --> 00:33:24,400
Speaker 1: looks like we can predict a little, but not great.

626
00:33:24,720 --> 00:33:27,440
Speaker 1: You know, that's a high mountain to climb. What we

627
00:33:27,520 --> 00:33:31,640
Speaker 1: found in our first fMRI study about bubbles was people

628
00:33:31,680 --> 00:33:34,840
Speaker 1: trade an artificial asset, so we know the value, the

629
00:33:34,840 --> 00:33:37,840
Speaker 1: fundamental value the asset, which we never know in you know,

630
00:33:37,920 --> 00:33:41,400
Speaker 1: in natural markets, and that the price is completely what

631
00:33:41,440 --> 00:33:44,520
Speaker 1: they agree upon. So typically what happens is that the

632
00:33:44,600 --> 00:33:48,680
Speaker 1: fundamental value is a number that we control, which happens

633
00:33:48,720 --> 00:33:51,400
Speaker 1: to be fourteen, and the value the asset comes from

634
00:33:51,400 --> 00:33:53,400
Speaker 1: the fact that if you hold at the end of

635
00:33:53,400 --> 00:33:56,560
Speaker 1: a period of trading, you get a dividend, or you

636
00:33:56,600 --> 00:34:00,760
Speaker 1: can invest currency in risk free bonds, and so the

637
00:34:00,880 --> 00:34:03,880
Speaker 1: trade off between the risk free earnings and the value

638
00:34:03,880 --> 00:34:06,280
Speaker 1: of the dividends establishes an equlibrium price. It's a very

639
00:34:06,280 --> 00:34:11,440
Speaker 1: simple equation, and typically the price starts around fourteen, it

640
00:34:11,480 --> 00:34:14,360
Speaker 1: goes up to maybe twenty or thirty and then crashes.

641
00:34:14,719 --> 00:34:18,320
Speaker 1: And then in order to bring the experiments to a close,

642
00:34:18,400 --> 00:34:20,760
Speaker 1: we have them trade for fifty periods or thirty periods,

643
00:34:20,760 --> 00:34:22,799
Speaker 1: and at the end they were able to cash the

644
00:34:22,840 --> 00:34:24,080
Speaker 1: assets out at fourteen.

645
00:34:24,800 --> 00:34:26,719
Speaker 2: So what would you pay for an asset that you'll

646
00:34:26,760 --> 00:34:30,640
Speaker 2: get fourteen for correct after a series of dividends thirty

647
00:34:30,719 --> 00:34:32,480
Speaker 2: or fifty trading periods exactly.

648
00:34:32,800 --> 00:34:35,080
Speaker 1: And so put yourselves in the mind, said of somebody

649
00:34:35,080 --> 00:34:39,200
Speaker 1: who in period thirty one the price is sixty, and

650
00:34:39,239 --> 00:34:42,840
Speaker 1: you kind of know that in period fifty nineteen periods

651
00:34:42,840 --> 00:34:46,240
Speaker 1: from now it's going to be fourteen. So well, unless

652
00:34:46,280 --> 00:34:49,080
Speaker 1: you think it's going to go up to seventy five, right,

653
00:34:49,200 --> 00:34:53,920
Speaker 1: So it's true. In fact, that's very helpful for me.

654
00:34:54,040 --> 00:34:57,040
Speaker 1: So what we found from the brain was that there's

655
00:34:57,080 --> 00:34:59,279
Speaker 1: two interesting signals, soh sort with the more interesting one.

656
00:34:59,320 --> 00:35:01,880
Speaker 1: The other one's a little more obvious. The interesting signal

657
00:35:02,000 --> 00:35:06,600
Speaker 1: is people who sold before the bubble crash, which is

658
00:35:06,600 --> 00:35:08,719
Speaker 1: the smart thing to do. And again, the bubble crash

659
00:35:08,760 --> 00:35:10,839
Speaker 1: is not announced. It's something you only see a store

660
00:35:10,880 --> 00:35:13,360
Speaker 1: cooking back and of your mirror, right.

661
00:35:14,040 --> 00:35:16,120
Speaker 2: Same in natural markets exactly.

662
00:35:15,719 --> 00:35:18,080
Speaker 1: Just like a national markets, right, bubbles are only shown

663
00:35:18,120 --> 00:35:20,239
Speaker 1: in hindsight. Gene Falum has written a lot about this.

664
00:35:20,239 --> 00:35:23,040
Speaker 1: That's one reason he's skeptical that we should even talk

665
00:35:23,080 --> 00:35:25,320
Speaker 1: about bubbles, you know, as a scientific phenomens.

666
00:35:25,320 --> 00:35:28,440
Speaker 2: Okay, I think it goes too far with that, but anyway, anyway.

667
00:35:28,160 --> 00:35:30,759
Speaker 1: Yeah, you know what I mean. So it turns out

668
00:35:30,760 --> 00:35:33,080
Speaker 1: the people who are more likely to sell when the

669
00:35:33,120 --> 00:35:35,600
Speaker 1: price is at sixty and we know it's going to crash,

670
00:35:35,719 --> 00:35:40,080
Speaker 1: but we're not sure when have heightened activity and insular cortex,

671
00:35:40,719 --> 00:35:44,040
Speaker 1: which is another region that's involved in emotion and interception.

672
00:35:44,440 --> 00:35:46,120
Speaker 1: So interception means.

673
00:35:46,280 --> 00:35:49,240
Speaker 2: Knowing what's going on on the inside of your own body,

674
00:35:49,280 --> 00:35:50,720
Speaker 2: like a self awareness exactly.

675
00:35:50,760 --> 00:35:54,560
Speaker 1: So perception is the outside world. Interception is the brain's

676
00:35:54,600 --> 00:35:57,399
Speaker 1: like the body's ambassadorship to the brain, you know, knowing

677
00:35:57,440 --> 00:36:02,640
Speaker 1: if I'm nervous or And it's often activated by, particularly

678
00:36:02,680 --> 00:36:06,160
Speaker 1: by negative emotions. So if you see something disgusting, insula,

679
00:36:06,320 --> 00:36:08,719
Speaker 1: if you choke a person a little bit, or you

680
00:36:08,719 --> 00:36:11,080
Speaker 1: you know, you cut off the oxygen, not so it's dangerous,

681
00:36:11,080 --> 00:36:14,839
Speaker 1: but just to make them uncomfortable. Insula, financial uncertainty, the insula.

682
00:36:14,960 --> 00:36:17,840
Speaker 1: And so we think of the insula is the early

683
00:36:17,880 --> 00:36:20,600
Speaker 1: warning signal that there's going to be a crash. And

684
00:36:20,640 --> 00:36:23,439
Speaker 1: the other interesting brain region is nucleus the cummins, which

685
00:36:23,480 --> 00:36:28,040
Speaker 1: is basically a reward center and it's called straightum part

686
00:36:28,040 --> 00:36:31,399
Speaker 1: of basoganglia in the very center of the brain, and

687
00:36:31,440 --> 00:36:34,320
Speaker 1: that's active in the people who are fueling the bubble.

688
00:36:34,440 --> 00:36:38,319
Speaker 1: Like when the bubbles, you know, forming the people who

689
00:36:38,360 --> 00:36:41,520
Speaker 1: have the highest nucleus incumbans activity by the most.

690
00:36:41,680 --> 00:36:44,560
Speaker 2: So you have a run of traders participating in this,

691
00:36:44,719 --> 00:36:48,880
Speaker 2: and you could tell by the brain activity who's contributing

692
00:36:48,920 --> 00:36:51,600
Speaker 2: to the bubble and who's saying, this is getting crazy.

693
00:36:51,640 --> 00:36:54,240
Speaker 2: I want to take my chips off the tails now.

694
00:36:54,560 --> 00:36:57,960
Speaker 1: Number one, we can't tell with exquisite precision. You know,

695
00:36:58,000 --> 00:37:00,399
Speaker 1: you can sort of see these groups, and we're only

696
00:37:00,560 --> 00:37:03,760
Speaker 1: looking at this X post. So I think it's it's

697
00:37:03,800 --> 00:37:07,759
Speaker 1: conceivable but challenging to do this in real time, you know.

698
00:37:07,840 --> 00:37:10,520
Speaker 1: So there's you're watching the market unfold. You're doing real

699
00:37:10,560 --> 00:37:14,480
Speaker 1: time from eye measurement that can be done, and it's like, okay,

700
00:37:14,520 --> 00:37:17,359
Speaker 1: traders seven, nine and eleven, you know, we think they're

701
00:37:17,360 --> 00:37:20,200
Speaker 1: probably going to sell. There the skeptics, they're the bulls

702
00:37:20,719 --> 00:37:24,920
Speaker 1: and fourteen, seventeen and twenty one. Their new kidcombanis Activity.

703
00:37:24,960 --> 00:37:27,000
Speaker 1: Seems they're really all in. They're going to be forming

704
00:37:27,000 --> 00:37:28,480
Speaker 1: the bubble and so on and so on. I mean,

705
00:37:28,520 --> 00:37:30,520
Speaker 1: we're a few steps away from be able to do it,

706
00:37:30,560 --> 00:37:33,400
Speaker 1: but we see these what we call proofs of concept

707
00:37:33,640 --> 00:37:36,239
Speaker 1: like it can be done. It may take a few

708
00:37:36,280 --> 00:37:38,680
Speaker 1: million dollars if any donors are listening.

709
00:37:39,239 --> 00:37:42,360
Speaker 2: But it makes perfect sense that that is possible. Different

710
00:37:42,400 --> 00:37:47,279
Speaker 2: parts of the brain are responding to different inputs, and

711
00:37:47,320 --> 00:37:51,200
Speaker 2: it's consistent with what we've observed amongst sure, you know,

712
00:37:51,400 --> 00:37:55,919
Speaker 2: just various investors and traders. There are people with as

713
00:37:56,000 --> 00:37:59,720
Speaker 2: the you know, in the latter stages of a bull market,

714
00:38:00,360 --> 00:38:02,920
Speaker 2: they think it's just going to keep going forever and

715
00:38:02,960 --> 00:38:06,080
Speaker 2: they pile in. And the flip side of that, there

716
00:38:06,080 --> 00:38:10,600
Speaker 2: are people the famous irrational zuberance speech by Alan Greenspan

717
00:38:11,239 --> 00:38:13,800
Speaker 2: in nineteen ninety six. You still had a ton of

718
00:38:14,440 --> 00:38:18,640
Speaker 2: gains until the March two thousand and top. So some people,

719
00:38:19,320 --> 00:38:23,080
Speaker 2: I'm just curious what drives that. Now that you know

720
00:38:23,320 --> 00:38:26,080
Speaker 2: what to look for and how to measure it in

721
00:38:26,160 --> 00:38:28,920
Speaker 2: traders in real time, what do you think is the

722
00:38:29,000 --> 00:38:32,840
Speaker 2: underlying drivers of whether a person is going to be

723
00:38:32,920 --> 00:38:35,439
Speaker 2: participating in one tribe or the other.

724
00:38:36,440 --> 00:38:38,799
Speaker 1: That's a great question. I'll say a little tiny bit

725
00:38:38,840 --> 00:38:41,919
Speaker 1: more about that. You mentioned the term irrational exuberance, which

726
00:38:42,040 --> 00:38:44,560
Speaker 1: was coined, as I recall, by Bob Shiller in his

727
00:38:44,600 --> 00:38:45,520
Speaker 1: book about.

728
00:38:46,000 --> 00:38:51,920
Speaker 2: I think it was from the Irrational Zuberant speech. Schiller

729
00:38:51,960 --> 00:38:56,240
Speaker 2: may have helped Greenspan with that speech, if I'm remembering,

730
00:38:56,280 --> 00:38:58,960
Speaker 2: because I've seen I've seen both, whether it was Schiller's

731
00:38:59,000 --> 00:39:00,200
Speaker 2: phrase or green Span.

732
00:39:00,080 --> 00:39:02,759
Speaker 1: It maybe it maybe, you know, it's kind of some

733
00:39:03,120 --> 00:39:06,200
Speaker 1: you know, some apocryphal. You know, we're not sure exactly

734
00:39:06,200 --> 00:39:08,000
Speaker 1: who said it first, but certainly there was a kind

735
00:39:08,000 --> 00:39:10,200
Speaker 1: of meeting of the minds that this was useful. And

736
00:39:10,239 --> 00:39:12,719
Speaker 1: in fact, when we didn't we use the phrase in

737
00:39:12,760 --> 00:39:14,320
Speaker 1: our paper, but we didn't put it in the title.

738
00:39:14,360 --> 00:39:17,319
Speaker 1: It just seemed a little too unscientific. It's okay for

739
00:39:17,520 --> 00:39:20,080
Speaker 1: USA today or something. But this is the proceedings of

740
00:39:20,080 --> 00:39:23,279
Speaker 1: the National Academy of Sciences, you know, And but we

741
00:39:23,320 --> 00:39:26,200
Speaker 1: think of this nucleus incumbents activity. That's that's the measure

742
00:39:26,200 --> 00:39:30,000
Speaker 1: of irrational exuberance. And the irrational part is, you know,

743
00:39:30,040 --> 00:39:33,160
Speaker 1: when it's too high, you're gonna end up paying a

744
00:39:33,239 --> 00:39:37,160
Speaker 1: high price for something it crashes fast. So this irrational

745
00:39:37,239 --> 00:39:41,440
Speaker 1: is really in there. Literally. But yeah, and and also

746
00:39:41,480 --> 00:39:45,160
Speaker 1: we when I present this in academic summers and later

747
00:39:45,200 --> 00:39:48,799
Speaker 1: today i'm meeting some Caltech people, we talk about this

748
00:39:48,840 --> 00:39:52,320
Speaker 1: famous saying from Warren Buffett. I believe when people are afraid,

749
00:39:52,440 --> 00:39:56,040
Speaker 1: be greedy, when people ready be afraid. And this brain

750
00:39:56,080 --> 00:40:00,760
Speaker 1: areas like insula is similar to fear and greed and inclusivecumbents.

751
00:40:00,800 --> 00:40:02,439
Speaker 1: You know, it's about as close you're going to get

752
00:40:03,000 --> 00:40:06,719
Speaker 1: to brain areas matching what Warren Buffett had to say,

753
00:40:06,719 --> 00:40:08,840
Speaker 1: which was such a wise thought.

754
00:40:08,600 --> 00:40:11,719
Speaker 2: So you really kind of answered the question I was

755
00:40:11,760 --> 00:40:15,680
Speaker 2: about to ask, which is why has behavioral economics been

756
00:40:15,760 --> 00:40:21,640
Speaker 2: so successful describing decision making where traditional economics seems to

757
00:40:21,760 --> 00:40:25,840
Speaker 2: have faltered. But what you're really saying is we don't

758
00:40:25,880 --> 00:40:27,880
Speaker 2: know what's going on in our brain when we're making

759
00:40:27,920 --> 00:40:32,160
Speaker 2: decisions as individuals, And when you look underneath the hood,

760
00:40:32,160 --> 00:40:35,279
Speaker 2: it turns out there's a lot more things happening than

761
00:40:35,840 --> 00:40:38,320
Speaker 2: at least classical economics seems to imply.

762
00:40:38,800 --> 00:40:43,680
Speaker 1: Yes, exactly exactly. And also, this isn't something we've carefully researched,

763
00:40:43,680 --> 00:40:46,000
Speaker 1: but I think it's a good speculation for your audience,

764
00:40:46,000 --> 00:40:48,560
Speaker 1: which is like when I was going to Chicago in

765
00:40:48,560 --> 00:40:51,719
Speaker 1: the late seventies, all my gratitude and friends were also

766
00:40:51,840 --> 00:40:54,920
Speaker 1: kind of critics of Nobody liked Bay of economics at

767
00:40:54,920 --> 00:40:58,640
Speaker 1: that time, though oh really, oh yeah it was you know,

768
00:40:58,680 --> 00:41:00,560
Speaker 1: people said things like I think, you know, I'm worried

769
00:41:00,600 --> 00:41:02,719
Speaker 1: you might be ruining your career because you switched out

770
00:41:02,719 --> 00:41:06,200
Speaker 1: of finance and well, and what it was was there

771
00:41:06,239 --> 00:41:10,120
Speaker 1: was a series of critical questions which were, but if

772
00:41:10,160 --> 00:41:14,040
Speaker 1: people make all these mistakes, couldn't someone profit from you know,

773
00:41:14,719 --> 00:41:18,560
Speaker 1: arbitrage or from selling them crappy goods, Like well, it

774
00:41:18,560 --> 00:41:21,359
Speaker 1: seems like that may happen, you know, Or if people

775
00:41:21,400 --> 00:41:23,400
Speaker 1: make these mistakes, don't they learn over time not to

776
00:41:23,440 --> 00:41:25,640
Speaker 1: make mistakes? That may also happen, and maybe that there's

777
00:41:25,640 --> 00:41:27,880
Speaker 1: a sucker born every minute. But there's a you know,

778
00:41:27,920 --> 00:41:31,120
Speaker 1: a generational process, and markets are always filled with some

779
00:41:31,160 --> 00:41:35,680
Speaker 1: combination of new investors or you know, sovereign funds of

780
00:41:35,719 --> 00:41:38,880
Speaker 1: people who aren't very savvy about markets or something like that.

781
00:41:39,360 --> 00:41:41,160
Speaker 1: So early in the history of behaval economics, there was

782
00:41:41,200 --> 00:41:44,839
Speaker 1: really a lot of hostility about it, and then we

783
00:41:44,880 --> 00:41:48,960
Speaker 1: gradually one thing about Chicago, and the economics profession in

784
00:41:48,960 --> 00:41:53,440
Speaker 1: general is data do win arguments, so ideology will often persist,

785
00:41:54,280 --> 00:41:57,400
Speaker 1: like for Gene Fama, for example, he's he'll always be

786
00:41:57,440 --> 00:42:02,160
Speaker 1: skeptical about behavioral finance for his own reasons, and you know,

787
00:42:02,280 --> 00:42:06,640
Speaker 1: their ideas. But eventually data went arguments, and there were

788
00:42:06,880 --> 00:42:09,120
Speaker 1: you know, there were just so many anomalies and ways

789
00:42:09,120 --> 00:42:12,200
Speaker 1: in which investors were making mistakes. And it wasn't just

790
00:42:12,239 --> 00:42:16,840
Speaker 1: small investors, you know, who were refinancing their mortgage mistakenly.

791
00:42:16,880 --> 00:42:19,480
Speaker 1: It was you know, some of these implicit things maybe

792
00:42:19,719 --> 00:42:23,239
Speaker 1: very big, you know, like venture capitalists joked about how

793
00:42:23,600 --> 00:42:25,840
Speaker 1: well you know when I think of Mark Zuckerberg and

794
00:42:25,840 --> 00:42:28,200
Speaker 1: a hoodie, and that's kind of my template for a

795
00:42:28,200 --> 00:42:31,960
Speaker 1: good founder to invest tens of millions of dollars. Hey, like,

796
00:42:32,480 --> 00:42:34,839
Speaker 1: that's not as sophisticated, that's not how economics.

797
00:42:35,200 --> 00:42:38,960
Speaker 2: And I recall reading one of the papers Bob Schiller

798
00:42:39,040 --> 00:42:42,879
Speaker 2: wrote was looking at divinend yield and saying, if if

799
00:42:42,960 --> 00:42:47,080
Speaker 2: markets are fully pricing in all data, why does this

800
00:42:47,160 --> 00:42:49,719
Speaker 2: divin and yield swing around so much? It should be

801
00:42:49,800 --> 00:42:53,440
Speaker 2: much more consistent than this correct, but apparently it's not.

802
00:42:54,320 --> 00:42:57,200
Speaker 2: I just I was very amused by Fama and Schiller

803
00:42:57,320 --> 00:43:00,480
Speaker 2: being awarded the Nobel together. It's almost says if the

804
00:43:00,520 --> 00:43:04,400
Speaker 2: committee said, look, markets are kind of efficient, and except

805
00:43:04,400 --> 00:43:06,920
Speaker 2: when they go crazy, you two guys work it out.

806
00:43:07,080 --> 00:43:10,279
Speaker 1: Yes, yeah, yeah, it was quite a It was kind

807
00:43:10,280 --> 00:43:13,600
Speaker 1: of a charming and I think sensible award for that reason.

808
00:43:13,719 --> 00:43:16,960
Speaker 1: And the you know, the journalist said like, well is

809
00:43:17,000 --> 00:43:19,839
Speaker 1: there you know, one person says a is true, one

810
00:43:19,880 --> 00:43:21,759
Speaker 1: says A is not always true? Like how could you

811
00:43:21,800 --> 00:43:24,239
Speaker 1: give that award? The answers they both made made a

812
00:43:24,239 --> 00:43:27,520
Speaker 1: lot of progress, you know, in different ways.

813
00:43:27,880 --> 00:43:31,000
Speaker 2: Let's talk about some of the other ways that we

814
00:43:31,080 --> 00:43:35,040
Speaker 2: can look inside. Are we looking at things like adrenaline

815
00:43:35,120 --> 00:43:38,440
Speaker 2: or dopamine or any of the sort of hormones that

816
00:43:38,520 --> 00:43:41,960
Speaker 2: seem to affect our behavior when when we're trying to

817
00:43:42,000 --> 00:43:43,200
Speaker 2: analyze decision making.

818
00:43:43,640 --> 00:43:47,960
Speaker 1: Yeah, so, actually that's a very good question, Barry. New

819
00:43:48,040 --> 00:43:51,600
Speaker 1: economics uses a lot of different methods. The fMRI is

820
00:43:51,640 --> 00:43:54,200
Speaker 1: sort of like, you know the movie star and a

821
00:43:54,200 --> 00:43:56,880
Speaker 1: family with four sisters, you know, the glamorous one that

822
00:43:56,880 --> 00:43:59,759
Speaker 1: everyone pays attention to, but it's actually high maintenance. And

823
00:43:59,800 --> 00:44:02,120
Speaker 1: then but all the other siblings are you know, kind

824
00:44:02,120 --> 00:44:05,800
Speaker 1: of contributing in some interesting way. So pharmacology is something

825
00:44:05,800 --> 00:44:07,319
Speaker 1: people are really interested in.

826
00:44:07,480 --> 00:44:11,920
Speaker 2: Meaning specifically pharmacology drugs that aren't yes, pharmacolo.

827
00:44:11,880 --> 00:44:14,800
Speaker 1: So pharmacology is drugs, but some of those, for example,

828
00:44:14,840 --> 00:44:18,960
Speaker 1: el dopa will actually ramp up dopamine levels and you

829
00:44:18,960 --> 00:44:20,560
Speaker 1: can see if some interesting things happen.

830
00:44:20,719 --> 00:44:23,719
Speaker 2: El Dopa is a drug you can consume correct in

831
00:44:23,800 --> 00:44:25,280
Speaker 2: order to raise your dopamine exactly.

832
00:44:25,440 --> 00:44:29,000
Speaker 1: So it's it's al Dopa's basically administrative. So Parkinson's patients

833
00:44:29,600 --> 00:44:33,279
Speaker 1: have a degradation of dopamine and so to kind of

834
00:44:33,360 --> 00:44:36,000
Speaker 1: ramp them up to normal levels. Al dopa is often

835
00:44:36,080 --> 00:44:37,240
Speaker 1: used in treatment.

836
00:44:37,440 --> 00:44:40,880
Speaker 2: Pharmacology is one what are some of the other forces,

837
00:44:41,160 --> 00:44:41,400
Speaker 2: So we.

838
00:44:41,760 --> 00:44:45,839
Speaker 1: Do look at neurotransmitters like oxytocin, argoniine Vasopressint is one

839
00:44:45,840 --> 00:44:46,520
Speaker 1: that we've studied.

840
00:44:46,840 --> 00:44:51,280
Speaker 2: Isytocin sounds a lot like OxyContin any correct overlap.

841
00:44:51,160 --> 00:44:56,600
Speaker 1: No exactually, so oxytocin is is sometimes called as like

842
00:44:56,640 --> 00:45:00,040
Speaker 1: an affiliation hormone. So for example, if you get a

843
00:45:00,080 --> 00:45:02,960
Speaker 1: really pleasurable massage, you might feel a surge of oxytocin.

844
00:45:04,280 --> 00:45:09,480
Speaker 1: When my wife was giving birth, they often to induce labor.

845
00:45:09,520 --> 00:45:14,479
Speaker 1: They often give somebody synthetic oxytocin, and oxytocin is also

846
00:45:14,520 --> 00:45:16,919
Speaker 1: produced after birth and when the mom is first coming,

847
00:45:16,960 --> 00:45:20,120
Speaker 1: the baby and probably the dad, although maybe less. You know,

848
00:45:20,160 --> 00:45:22,640
Speaker 1: it's this very pleasurable thing that makes you want to

849
00:45:22,840 --> 00:45:26,600
Speaker 1: like hug somebody and feel affiliated. It's affiliated. It's this

850
00:45:26,680 --> 00:45:29,800
Speaker 1: sort of bio term. So there's a bunch of studies

851
00:45:29,800 --> 00:45:34,600
Speaker 1: on oxidosins yesting that improved trust. But there's a cautionary tale,

852
00:45:34,640 --> 00:45:37,759
Speaker 1: which is me and some colleagues went back and looked

853
00:45:37,760 --> 00:45:41,840
Speaker 1: at those carefully, and it seems that giving people artificial

854
00:45:41,960 --> 00:45:45,400
Speaker 1: is giving people oxytocin for a modest dose and then

855
00:45:45,440 --> 00:45:48,280
Speaker 1: see what happens, you know, an hour later. It improves

856
00:45:48,320 --> 00:45:54,080
Speaker 1: trust a little bit, but it's scientifically very very tricky

857
00:45:54,200 --> 00:45:56,840
Speaker 1: and some of the standard results if you do the

858
00:45:56,880 --> 00:45:59,680
Speaker 1: same exact experiment over again, you just don't always get

859
00:45:59,680 --> 00:46:02,600
Speaker 1: the same result. So we don't know how sturdy oxytocin is.

860
00:46:03,000 --> 00:46:04,880
Speaker 2: What What are some of the other chemicals you mentioned

861
00:46:04,880 --> 00:46:06,200
Speaker 2: neurotrano When.

862
00:46:06,040 --> 00:46:08,799
Speaker 1: We studied I'll say a little bit of was argonon vasopressin,

863
00:46:09,440 --> 00:46:13,799
Speaker 1: So that's another hormone which is similar to oxytocin, and

864
00:46:13,800 --> 00:46:18,000
Speaker 1: that when when animals are bonding in groups, this organon

865
00:46:18,080 --> 00:46:20,880
Speaker 1: vasopressant sort of you know, you'll get a surge and

866
00:46:21,200 --> 00:46:21,759
Speaker 1: it shows that.

867
00:46:22,040 --> 00:46:24,120
Speaker 2: So when you say bonding in groups, I'm thinking of

868
00:46:24,160 --> 00:46:27,520
Speaker 2: a wolf pack or a hyena pack, where yes, they're

869
00:46:27,600 --> 00:46:32,399
Speaker 2: cooperative species that work together, and there are chemicals that

870
00:46:33,280 --> 00:46:35,880
Speaker 2: contribute to that. Is that Is that what we're suggesting exactly?

871
00:46:35,920 --> 00:46:36,920
Speaker 1: So part of me.

872
00:46:37,120 --> 00:46:42,080
Speaker 2: Wants to say we're just meat sex operating obliviously to

873
00:46:42,160 --> 00:46:46,600
Speaker 2: what's going on underneath our skin, where we think it's

874
00:46:46,640 --> 00:46:48,880
Speaker 2: free will, But it sounds like there's a lot of

875
00:46:48,880 --> 00:46:53,720
Speaker 2: things happening below the surface that's really influencing our decision making.

876
00:46:53,880 --> 00:46:56,560
Speaker 1: Yeah, oh absolutely. I mean think about things like breathing.

877
00:46:56,760 --> 00:47:00,480
Speaker 1: You know, breathing is so automatic, then when we stop

878
00:47:00,560 --> 00:47:02,640
Speaker 1: and do sort of breath work and try to think

879
00:47:02,680 --> 00:47:04,840
Speaker 1: about it, like the Navy seals might have a breathing

880
00:47:04,840 --> 00:47:07,640
Speaker 1: exercise to calm down before a terrifying thing they have

881
00:47:07,719 --> 00:47:10,160
Speaker 1: to take. You know, it actually takes a lot of

882
00:47:10,200 --> 00:47:13,200
Speaker 1: executive function to think about breathing because we never have.

883
00:47:13,160 --> 00:47:14,320
Speaker 2: To because it's automated.

884
00:47:14,520 --> 00:47:16,800
Speaker 1: Because it's so automated. So the fact that it's actually

885
00:47:17,280 --> 00:47:19,520
Speaker 1: grabs a lot of attention is because the automation is

886
00:47:19,920 --> 00:47:23,080
Speaker 1: we've completely flipped back in the opposite situation. Let me

887
00:47:23,120 --> 00:47:26,040
Speaker 1: tell you Urgan investor Pressen's study. We did. So. There's

888
00:47:26,080 --> 00:47:28,359
Speaker 1: a game similar to prison die Lemma, but not the same,

889
00:47:28,800 --> 00:47:31,480
Speaker 1: called the Stag hunt game, and the idea is two

890
00:47:31,480 --> 00:47:34,439
Speaker 1: people decide to show up in the morning and hunt

891
00:47:34,520 --> 00:47:37,200
Speaker 1: for a stag is a very old fashioned name from

892
00:47:37,280 --> 00:47:39,920
Speaker 1: the Jean jau Bussau and the sixteen hundreds.

893
00:47:40,040 --> 00:47:43,439
Speaker 2: We're talking about a male elk or deer.

894
00:47:43,520 --> 00:47:45,480
Speaker 1: Yeah, an elk or deer. Yeah. The point of the

895
00:47:45,480 --> 00:47:47,799
Speaker 1: stag is it's so big that no one person can't

896
00:47:47,800 --> 00:47:50,680
Speaker 1: catch themselves. One person has to spot and the other

897
00:47:50,719 --> 00:47:53,799
Speaker 1: a shoot or something like that, or they cannot show

898
00:47:53,840 --> 00:47:55,600
Speaker 1: up in the morning at the appointed spot and just

899
00:47:55,680 --> 00:47:58,440
Speaker 1: hunt for rabbits on their own. And so the structure

900
00:47:58,480 --> 00:48:00,960
Speaker 1: of the game when we do it with money or

901
00:48:01,080 --> 00:48:05,759
Speaker 1: reward with animals is you get one point if you

902
00:48:05,880 --> 00:48:08,440
Speaker 1: just go for rabbit. If you both hunt for stag,

903
00:48:08,520 --> 00:48:10,319
Speaker 1: you get two if you hunt for stag. But if

904
00:48:10,360 --> 00:48:13,080
Speaker 1: you show up by yourself prepared to hunt for stag,

905
00:48:13,200 --> 00:48:16,120
Speaker 1: you can't catch it and you get zero. And so

906
00:48:16,200 --> 00:48:19,920
Speaker 1: the choosing a rabbit is choosing one and not helping

907
00:48:19,920 --> 00:48:22,719
Speaker 1: your friend both showing up for STAG is better for

908
00:48:22,760 --> 00:48:25,280
Speaker 1: the both of them, but they have to somehow coordinate

909
00:48:25,320 --> 00:48:28,000
Speaker 1: that activity. And so what we found was when you

910
00:48:28,040 --> 00:48:32,839
Speaker 1: give people this AVP and it's a crossover design, which

911
00:48:32,880 --> 00:48:34,759
Speaker 1: means sometimes they get AVP and sometimes they get a

912
00:48:34,760 --> 00:48:38,440
Speaker 1: placebo because there's a well known placebo effect where if

913
00:48:38,440 --> 00:48:40,319
Speaker 1: they think maybe they got the a VP, it might

914
00:48:40,719 --> 00:48:44,920
Speaker 1: subconsciously affect the behavior. So we always control for placebo effects,

915
00:48:44,920 --> 00:48:46,680
Speaker 1: just like in drug trials, you know, the same thing

916
00:48:46,760 --> 00:48:50,160
Speaker 1: very routine. When you give them a VP, they're more

917
00:48:50,239 --> 00:48:53,759
Speaker 1: likely to choose STAG, which is the socially risky and

918
00:48:53,840 --> 00:48:58,040
Speaker 1: beneficial thing. It's like it generates this willingness to join

919
00:48:58,080 --> 00:49:00,000
Speaker 1: the group in a way that's going to help better

920
00:49:00,000 --> 00:49:03,839
Speaker 1: everybody if another if you people join. And the other

921
00:49:03,880 --> 00:49:07,040
Speaker 1: thing that was really nice in this paper was we

922
00:49:07,200 --> 00:49:10,080
Speaker 1: also used fhor Mari. So we had two groups of

923
00:49:10,120 --> 00:49:14,000
Speaker 1: people with administering a VP, one group of scan and

924
00:49:14,040 --> 00:49:16,080
Speaker 1: one was not scan, which is just to see, like

925
00:49:16,120 --> 00:49:18,239
Speaker 1: to replicate, do you get the same behavioral thing if

926
00:49:18,280 --> 00:49:20,280
Speaker 1: they're not. You know, boom boom boom in the scanner

927
00:49:20,840 --> 00:49:24,399
Speaker 1: and in the scanner you see activity globist palatue, which

928
00:49:24,440 --> 00:49:27,719
Speaker 1: is known to be it's a small region. It's not

929
00:49:27,840 --> 00:49:29,960
Speaker 1: one of the more familiar areas you know that show

930
00:49:30,040 --> 00:49:32,759
Speaker 1: up a lots over and over in economics like Bezo Ganglia,

931
00:49:33,400 --> 00:49:38,360
Speaker 1: A Magdola, Nsula, PFC. But you do see activity globist

932
00:49:38,400 --> 00:49:44,279
Speaker 1: paladue when people under a VP are choosing STAG, So

933
00:49:44,320 --> 00:49:46,680
Speaker 1: it looks like the the a VP is sort of

934
00:49:46,680 --> 00:49:48,320
Speaker 1: promoting this STAG choice.

935
00:49:48,600 --> 00:49:52,440
Speaker 2: But when we see people working cooperatively, you see a

936
00:49:52,520 --> 00:49:56,319
Speaker 2: similar neurotransmitter as you do in the.

937
00:49:56,239 --> 00:49:58,920
Speaker 1: Path and it's and it's and it's causal. Right, So

938
00:49:58,960 --> 00:50:01,040
Speaker 1: these are the group of people and sometimes they just

939
00:50:01,080 --> 00:50:01,440
Speaker 1: get this.

940
00:50:01,480 --> 00:50:05,080
Speaker 2: Drug and it makes them want to cooperate.

941
00:50:04,719 --> 00:50:06,399
Speaker 1: And it makes them want to cooperate in a way

942
00:50:06,440 --> 00:50:08,600
Speaker 1: that with this risky it benefits the group. But we

943
00:50:08,719 --> 00:50:12,879
Speaker 1: sometimes think of it it overcomes their inhibition to be well,

944
00:50:12,960 --> 00:50:14,600
Speaker 1: I don't know if you're going to choose STAGG, and

945
00:50:14,760 --> 00:50:16,120
Speaker 1: I don't know if you're going to show up well.

946
00:50:16,160 --> 00:50:19,360
Speaker 2: The prisoner's dilemma is you're always better off throwing the

947
00:50:19,360 --> 00:50:21,240
Speaker 2: other person under the bush.

948
00:50:21,400 --> 00:50:24,560
Speaker 1: This is not that because here's the other person helps out,

949
00:50:24,600 --> 00:50:26,640
Speaker 1: you want to help out too. It's the best response.

950
00:50:26,800 --> 00:50:29,640
Speaker 1: So it's different structurally than the prison's dilemma.

951
00:50:29,719 --> 00:50:33,560
Speaker 2: So I keep coming back every time I read a

952
00:50:33,640 --> 00:50:37,799
Speaker 2: new anything about behavioral finance, new economics, anything about this.

953
00:50:38,600 --> 00:50:41,399
Speaker 2: I can help but come back to the conclusion that

954
00:50:42,520 --> 00:50:47,520
Speaker 2: all of our evolutionary biology has led us to a

955
00:50:47,680 --> 00:50:54,080
Speaker 2: state where we're so well adapted to adjusting to changes

956
00:50:54,120 --> 00:50:57,319
Speaker 2: in the natural world, and all of those things that

957
00:50:57,360 --> 00:51:00,920
Speaker 2: have developed over the millennia really don't help us in

958
00:51:00,920 --> 00:51:04,960
Speaker 2: the modern world. If anything, it's probably certainly in investing.

959
00:51:05,000 --> 00:51:06,640
Speaker 2: It seems to be pretty problematic.

960
00:51:07,000 --> 00:51:10,000
Speaker 1: Yeah, exactly. In fact, that's called the evolutionary mismatch hypothesis.

961
00:51:10,120 --> 00:51:11,960
Speaker 2: Oh really, I didn't know it had a name.

962
00:51:12,040 --> 00:51:15,200
Speaker 3: Yes, exactly, So tell us about it, called the Ritholts

963
00:51:16,520 --> 00:51:21,480
Speaker 3: if only so, this mismatch is simply we evolve to

964
00:51:21,560 --> 00:51:24,240
Speaker 3: adapt on the savannah, and that doesn't help us figure

965
00:51:24,280 --> 00:51:25,359
Speaker 3: out which bonds to buy.

966
00:51:25,440 --> 00:51:26,200
Speaker 2: Is it that simple?

967
00:51:26,360 --> 00:51:30,120
Speaker 1: Exactly exactly. So another way to think of it is

968
00:51:30,120 --> 00:51:34,319
Speaker 1: is institutions. Sometimes it's families, it's political advertisement. It might

969
00:51:34,360 --> 00:51:38,600
Speaker 1: be fine print about fees in a you know, in

970
00:51:38,680 --> 00:51:42,120
Speaker 1: a financial advertisement. Those are all things that are kind

971
00:51:42,120 --> 00:51:47,759
Speaker 1: of tricking or exploiting vulnerabilities in our basic ancestral biology.

972
00:51:48,080 --> 00:51:51,000
Speaker 1: Now again, people are smart too, so there's there is

973
00:51:51,080 --> 00:51:54,759
Speaker 1: adaptation and kind of plasticity. So over a lifetime you

974
00:51:54,840 --> 00:51:59,560
Speaker 1: might or maybe in one mba course or even possibly

975
00:51:59,560 --> 00:52:02,680
Speaker 1: a high schoo of course, you might learn some principles

976
00:52:02,680 --> 00:52:05,480
Speaker 1: of basic finance that really help you avoid dumb mistakes.

977
00:52:05,600 --> 00:52:08,640
Speaker 1: You know, like compound interest really compounds quickly. You know,

978
00:52:09,160 --> 00:52:13,600
Speaker 1: the Caveman brain thinks compounding quickly. I have no idea

979
00:52:13,640 --> 00:52:16,640
Speaker 1: what that means. My brain can't imagine if I invested

980
00:52:16,640 --> 00:52:19,040
Speaker 1: in the S and P one thousand dollars forty years ago,

981
00:52:19,120 --> 00:52:21,440
Speaker 1: how much i'd have. You know, I can't compute that number.

982
00:52:21,920 --> 00:52:24,880
Speaker 2: Well, we live in an arithmetic world. Exponential numbers, they

983
00:52:24,880 --> 00:52:26,200
Speaker 2: are hard to comprehend.

984
00:52:26,560 --> 00:52:30,279
Speaker 1: The brain is mostly linearizing things that and if they're

985
00:52:30,280 --> 00:52:36,280
Speaker 1: not linear, or they're dramatically nonlinear, like pandemic compound interest,

986
00:52:36,800 --> 00:52:38,560
Speaker 1: we can learn to overcome it. But we need these

987
00:52:38,600 --> 00:52:41,759
Speaker 1: kind of external tools. It's almost like exoskeleton, you know,

988
00:52:41,760 --> 00:52:44,200
Speaker 1: whether it's education advisors and so on.

989
00:52:44,600 --> 00:52:47,680
Speaker 2: So let's talk a little bit about risk aversion, which

990
00:52:47,719 --> 00:52:54,320
Speaker 2: has been this behavioral finance concept. People dislike losses twice

991
00:52:54,320 --> 00:52:58,480
Speaker 2: as much as they enjoy gains. What does a world

992
00:52:58,560 --> 00:53:02,040
Speaker 2: of neuroeconomics say about loss of version. I've seen a

993
00:53:02,040 --> 00:53:08,680
Speaker 2: few mathematicians claim, oh, it's just a statistical anomaly. I

994
00:53:08,760 --> 00:53:10,760
Speaker 2: remain unconvinced that that's the case.

995
00:53:11,320 --> 00:53:13,480
Speaker 1: Yeah, so, actually I know a lot about loss of persion.

996
00:53:13,520 --> 00:53:16,879
Speaker 1: We published a meta analysis last year about.

997
00:53:16,680 --> 00:53:19,040
Speaker 2: There's a reason I'm asking this question. It's not out

998
00:53:19,040 --> 00:53:19,920
Speaker 2: of left field.

999
00:53:19,760 --> 00:53:23,319
Speaker 1: Right, you came to the right place. So in the

1000
00:53:23,320 --> 00:53:25,719
Speaker 1: men analysis, we looked at hundreds of studies, basically every

1001
00:53:25,760 --> 00:53:29,520
Speaker 1: study we could find, you know, using informatics, and nowadays

1002
00:53:29,560 --> 00:53:32,360
Speaker 1: you can really do this. It's like a industrial fishing,

1003
00:53:32,400 --> 00:53:34,080
Speaker 1: you know, you throw this net out and you get

1004
00:53:34,320 --> 00:53:36,279
Speaker 1: four thousand studies. Then you win to it down to the

1005
00:53:36,280 --> 00:53:38,799
Speaker 1: ones that are really just all trying to measure the

1006
00:53:38,840 --> 00:53:41,040
Speaker 1: same thing, so you can add them up. There was

1007
00:53:41,080 --> 00:53:44,759
Speaker 1: something like three hundred and seventy estimates of lambda, which

1008
00:53:44,800 --> 00:53:46,640
Speaker 1: is the Greek symbol that means the ratio of the

1009
00:53:46,680 --> 00:53:50,319
Speaker 1: disutility of loss to gain. And as you mentioned, two

1010
00:53:50,440 --> 00:53:52,080
Speaker 1: is sort of a we think it's a little bit

1011
00:53:52,080 --> 00:53:55,920
Speaker 1: smaller like one point sabin, but you know it's comparable. Yeah,

1012
00:53:55,960 --> 00:53:58,640
Speaker 1: it's comparable, and it's not one, which which would be

1013
00:53:58,680 --> 00:54:00,719
Speaker 1: the case in which you're not just finguishing loss and

1014
00:54:00,760 --> 00:54:04,800
Speaker 1: gain at all. You know, they're just like one scale.

1015
00:54:05,239 --> 00:54:08,640
Speaker 1: So the evidence is pretty good. Some other fun facts

1016
00:54:08,640 --> 00:54:10,960
Speaker 1: about loss of version, which is you might think that

1017
00:54:11,040 --> 00:54:14,840
Speaker 1: loss of version is is some kind of handicap, but

1018
00:54:14,960 --> 00:54:18,279
Speaker 1: actually we published a paper with two people who have

1019
00:54:18,360 --> 00:54:22,400
Speaker 1: brain damage and bilateral amigdala, which means neither part of

1020
00:54:22,440 --> 00:54:25,360
Speaker 1: the amgdala can compensate for the other. There's a very

1021
00:54:25,440 --> 00:54:27,759
Speaker 1: unusual disease. It comes from a or a bag via

1022
00:54:27,880 --> 00:54:30,600
Speaker 1: the disease, and they basically the amigdala is kind of

1023
00:54:30,640 --> 00:54:34,920
Speaker 1: like calcified, so it's it's there, but it's like deep freeze,

1024
00:54:34,960 --> 00:54:36,000
Speaker 1: you know, just so work.

1025
00:54:36,520 --> 00:54:40,920
Speaker 2: These people lose the ability to have these emotional responses

1026
00:54:41,080 --> 00:54:42,480
Speaker 2: to stimulus.

1027
00:54:41,840 --> 00:54:45,520
Speaker 1: Correct correct, and a lot has been known about because

1028
00:54:45,520 --> 00:54:48,400
Speaker 1: they've been studied. One of my colleagues, Ralphedelps, has studied

1029
00:54:48,880 --> 00:54:50,920
Speaker 1: several of them for years and they you know, they

1030
00:54:50,920 --> 00:54:52,680
Speaker 1: come back every so often and do a different.

1031
00:54:52,520 --> 00:54:55,520
Speaker 2: Kind of task and let me guess, they're pretty good traders.

1032
00:54:55,880 --> 00:54:59,680
Speaker 1: Generally, they're in disability because, uh, the amygdala damage is

1033
00:54:59,800 --> 00:55:02,839
Speaker 1: not to make they basically take too much risk in

1034
00:55:02,920 --> 00:55:04,160
Speaker 1: a lot of areas of life.

1035
00:55:05,360 --> 00:55:07,960
Speaker 2: So they're risk embracing, not risk averse at all.

1036
00:55:08,040 --> 00:55:11,480
Speaker 1: So the idea that that risk and fear are there

1037
00:55:11,560 --> 00:55:15,000
Speaker 1: to kind of protect you applies to them. Like when

1038
00:55:15,000 --> 00:55:17,719
Speaker 1: you remove that, like one of the patients, Sam makes

1039
00:55:17,719 --> 00:55:18,800
Speaker 1: a lot of poor choices.

1040
00:55:19,920 --> 00:55:20,840
Speaker 2: Give us examples.

1041
00:55:21,120 --> 00:55:23,520
Speaker 1: Well, this example I recall, I hope I'm not getting that.

1042
00:55:23,880 --> 00:55:26,680
Speaker 1: My memory is not mangling it too badly? Is She

1043
00:55:26,840 --> 00:55:29,600
Speaker 1: went on some kind of a date and the person

1044
00:55:29,719 --> 00:55:33,440
Speaker 1: was very sexually aggressive, and she ended up okay, And

1045
00:55:33,520 --> 00:55:35,160
Speaker 1: then somebody said, well, would you want to go out

1046
00:55:35,160 --> 00:55:37,000
Speaker 1: with that person again? She said, yeah, yeah, it was fine,

1047
00:55:37,120 --> 00:55:40,160
Speaker 1: it was fun. You know, she just didn't have this trauma.

1048
00:55:40,880 --> 00:55:44,000
Speaker 1: The amial was not processing. This is really bad. Run away,

1049
00:55:44,200 --> 00:55:45,600
Speaker 1: run away, avoid avoid.

1050
00:55:46,040 --> 00:55:51,200
Speaker 2: So how does this manifest itself amongst investors making risk

1051
00:55:51,520 --> 00:55:58,640
Speaker 2: decisions If their ability to process threats process fear is

1052
00:55:58,719 --> 00:56:01,360
Speaker 2: in present, what happens with those sort of decisions.

1053
00:56:01,440 --> 00:56:04,160
Speaker 1: Well, so for these two patients with imigal damage, they

1054
00:56:04,200 --> 00:56:05,320
Speaker 1: have no loss of version.

1055
00:56:05,520 --> 00:56:10,160
Speaker 2: None whatsoever, And so aggressive traders and investors, Well, so.

1056
00:56:10,520 --> 00:56:12,279
Speaker 1: Yeah, so the way we measures we give them these

1057
00:56:12,360 --> 00:56:15,359
Speaker 1: financial simple financial risks, like you could win. Most people

1058
00:56:15,400 --> 00:56:18,000
Speaker 1: if you say you could win ten, but you might

1059
00:56:18,120 --> 00:56:20,320
Speaker 1: lose eight or might lose seven, they're kind of just

1060
00:56:20,440 --> 00:56:23,080
Speaker 1: indifferent because a loss of seven and a gainer ten.

1061
00:56:23,040 --> 00:56:24,440
Speaker 2: Or you know, if I could, if I could do

1062
00:56:24,600 --> 00:56:27,200
Speaker 2: that on a billion dollars, I would, you know, I'd

1063
00:56:27,239 --> 00:56:27,759
Speaker 2: love to do that.

1064
00:56:28,480 --> 00:56:32,719
Speaker 1: But these two so damage the amgala. No more loss

1065
00:56:32,719 --> 00:56:36,560
Speaker 1: of version. So that's partly a reminder that be careful

1066
00:56:36,560 --> 00:56:39,239
Speaker 1: what you wish for, right, right, Like you.

1067
00:56:39,239 --> 00:56:42,640
Speaker 2: Don't want to react emotionally to everything correct right. The

1068
00:56:43,080 --> 00:56:45,719
Speaker 2: reason it's so hard to do, what Warren Buffett says,

1069
00:56:46,320 --> 00:56:49,520
Speaker 2: is when everybody's clamoring to buy, you get most people

1070
00:56:49,640 --> 00:56:54,120
Speaker 2: get caught up in that enthusiasm where we're social primates,

1071
00:56:54,160 --> 00:56:57,759
Speaker 2: and when the group is screaming bye bye bye, it's

1072
00:56:57,920 --> 00:57:00,359
Speaker 2: very hard to go with the other direction. And then

1073
00:57:00,640 --> 00:57:04,160
Speaker 2: at the bottom, when everybody is selling, the fear is

1074
00:57:04,239 --> 00:57:05,240
Speaker 2: of the alcohols.

1075
00:57:05,640 --> 00:57:09,600
Speaker 1: The fear of school was contagious very much. So right, yeah, yeah, yeah.

1076
00:57:09,760 --> 00:57:13,440
Speaker 2: So you lose that risk aversion. Do you have the

1077
00:57:13,560 --> 00:57:17,400
Speaker 2: ability to just go opposite the crowd because you don't care?

1078
00:57:17,880 --> 00:57:21,400
Speaker 1: It could be I mean, I've I have a feeling

1079
00:57:21,760 --> 00:57:25,880
Speaker 1: successful traders is it's not that they're not loss of verse,

1080
00:57:25,920 --> 00:57:29,720
Speaker 1: but they managed to inhibit it somehow. Or we did

1081
00:57:29,760 --> 00:57:31,600
Speaker 1: a such study in this but it's I don't think

1082
00:57:31,640 --> 00:57:34,200
Speaker 1: the details are all interesting for your readers, but or

1083
00:57:34,240 --> 00:57:36,440
Speaker 1: they're able to do what we call bracketing or kind

1084
00:57:36,480 --> 00:57:39,800
Speaker 1: of portfolio view, which is to say, you have bad

1085
00:57:39,880 --> 00:57:41,680
Speaker 1: days and good days and at the end it's my

1086
00:57:41,840 --> 00:57:43,840
Speaker 1: you know, it's my p and L at the end

1087
00:57:43,880 --> 00:57:45,200
Speaker 1: of the month or at the end of the year

1088
00:57:45,280 --> 00:57:48,160
Speaker 1: or the other quarter, and managed to kind of shrug

1089
00:57:48,240 --> 00:57:51,520
Speaker 1: off a loss. Now, I don't think that's that easy

1090
00:57:51,600 --> 00:57:54,800
Speaker 1: to do if you have intact amygdala right right, So

1091
00:57:54,880 --> 00:57:58,040
Speaker 1: it's it's almost it. It leads into another interesting topic

1092
00:57:58,040 --> 00:58:01,120
Speaker 1: which we've studied a little bit called emotion regular which

1093
00:58:01,200 --> 00:58:03,240
Speaker 1: is the fact that a lot of our emotions are

1094
00:58:03,240 --> 00:58:05,920
Speaker 1: sort of involuntary. You know, if there's a loud boom,

1095
00:58:06,000 --> 00:58:08,400
Speaker 1: you and I are both going to have this fear reaction.

1096
00:58:08,560 --> 00:58:12,320
Speaker 1: You know, haro, stand up will freeze. But you can

1097
00:58:12,360 --> 00:58:15,400
Speaker 1: also learn to regulate emotions. I mean kids are learning

1098
00:58:15,480 --> 00:58:18,400
Speaker 1: that when they learn to, you know, not be too

1099
00:58:18,440 --> 00:58:21,560
Speaker 1: afraid on the first day of school. As people get older,

1100
00:58:21,600 --> 00:58:25,720
Speaker 1: they learn to regulate emotions it's a pretty important skill.

1101
00:58:25,920 --> 00:58:29,600
Speaker 1: And so I think successful trading is probably some kind

1102
00:58:29,600 --> 00:58:33,080
Speaker 1: of cocktail of either a little less natural loss of

1103
00:58:33,160 --> 00:58:35,600
Speaker 1: version but not too little, right, because you don't want

1104
00:58:35,600 --> 00:58:38,040
Speaker 1: to like going crazy. You don't want them to be

1105
00:58:38,080 --> 00:58:40,439
Speaker 1: immune to loss, just like you don't want your hand

1106
00:58:40,520 --> 00:58:42,880
Speaker 1: to be immune to pain, right, because you're going to

1107
00:58:42,960 --> 00:58:46,800
Speaker 1: lean on a hot stoves one day and not notice

1108
00:58:46,800 --> 00:58:50,680
Speaker 1: that your hand is on fire. Right. So you a

1109
00:58:50,760 --> 00:58:53,280
Speaker 1: good trader probably has a little less natural loss of version,

1110
00:58:53,320 --> 00:58:57,200
Speaker 1: and then a really good ability to emotionally regulate, you know,

1111
00:58:57,280 --> 00:59:00,920
Speaker 1: when too much loss is acceptable or getting you into trouble.

1112
00:59:01,080 --> 00:59:06,400
Speaker 2: So the emotional regulation aspect is really interesting. I'm going

1113
00:59:06,480 --> 00:59:09,680
Speaker 2: to push you a little outside of your normal I

1114
00:59:09,800 --> 00:59:13,920
Speaker 2: think of your normal research area. One of the interesting

1115
00:59:14,160 --> 00:59:18,959
Speaker 2: comments that have come up when discussing who's a great

1116
00:59:19,240 --> 00:59:21,920
Speaker 2: fund manager, who's a great trader? Who are these folks

1117
00:59:22,240 --> 00:59:26,320
Speaker 2: that have put together these really impressive track records? A

1118
00:59:26,560 --> 00:59:29,840
Speaker 2: surprising number of neuroatypical folks.

1119
00:59:30,000 --> 00:59:30,439
Speaker 1: Oh yeah.

1120
00:59:30,760 --> 00:59:33,440
Speaker 2: The reason I asked you this is it seems like

1121
00:59:33,680 --> 00:59:35,680
Speaker 2: not only is there a little bit of ability to

1122
00:59:35,920 --> 00:59:40,080
Speaker 2: manage the emotions, but there's that ability to step outside

1123
00:59:40,080 --> 00:59:42,400
Speaker 2: of the crowd and say, I don't care what the

1124
00:59:42,480 --> 00:59:44,040
Speaker 2: rest of the primates are doing.

1125
00:59:44,680 --> 00:59:44,880
Speaker 1: Here.

1126
00:59:45,440 --> 00:59:48,880
Speaker 2: In March two thousand and nine, stocks look really attractive,

1127
00:59:48,960 --> 00:59:51,200
Speaker 2: and I want to be a buyer even though everybody

1128
00:59:51,200 --> 00:59:54,200
Speaker 2: else is selling. Is there an aspect of that to

1129
00:59:54,440 --> 00:59:55,520
Speaker 2: those sorts of ya.

1130
00:59:56,000 --> 00:59:58,480
Speaker 1: That's a fantastic topic. In fact, it is close to something.

1131
00:59:58,680 --> 01:00:00,720
Speaker 2: Oh, it is all right, good, I've been thinking about.

1132
01:00:00,760 --> 01:00:04,040
Speaker 1: So one thing is I was going to mention from before.

1133
01:00:04,160 --> 01:00:06,600
Speaker 1: So one of the striking things. I was working on

1134
01:00:06,680 --> 01:00:09,640
Speaker 1: an economics book and I was reading a lot of

1135
01:00:09,680 --> 01:00:13,520
Speaker 1: papers on social conformity. It turns out that almost every

1136
01:00:13,680 --> 01:00:18,240
Speaker 1: study finds the typical paradigm is something very stylized and simple, like,

1137
01:00:18,920 --> 01:00:21,680
Speaker 1: you know, you see a face and three other people

1138
01:00:21,760 --> 01:00:23,600
Speaker 1: see the same face, and you're asked to say is

1139
01:00:23,640 --> 01:00:26,880
Speaker 1: this person friendly or unfriendly? And in the conformity case,

1140
01:00:26,960 --> 01:00:30,200
Speaker 1: the other three people say friendly, and some other subject

1141
01:00:30,240 --> 01:00:34,480
Speaker 1: the other three see unfriendly, and people there seems to

1142
01:00:34,480 --> 01:00:37,840
Speaker 1: be a reward activity when you conform to the group.

1143
01:00:38,960 --> 01:00:42,400
Speaker 1: And these are not we're not super stress testing, so

1144
01:00:42,480 --> 01:00:45,280
Speaker 1: we're not quite something like you know, you're in the

1145
01:00:45,680 --> 01:00:48,360
Speaker 1: depth of a crash two thousand and eight crash and

1146
01:00:48,520 --> 01:00:51,800
Speaker 1: everyone's selling, and you know, ethically, it's hard for us

1147
01:00:51,840 --> 01:00:54,880
Speaker 1: to generate that dramatic an event in the lab. But

1148
01:00:55,520 --> 01:00:57,800
Speaker 1: even for these mild effects, and a lot of these people,

1149
01:00:57,840 --> 01:01:00,280
Speaker 1: if you ask them, do you follow the craft, they

1150
01:01:00,280 --> 01:01:01,520
Speaker 1: would say no, no, no, I kind of go in

1151
01:01:01,560 --> 01:01:03,040
Speaker 1: my own way. Like if a bunch of people said

1152
01:01:03,080 --> 01:01:05,640
Speaker 1: someone is friendly and you weren't sure, if you thought

1153
01:01:05,640 --> 01:01:07,720
Speaker 1: they weren't friendly, would you disagree? H Yeah, yeah, I

1154
01:01:07,880 --> 01:01:10,880
Speaker 1: wouldn't bother me. But study after study, the study shows

1155
01:01:10,960 --> 01:01:15,400
Speaker 1: there's generally reward value from conformity, which is essentially just

1156
01:01:15,480 --> 01:01:18,760
Speaker 1: the modern evidence for what you were talking about, which

1157
01:01:18,800 --> 01:01:20,560
Speaker 1: is that part of being a social animal right.

1158
01:01:20,680 --> 01:01:25,920
Speaker 2: The evolution of cooperation has has been very successful for us. Exactly,

1159
01:01:26,080 --> 01:01:27,680
Speaker 2: it started to fight the craft, It.

1160
01:01:27,720 --> 01:01:30,120
Speaker 1: Did his job, Yeah, exactly. Huh So I thought that

1161
01:01:30,200 --> 01:01:32,120
Speaker 1: was quite striking. Again, if you were if you wanted

1162
01:01:32,160 --> 01:01:36,160
Speaker 1: to study anti authoritarian personality, it might be a way

1163
01:01:36,200 --> 01:01:39,200
Speaker 1: to get into that that there be people who almost pathologically.

1164
01:01:39,480 --> 01:01:43,360
Speaker 1: But let's get back to your point about neurotypical people.

1165
01:01:43,520 --> 01:01:47,800
Speaker 1: So we're actually working at it beginning that a study

1166
01:01:47,840 --> 01:01:50,840
Speaker 1: on autism, so it's autism is called a spectrum disorder,

1167
01:01:50,880 --> 01:01:52,560
Speaker 1: which basically means it's not like you have it or

1168
01:01:52,600 --> 01:01:56,320
Speaker 1: you don't like schizophrenia. So you know, statistically it's it

1169
01:01:56,400 --> 01:01:57,600
Speaker 1: doesn't look like two humps.

1170
01:01:57,760 --> 01:01:59,320
Speaker 2: So you have a little, you could have some, you

1171
01:01:59,360 --> 01:02:00,600
Speaker 2: could have more, you can have a lot.

1172
01:02:00,800 --> 01:02:03,960
Speaker 1: Correct, correct, And there's often differences of symptoms like extreme

1173
01:02:04,000 --> 01:02:08,320
Speaker 1: autism often involves catatonia and severe language deficits and what

1174
01:02:08,440 --> 01:02:12,720
Speaker 1: have you. And so what people often think about Asperger syndrome,

1175
01:02:12,760 --> 01:02:15,280
Speaker 1: which is something that's called high functioning autism, right, which

1176
01:02:15,320 --> 01:02:18,200
Speaker 1: is basically you just just socially awkward and hard to

1177
01:02:18,320 --> 01:02:23,360
Speaker 1: understand what people do. But a lot of these pathologies

1178
01:02:24,000 --> 01:02:26,560
Speaker 1: or disorders, I should say pathology is not the right word.

1179
01:02:27,040 --> 01:02:29,640
Speaker 1: A lot of these disorders are accompanied by some enhancement.

1180
01:02:30,840 --> 01:02:34,400
Speaker 1: So for example, Asperger's patients have are more, they could

1181
01:02:34,440 --> 01:02:37,400
Speaker 1: have perfect pitch for a sound. They are better at

1182
01:02:37,440 --> 01:02:41,600
Speaker 1: ignoring some costs, which is a classic Bayer economics. You know,

1183
01:02:41,920 --> 01:02:43,920
Speaker 1: I spend so much on this dessert. You know, I

1184
01:02:44,000 --> 01:02:47,280
Speaker 1: came to New York. It is eighteen dollars for some flower.

1185
01:02:47,520 --> 01:02:51,160
Speaker 1: You know, I have to finish it, right.

1186
01:02:51,440 --> 01:02:53,440
Speaker 2: The autism, the money is spent whether you get the

1187
01:02:53,520 --> 01:02:54,200
Speaker 2: categories or not.

1188
01:02:54,320 --> 01:02:57,320
Speaker 1: So the autists have the right idea and.

1189
01:02:57,360 --> 01:02:59,280
Speaker 2: There is a sweet spot. I'm going to get you

1190
01:02:59,400 --> 01:03:03,080
Speaker 2: a list of the people who I know in this

1191
01:03:03,320 --> 01:03:08,000
Speaker 2: field who have that fantastic impressive numbers and have either

1192
01:03:08,760 --> 01:03:13,320
Speaker 2: stated there on the spectrum or it's kind of obvious hey.

1193
01:03:14,040 --> 01:03:16,840
Speaker 1: Yeah, yeah, yeah. You could look at film, video or

1194
01:03:17,080 --> 01:03:20,800
Speaker 1: written statements and you know, machine learned them and say,

1195
01:03:21,040 --> 01:03:22,440
Speaker 1: this person talks or looks.

1196
01:03:22,840 --> 01:03:27,000
Speaker 2: I'll ask on Twitter who's on the autism spectrum in

1197
01:03:27,040 --> 01:03:29,560
Speaker 2: the world of finance and has a good track record.

1198
01:03:29,640 --> 01:03:31,800
Speaker 2: But I have like two dozen names in my head.

1199
01:03:31,880 --> 01:03:34,560
Speaker 1: I'll give you a name. Unfortunately, he just died not

1200
01:03:34,640 --> 01:03:36,920
Speaker 1: too long ago, Charlie Munger. So of course Charlie a

1201
01:03:36,960 --> 01:03:38,160
Speaker 1: few times, right.

1202
01:03:38,240 --> 01:03:42,120
Speaker 2: And he doesn't strike me as very spectrumy.

1203
01:03:42,360 --> 01:03:45,520
Speaker 1: Well, but what one marker of autism is is like

1204
01:03:46,200 --> 01:03:49,920
Speaker 1: poor conversational turn taking, you know. And so when the

1205
01:03:50,000 --> 01:03:52,200
Speaker 1: times I met Charlie just twice and if you see

1206
01:03:52,240 --> 01:03:55,040
Speaker 1: him at the Berkshire Hathaway, I mean, he's amazing. I

1207
01:03:55,120 --> 01:03:57,520
Speaker 1: think it was like the Mark Twain of finance for sure,

1208
01:03:57,680 --> 01:04:00,640
Speaker 1: you know, because he was really witty and but also

1209
01:04:00,720 --> 01:04:04,040
Speaker 1: there's always like a really deep psychological insight in there.

1210
01:04:04,240 --> 01:04:06,240
Speaker 1: You know, it wasn't just funny. It was funny and

1211
01:04:06,320 --> 01:04:09,080
Speaker 1: true and often something other people didn't want to say.

1212
01:04:09,880 --> 01:04:12,200
Speaker 1: But when I met him, he was just like a

1213
01:04:12,280 --> 01:04:15,520
Speaker 1: freight train, and so you had to interrupt, and I

1214
01:04:15,640 --> 01:04:18,800
Speaker 1: realized the goal is to not have a conversation. You're

1215
01:04:18,840 --> 01:04:19,960
Speaker 1: just going to move the train.

1216
01:04:19,800 --> 01:04:22,080
Speaker 2: And different, just nudge him in different. Right.

1217
01:04:22,720 --> 01:04:24,360
Speaker 1: Well, you know that reminds me of X boom, and

1218
01:04:24,400 --> 01:04:25,760
Speaker 1: then he's often discussing X.

1219
01:04:26,000 --> 01:04:27,760
Speaker 2: I never realized that about him.

1220
01:04:27,800 --> 01:04:30,560
Speaker 1: So you're saying, that's my non clinical I am not

1221
01:04:30,640 --> 01:04:33,440
Speaker 1: a transition, but you know, disclaimer. Part of it is

1222
01:04:33,520 --> 01:04:36,000
Speaker 1: reflected and why he was successful. You know, he he

1223
01:04:36,080 --> 01:04:38,880
Speaker 1: saw himself as an average person who wasn't making the

1224
01:04:38,960 --> 01:04:41,800
Speaker 1: dumb mistakes other people make. But some of those dumb

1225
01:04:41,800 --> 01:04:44,040
Speaker 1: mistake people make, you know, he may have not made

1226
01:04:44,080 --> 01:04:46,800
Speaker 1: them because he doesn't get caught up in social conformity,

1227
01:04:47,280 --> 01:04:49,919
Speaker 1: or because he's very focused on he has good metacognition,

1228
01:04:50,160 --> 01:04:51,880
Speaker 1: like if I don't I don't buy a company I

1229
01:04:51,920 --> 01:04:56,400
Speaker 1: don't understand, right, you know, that's probably a good strategy.

1230
01:04:56,720 --> 01:04:59,520
Speaker 2: So I'm working on a new book. I'm almost done,

1231
01:04:59,600 --> 01:05:02,960
Speaker 2: and Munger is great. One of the two people I

1232
01:05:03,080 --> 01:05:06,360
Speaker 2: dedicate the book to and the quote of his that

1233
01:05:07,040 --> 01:05:11,040
Speaker 2: very much informs the the theme of the book is

1234
01:05:11,160 --> 01:05:15,080
Speaker 2: someone once asked him was Berkshire successful because Ewan and

1235
01:05:15,200 --> 01:05:17,840
Speaker 2: Warren are so much smarter than everybody else? And his

1236
01:05:18,000 --> 01:05:20,760
Speaker 2: response was, it's not that we're smarter than everybody else,

1237
01:05:21,200 --> 01:05:27,040
Speaker 2: we were just less stupid, which is such an insightful observation. Hey,

1238
01:05:27,320 --> 01:05:31,919
Speaker 2: just fewer Charlie Ellis, make less unforced errors, and you'll

1239
01:05:32,320 --> 01:05:35,800
Speaker 2: do better in tennis or investing than the guy trying

1240
01:05:35,840 --> 01:05:38,000
Speaker 2: to slam the ace, and most people are not going

1241
01:05:38,040 --> 01:05:40,880
Speaker 2: to get it in. Him and Munger had the two

1242
01:05:41,000 --> 01:05:44,920
Speaker 2: Charlie's had the same belief system, just be less stupid.

1243
01:05:45,240 --> 01:05:50,360
Speaker 2: It's really fascinating. So when you've interviewed Munger, what are

1244
01:05:50,400 --> 01:05:53,680
Speaker 2: some of the takeaways you've had from your conversations with him?

1245
01:05:55,200 --> 01:05:57,280
Speaker 1: One thing I remember was for so we went and

1246
01:05:57,360 --> 01:05:59,280
Speaker 1: looked at our neuroimaging center.

1247
01:06:00,040 --> 01:06:01,880
Speaker 2: Did you ever get him in a machine? No?

1248
01:06:02,720 --> 01:06:05,080
Speaker 1: I wish. I wish we had. He he may have

1249
01:06:05,120 --> 01:06:06,680
Speaker 1: gone for it too. He's you know, he's a pretty

1250
01:06:06,760 --> 01:06:12,320
Speaker 1: interesting person and I think very open minds, scientifically curious

1251
01:06:12,480 --> 01:06:16,680
Speaker 1: as well as in his financial life. He had gone

1252
01:06:16,720 --> 01:06:19,000
Speaker 1: to Celtic for a while. So he was we got

1253
01:06:19,040 --> 01:06:20,880
Speaker 1: to run into every so often. Of course, we're always

1254
01:06:20,960 --> 01:06:22,400
Speaker 1: people like that. They're always trying to get him to

1255
01:06:22,400 --> 01:06:24,400
Speaker 1: give money and or at least show.

1256
01:06:24,280 --> 01:06:26,440
Speaker 2: Up and give a speech something.

1257
01:06:26,680 --> 01:06:30,240
Speaker 1: Yeah, talk, and so so we showed in the brain scanner.

1258
01:06:30,520 --> 01:06:32,440
Speaker 1: He had a really interesting thought which I didn't quite

1259
01:06:32,440 --> 01:06:37,760
Speaker 1: appreciate till later, which was he said, what you guys

1260
01:06:37,760 --> 01:06:40,480
Speaker 1: should be doing is if you're trying to change behavior,

1261
01:06:40,560 --> 01:06:42,680
Speaker 1: like let's say you're trying to get somebody to vote,

1262
01:06:43,000 --> 01:06:46,520
Speaker 1: or to wear a mask or you know, quit smoking.

1263
01:06:46,600 --> 01:06:47,960
Speaker 1: Opioid's the really hard stuff.

1264
01:06:48,000 --> 01:06:48,120
Speaker 2: You know.

1265
01:06:48,200 --> 01:06:50,480
Speaker 1: Wait, unless he said, what you should really do is,

1266
01:06:50,600 --> 01:06:53,400
Speaker 1: rather than doing one little thing, you should go for

1267
01:06:53,480 --> 01:06:56,320
Speaker 1: a lollapalooza, you know, like basically try to add in

1268
01:06:56,480 --> 01:07:00,280
Speaker 1: six different things to get the biggest ability to get

1269
01:07:00,280 --> 01:07:01,120
Speaker 1: people to quit smoking.

1270
01:07:01,240 --> 01:07:02,080
Speaker 2: Let's say it makes sense.

1271
01:07:02,480 --> 01:07:04,440
Speaker 1: And so he was thinking as a practitioner like I

1272
01:07:04,560 --> 01:07:07,320
Speaker 1: want I'm going to know what's going to work, as

1273
01:07:07,400 --> 01:07:10,800
Speaker 1: scientists were often thinking piecemeal, like if we put six

1274
01:07:10,880 --> 01:07:13,480
Speaker 1: different things in and it works, we don't know which

1275
01:07:13,520 --> 01:07:16,040
Speaker 1: of the six is the active ingredient, but it.

1276
01:07:16,080 --> 01:07:18,960
Speaker 2: Could be a different combination for each different exactly.

1277
01:07:18,680 --> 01:07:21,560
Speaker 1: So exactly, but and so the reason I was thinking

1278
01:07:21,600 --> 01:07:24,440
Speaker 1: about that was nowadays, one of the fallouts or one

1279
01:07:24,480 --> 01:07:26,920
Speaker 1: of the products I should say, from Fallow it's definitely

1280
01:07:26,920 --> 01:07:29,520
Speaker 1: the wrong word. One of the products from Beata economics

1281
01:07:29,680 --> 01:07:32,680
Speaker 1: was this idea of a nudge that often because people

1282
01:07:32,680 --> 01:07:35,200
Speaker 1: are often sensitive to very subtle things like opt in

1283
01:07:35,360 --> 01:07:38,080
Speaker 1: versus opt out, you know there may be a low cost,

1284
01:07:38,640 --> 01:07:41,280
Speaker 1: light touchway to change behavior a little bit.

1285
01:07:41,840 --> 01:07:44,520
Speaker 2: Well just look at the four oh one k exactly,

1286
01:07:44,600 --> 01:07:51,720
Speaker 2: making the default go to just some specific investment as

1287
01:07:51,800 --> 01:07:56,120
Speaker 2: opposed to it just sits there in cash for god

1288
01:07:56,280 --> 01:07:59,400
Speaker 2: knows how long. Seems to have really had a big impa.

1289
01:07:59,320 --> 01:08:03,080
Speaker 1: Yes, exactly that that was definitely the poster child for

1290
01:08:03,240 --> 01:08:06,000
Speaker 1: the simplest nudge, and we kind of understand the psychology

1291
01:08:06,080 --> 01:08:08,320
Speaker 1: of it anyway. So now what a lot of people

1292
01:08:08,360 --> 01:08:11,040
Speaker 1: are thinking about nudge. This is exactly this lollapaloosa idea

1293
01:08:11,080 --> 01:08:14,000
Speaker 1: of Munger's, which is, if we want to get people

1294
01:08:14,040 --> 01:08:17,120
Speaker 1: to get out the vote, rather than try six different things,

1295
01:08:17,640 --> 01:08:22,000
Speaker 1: we should be trying like six combinations of three things. Statistically,

1296
01:08:22,080 --> 01:08:25,559
Speaker 1: it's messy because you'll never really end up knowing which

1297
01:08:25,600 --> 01:08:28,960
Speaker 1: of those is the active ingredient. But to just get results,

1298
01:08:29,439 --> 01:08:34,000
Speaker 1: that's useful information, it's useful formation. So the Nudge Enterprise,

1299
01:08:34,080 --> 01:08:36,240
Speaker 1: which I've been connected to a little bit, is moving

1300
01:08:36,320 --> 01:08:38,720
Speaker 1: somewhe in that direction that Munger mentioned many years ago.

1301
01:08:39,000 --> 01:08:41,880
Speaker 2: Huh. Really interesting? All right, I only have you for

1302
01:08:41,920 --> 01:08:44,280
Speaker 2: a limited amount of time, so let me jump to

1303
01:08:44,400 --> 01:08:47,479
Speaker 2: my favorite questions that I ask all of my guests,

1304
01:08:48,320 --> 01:08:51,760
Speaker 2: starting with what are you watching or listening to these days?

1305
01:08:51,840 --> 01:08:53,720
Speaker 2: What's keeping you entertained?

1306
01:08:54,320 --> 01:08:58,120
Speaker 1: So Katie Molkman's podcast Choiceology is one that I've been

1307
01:08:58,160 --> 01:08:59,920
Speaker 1: on that I think is quite good. It's basically the

1308
01:09:00,280 --> 01:09:03,880
Speaker 1: beaval economics podcasts. There are quite a few others, but

1309
01:09:03,960 --> 01:09:05,960
Speaker 1: Katie is a real expert on this and is a

1310
01:09:06,040 --> 01:09:07,280
Speaker 1: great interviewer and has.

1311
01:09:07,160 --> 01:09:11,519
Speaker 2: Had good guests choice Ology. Choice Ology tell us about

1312
01:09:11,640 --> 01:09:14,840
Speaker 2: your mentors who helped to shape your fascinating career.

1313
01:09:15,840 --> 01:09:18,400
Speaker 1: So two people who were on my thesis committee, Robin

1314
01:09:18,439 --> 01:09:22,519
Speaker 1: Hogarth and Hilly Einhorn were two and there's an interesting story.

1315
01:09:22,640 --> 01:09:33,360
Speaker 1: So Robin was Scottish, very verbal, every sentence started with howsoever. Therefore, notwithstanding,

1316
01:09:34,040 --> 01:09:37,400
Speaker 1: Hilly was a very blunt jew from Brooklyn, and it

1317
01:09:37,520 --> 01:09:40,400
Speaker 1: was the exact opposite so Hilly would mark up my

1318
01:09:40,560 --> 01:09:43,479
Speaker 1: thesis and put in all these fancy Hilly would rather

1319
01:09:43,520 --> 01:09:45,680
Speaker 1: would take out the whatsoevers and the howevers and that

1320
01:09:45,760 --> 01:09:48,679
Speaker 1: thereforece and he was like put in more like boom,

1321
01:09:49,080 --> 01:09:52,160
Speaker 1: like short sentences, no sema colons, but like he had

1322
01:09:52,240 --> 01:09:54,760
Speaker 1: one punctuation mark period. That's it right, Like you know,

1323
01:09:54,840 --> 01:09:57,400
Speaker 1: he like about a million periods at a store, and

1324
01:09:57,520 --> 01:09:59,840
Speaker 1: like I'm not likely to use those. And Robin was

1325
01:09:59,880 --> 01:10:02,160
Speaker 1: the way around, Oh, this really need to do summa colon,

1326
01:10:02,240 --> 01:10:04,519
Speaker 1: you know, let's plump this. And at one point I

1327
01:10:04,600 --> 01:10:06,479
Speaker 1: was going back and forth, you know, near the completion

1328
01:10:06,600 --> 01:10:09,120
Speaker 1: of my thesis with the two of them were co advisors,

1329
01:10:10,200 --> 01:10:13,000
Speaker 1: and I got so frustrated, and I said, how should

1330
01:10:13,000 --> 01:10:16,360
Speaker 1: I write this? And we had this this kind of

1331
01:10:16,479 --> 01:10:20,240
Speaker 1: like grasshopper moment of it's your thesis, you figure out

1332
01:10:20,240 --> 01:10:22,400
Speaker 1: how you want to write it. And I realized they

1333
01:10:22,400 --> 01:10:24,360
Speaker 1: were kind of waiting for me to find my voice,

1334
01:10:24,479 --> 01:10:27,559
Speaker 1: like they say in writing, you know, like and one

1335
01:10:27,600 --> 01:10:30,559
Speaker 1: of the love tables and then the other love graphs.

1336
01:10:31,040 --> 01:10:33,280
Speaker 1: So the drafts of my thesis was the table and

1337
01:10:33,320 --> 01:10:35,559
Speaker 1: a graph were exactly the same thing. And I had

1338
01:10:35,600 --> 01:10:37,679
Speaker 1: to decide was I a graph person or a table

1339
01:10:37,720 --> 01:10:40,600
Speaker 1: person or was I kind of like bilingual? So I

1340
01:10:40,680 --> 01:10:43,880
Speaker 1: basically became kind of bilingual in terms of how I

1341
01:10:43,960 --> 01:10:46,320
Speaker 1: was thinking. It's night. That was very helpful. The other

1342
01:10:46,360 --> 01:10:50,120
Speaker 1: person probably is Dick Taylor, because he he's a very

1343
01:10:50,160 --> 01:10:52,920
Speaker 1: good writer. He did exactly what so many academics aspire

1344
01:10:53,000 --> 01:10:55,840
Speaker 1: to and we always ask for more of, which is

1345
01:10:55,880 --> 01:10:59,519
Speaker 1: to write a small number of extremely high quality papers.

1346
01:11:00,080 --> 01:11:03,280
Speaker 1: It's very unusual because for career reasons and stuff, you

1347
01:11:03,360 --> 01:11:07,000
Speaker 1: have to get tenure and right, and Dick just couldn't

1348
01:11:07,000 --> 01:11:09,000
Speaker 1: really write a bad paper. I don't write as many

1349
01:11:09,640 --> 01:11:11,760
Speaker 1: great papers as him, and I as a result, I

1350
01:11:11,840 --> 01:11:14,599
Speaker 1: write too many okay papers. But that's something I think

1351
01:11:14,680 --> 01:11:15,479
Speaker 1: is useful for everyone.

1352
01:11:15,760 --> 01:11:18,479
Speaker 2: He's one of my favorite people in the world. I

1353
01:11:18,600 --> 01:11:20,719
Speaker 2: got to interview I don't know half a dozen times,

1354
01:11:21,439 --> 01:11:24,720
Speaker 2: only once since he won the Nobel Prize, but I

1355
01:11:24,840 --> 01:11:29,840
Speaker 2: always find him so informative and entertaining, and I just

1356
01:11:30,080 --> 01:11:33,400
Speaker 2: loved his response to winning the prize. What are you

1357
01:11:33,400 --> 01:11:35,600
Speaker 2: gonna do with the money? His answer is, I'm going

1358
01:11:35,680 --> 01:11:38,720
Speaker 2: to spend it as irrationally as I possibly can, just

1359
01:11:38,840 --> 01:11:42,000
Speaker 2: so so him he enjoys life. He very much does

1360
01:11:42,560 --> 01:11:47,040
Speaker 2: he's just also a fascinating, fascinating, charming guy. Let's talk

1361
01:11:47,040 --> 01:11:48,920
Speaker 2: about books. What are some of your favorites. What are

1362
01:11:48,960 --> 01:11:49,720
Speaker 2: you reading right now?

1363
01:11:50,560 --> 01:11:53,479
Speaker 1: I am reading Emma Klein a book called The Guests,

1364
01:11:54,760 --> 01:11:58,599
Speaker 1: especially for neworlcus in your audience. It's about a very grifty,

1365
01:11:58,800 --> 01:12:02,439
Speaker 1: sketchy woman who goes to the Hamptons and kind of

1366
01:12:02,560 --> 01:12:05,680
Speaker 1: cons her way around the Hamptons. It's really it's almost.

1367
01:12:05,400 --> 01:12:07,680
Speaker 2: Like a very didn't we have kind of a real

1368
01:12:07,760 --> 01:12:09,680
Speaker 2: life thing like that happening a year?

1369
01:12:09,800 --> 01:12:13,040
Speaker 1: Yes, exactly. It may be loosely inspired by Anna deel

1370
01:12:13,120 --> 01:12:17,479
Speaker 1: Vi in Manhattan or some similar cases. It's basically almost

1371
01:12:17,560 --> 01:12:21,680
Speaker 1: like a nineteenth century novel about class, because she's very

1372
01:12:21,760 --> 01:12:24,000
Speaker 1: conscious of not belonging in the Hamptons, but she's very

1373
01:12:24,040 --> 01:12:27,200
Speaker 1: beautiful and kind of charming in this sort of man

1374
01:12:27,280 --> 01:12:30,800
Speaker 1: eater femvatal way. And I'm almost done with that. It's

1375
01:12:30,840 --> 01:12:34,559
Speaker 1: really delicious. The other thing, I love movies and books

1376
01:12:34,600 --> 01:12:38,120
Speaker 1: about capers and heists and grift, which includes Emma Klein

1377
01:12:38,640 --> 01:12:41,360
Speaker 1: The Guest. So I'm reading these books by Jim Swain,

1378
01:12:41,520 --> 01:12:44,920
Speaker 1: who's not known. I got onto him. Lee Child, who

1379
01:12:45,320 --> 01:12:45,559
Speaker 1: who I.

1380
01:12:45,600 --> 01:12:48,479
Speaker 2: Love life reads all of his books plow through all

1381
01:12:48,520 --> 01:12:51,040
Speaker 2: of it exactly. Yeah, and that did that include the

1382
01:12:51,200 --> 01:12:52,080
Speaker 2: Reacher series.

1383
01:12:52,240 --> 01:12:54,280
Speaker 1: The Reacher series, that's what he is most famous for,

1384
01:12:54,439 --> 01:12:57,040
Speaker 1: the Lee Child. But so Jim Swain was blurbed by

1385
01:12:57,120 --> 01:12:59,920
Speaker 1: Lee Child, saying, Jim Swain's the best at what he does,

1386
01:13:00,040 --> 01:13:02,840
Speaker 1: and what he does is he writes about a very

1387
01:13:02,920 --> 01:13:07,880
Speaker 1: sophisticated cheater in Las Vegas who cheats casinos, and it's

1388
01:13:08,360 --> 01:13:10,519
Speaker 1: you know, I'm going to use recycle this and you're

1389
01:13:11,800 --> 01:13:16,320
Speaker 1: very shortly for you. But basically there are procedurals about

1390
01:13:16,439 --> 01:13:19,240
Speaker 1: how to cheat a casino, but in the end if

1391
01:13:19,280 --> 01:13:23,080
Speaker 1: you get caught. There's also this sort of socio psycho

1392
01:13:23,160 --> 01:13:25,960
Speaker 1: political thing of you know, if I make up a

1393
01:13:26,080 --> 01:13:28,920
Speaker 1: story about why something happened, like if there's a murder

1394
01:13:29,000 --> 01:13:31,559
Speaker 1: in a casino and I make up a story about

1395
01:13:31,600 --> 01:13:35,120
Speaker 1: it that helps them act like the murder was freakish

1396
01:13:35,160 --> 01:13:38,719
Speaker 1: and won't drive away customers. I'm actually delivering a gift

1397
01:13:38,760 --> 01:13:40,600
Speaker 1: to them, and they're going to trade off. They're not

1398
01:13:40,640 --> 01:13:42,080
Speaker 1: going to send me to jail. But if I give

1399
01:13:42,120 --> 01:13:44,040
Speaker 1: them this gift. So there's a lot of layers of

1400
01:13:44,280 --> 01:13:47,240
Speaker 1: This is not Dostoevski, It's not brilliant. This is not

1401
01:13:48,280 --> 01:13:52,000
Speaker 1: sum but for me, there's a lot of like psychology,

1402
01:13:52,080 --> 01:13:54,360
Speaker 1: and you know, in a way, it's a game theory.

1403
01:13:54,400 --> 01:13:58,280
Speaker 1: What if there's an arms race between the Vegas Gaming

1404
01:13:58,280 --> 01:14:01,920
Speaker 1: Commission and each of the individual casinos who are very sophisticated.

1405
01:14:01,960 --> 01:14:03,760
Speaker 1: They hire a lot of ex cheats, you know, to

1406
01:14:04,680 --> 01:14:07,080
Speaker 1: tell them what to look for, and then these cheaters

1407
01:14:07,120 --> 01:14:09,200
Speaker 1: who know you know, so truly, there's arms series of

1408
01:14:09,200 --> 01:14:10,920
Speaker 1: who's gonna win? I found those really interesting.

1409
01:14:11,760 --> 01:14:18,040
Speaker 2: If you like books on griffs and cheats and corruption,

1410
01:14:18,760 --> 01:14:22,240
Speaker 2: I'm gonna recommend pretty much anything he's written. I've been

1411
01:14:22,240 --> 01:14:26,160
Speaker 2: a fan of his for years. Carlhassen was a reorder

1412
01:14:26,760 --> 01:14:30,120
Speaker 2: for the Miami Herald Prime Reporter and then just one

1413
01:14:30,240 --> 01:14:34,280
Speaker 2: after another, these series of novels and his one of

1414
01:14:34,320 --> 01:14:37,040
Speaker 2: his more recent books is now a TV series on

1415
01:14:37,800 --> 01:14:42,720
Speaker 2: Apple Plus Bad Monkey, but all of his books it's

1416
01:14:42,800 --> 01:14:45,200
Speaker 2: Bad Monkey in the I think the sequel is called

1417
01:14:45,360 --> 01:14:48,360
Speaker 2: Razor Girl. But all his books take place in Florida.

1418
01:14:49,080 --> 01:14:52,559
Speaker 2: Everybody's corrupt. The police are corrupt, the building inspectors are corrupt,

1419
01:14:52,640 --> 01:14:56,000
Speaker 2: the politicians are corrupt, and there's always one or two

1420
01:14:56,080 --> 01:14:58,559
Speaker 2: good people in the heart of the story and it's

1421
01:14:58,600 --> 01:15:02,680
Speaker 2: how do they navigate? It's just endless sea of treachery

1422
01:15:02,800 --> 01:15:07,280
Speaker 2: and corruption. And he's just a delightful, entertaining writer. If

1423
01:15:07,280 --> 01:15:10,320
Speaker 2: you you could randomly pick any of his books and

1424
01:15:10,600 --> 01:15:13,040
Speaker 2: they're just all they're great beach reads.

1425
01:15:13,560 --> 01:15:15,519
Speaker 1: You know. Le Me also mentioned The Wire because I

1426
01:15:15,560 --> 01:15:20,160
Speaker 1: grew up in Baltimore County and a series. Yes, and

1427
01:15:20,520 --> 01:15:23,599
Speaker 1: David Simon's book The Corner is a kind of a precursor.

1428
01:15:23,640 --> 01:15:25,240
Speaker 1: I mean, he's a very interesting person. He was a

1429
01:15:25,320 --> 01:15:29,880
Speaker 1: reporter and I think he may have been Baltimore, Britimore.

1430
01:15:30,360 --> 01:15:33,960
Speaker 1: And The Corner is like this beautiful I think it

1431
01:15:34,040 --> 01:15:36,800
Speaker 1: was a precursor to the Wire. But it's basically about

1432
01:15:36,800 --> 01:15:39,479
Speaker 1: a corner in West Baltimore everyone buys drugs, and it's

1433
01:15:39,479 --> 01:15:41,920
Speaker 1: about drug addiction and all the things that's surrounded. So

1434
01:15:42,000 --> 01:15:44,080
Speaker 1: as somebody who you know, one of the things we

1435
01:15:44,120 --> 01:15:47,200
Speaker 1: study in behavioral economics is habits and addictions, and you know,

1436
01:15:47,320 --> 01:15:50,559
Speaker 1: and the neuroscience, of course is fascinating along the way,

1437
01:15:51,040 --> 01:15:53,320
Speaker 1: and that one is great. And The Wire having grown

1438
01:15:53,400 --> 01:15:56,040
Speaker 1: up in Baltimore County, which is not Baltimore City, The

1439
01:15:56,120 --> 01:15:58,360
Speaker 1: Wire is almost like a documentary and it has all

1440
01:15:58,400 --> 01:16:00,960
Speaker 1: this Baltimore stuff, as well as all my accents where

1441
01:16:00,960 --> 01:16:03,960
Speaker 1: you have people are talking about talking like this, and

1442
01:16:04,120 --> 01:16:07,880
Speaker 1: it has Tommy Garcetti is this political character who's sort

1443
01:16:07,880 --> 01:16:12,320
Speaker 1: of inspired by Tommy Delsandro, whose daughter is Nancy PELUSI.

1444
01:16:12,800 --> 01:16:16,639
Speaker 2: Oh really, that's amazing. I found the series the Wire.

1445
01:16:17,280 --> 01:16:21,320
Speaker 2: It's a tough watch. It's a great show. It's brutal. Yeah,

1446
01:16:21,400 --> 01:16:24,320
Speaker 2: gritty is mild. I mean some of the stuff that

1447
01:16:24,439 --> 01:16:26,120
Speaker 2: goes on, and the show is.

1448
01:16:26,280 --> 01:16:28,080
Speaker 1: Just like, yeah, there's a famous scene with the nail

1449
01:16:28,120 --> 01:16:33,320
Speaker 1: gun you're which if your listeners have the stomach, that's

1450
01:16:33,360 --> 01:16:34,080
Speaker 1: pretty classic.

1451
01:16:34,640 --> 01:16:39,240
Speaker 2: Similar in the Jack Reacher series, there's a really something

1452
01:16:39,400 --> 01:16:42,439
Speaker 2: not that far off. Yeah, they toned it down for television,

1453
01:16:42,520 --> 01:16:45,559
Speaker 2: but the book is is really brutal. All Right, we're

1454
01:16:45,640 --> 01:16:48,559
Speaker 2: up to our final two questions. What sort of advice

1455
01:16:48,640 --> 01:16:51,600
Speaker 2: would you give to a college grad interested in a

1456
01:16:51,680 --> 01:16:56,840
Speaker 2: career in filling the blank neuroeconomics, behavioral finance, or even

1457
01:16:57,120 --> 01:16:58,280
Speaker 2: just investing.

1458
01:16:58,479 --> 01:17:02,400
Speaker 1: For somebody who say it doesn't want to get a PhD.

1459
01:17:02,680 --> 01:17:05,080
Speaker 1: That's a different track and probably of less interest. And

1460
01:17:05,120 --> 01:17:07,600
Speaker 1: there's you can get a lot of guests advice on

1461
01:17:07,720 --> 01:17:10,759
Speaker 1: how to do that. I would study not just finance

1462
01:17:10,920 --> 01:17:16,720
Speaker 1: like straight asset pricing and derivatives, but also behavioral economics

1463
01:17:16,880 --> 01:17:19,320
Speaker 1: game theory, I think, because even though game theory is

1464
01:17:19,479 --> 01:17:22,600
Speaker 1: usually like two players or small numbers of players, it

1465
01:17:22,680 --> 01:17:25,720
Speaker 1: really sharpens the logic of you know, when do I

1466
01:17:25,840 --> 01:17:28,760
Speaker 1: know something another person doesn't know? And do I know

1467
01:17:28,920 --> 01:17:30,760
Speaker 1: that they don't know it? You know, you have to

1468
01:17:30,840 --> 01:17:34,000
Speaker 1: really relentlessly think about the math underlying that. And then

1469
01:17:34,000 --> 01:17:37,360
Speaker 1: there's a lot of experimental and real world data. One

1470
01:17:37,400 --> 01:17:39,920
Speaker 1: of my I just got a text from our students

1471
01:17:40,000 --> 01:17:42,439
Speaker 1: this term, and there's a lot of data from sports

1472
01:17:42,720 --> 01:17:48,080
Speaker 1: about whether sports activities are like equilibrium responses to other players,

1473
01:17:49,320 --> 01:17:51,559
Speaker 1: So you can actually there's there's a lot of sources

1474
01:17:51,560 --> 01:17:54,360
Speaker 1: of data besides just say the lab experiments. I talk

1475
01:17:54,360 --> 01:17:56,400
Speaker 1: about my book from two thousand and three, Sneaking the

1476
01:17:56,520 --> 01:17:59,960
Speaker 1: Plug in Cognitive science is something I would study too.

1477
01:18:00,280 --> 01:18:05,160
Speaker 1: So cognitive science is a modern brand of cognitive psych

1478
01:18:05,240 --> 01:18:07,360
Speaker 1: that has more math in it, and a lot of

1479
01:18:07,439 --> 01:18:10,080
Speaker 1: it actually goes back to something we spoke about, like

1480
01:18:10,680 --> 01:18:12,960
Speaker 1: a mismatch. But they are quite interested in what they

1481
01:18:13,000 --> 01:18:16,200
Speaker 1: call resource rationality, which means a lot of the mistakes

1482
01:18:16,240 --> 01:18:19,599
Speaker 1: people might make, like anchoring on one number and being

1483
01:18:19,680 --> 01:18:23,759
Speaker 1: influenced by that. A famous anchoring adjustment heuristic may actually

1484
01:18:23,840 --> 01:18:26,240
Speaker 1: be rational if you only have so much working memory,

1485
01:18:26,400 --> 01:18:29,800
Speaker 1: or you're under time pressure, or you're tired. It's also

1486
01:18:29,840 --> 01:18:33,799
Speaker 1: closely related to the way economists would think about mistakes,

1487
01:18:33,840 --> 01:18:36,280
Speaker 1: which is they may be optimal given some constraint, like

1488
01:18:36,320 --> 01:18:39,240
Speaker 1: what is that constraint and can we test that experimentally?

1489
01:18:39,840 --> 01:18:41,200
Speaker 1: So I think there's a lot of stuff you could

1490
01:18:41,280 --> 01:18:43,600
Speaker 1: learn there that will help you think about markets. The

1491
01:18:43,720 --> 01:18:48,639
Speaker 1: other thing I would say is get experience thinking about markets,

1492
01:18:48,960 --> 01:18:52,040
Speaker 1: whether in turning or I'll tell you a story about

1493
01:18:52,040 --> 01:18:53,880
Speaker 1: what worked for me, which was when I was twelve

1494
01:18:53,960 --> 01:18:59,679
Speaker 1: years old in Cockeysville, Maryland, August, there was a one

1495
01:18:59,800 --> 01:19:04,160
Speaker 1: month month racing program at a small racetrack called Timonium, Maryland,

1496
01:19:05,080 --> 01:19:06,920
Speaker 1: and it was a five eighths of a mile tracks.

1497
01:19:06,960 --> 01:19:09,680
Speaker 1: It's like a you know, small I would go with

1498
01:19:09,840 --> 01:19:12,320
Speaker 1: my dad and a friend of his who was a stockbroker,

1499
01:19:12,960 --> 01:19:14,360
Speaker 1: and we would also go to the big tracks like

1500
01:19:14,400 --> 01:19:16,840
Speaker 1: Pimlico where the preak mistakes is. But if you go

1501
01:19:16,920 --> 01:19:20,479
Speaker 1: to Timonium, you get to see all the horses. There

1502
01:19:20,560 --> 01:19:23,120
Speaker 1: was so much interest. You learned so much about markets.

1503
01:19:24,520 --> 01:19:26,120
Speaker 1: Number one. It gives you, I think, a respect for

1504
01:19:26,240 --> 01:19:27,000
Speaker 1: market efficiency.

1505
01:19:27,400 --> 01:19:30,080
Speaker 2: Couse, the odds are actually not that bad. They were

1506
01:19:30,160 --> 01:19:32,200
Speaker 2: extremely pretty pretty dead on exactly.

1507
01:19:32,240 --> 01:19:34,080
Speaker 1: And so you see, you know, eight horses come out,

1508
01:19:34,160 --> 01:19:37,479
Speaker 1: they all look pretty similar. You know, the jockeys are

1509
01:19:37,520 --> 01:19:39,960
Speaker 1: all you know, the same size, and they're all pretty good.

1510
01:19:39,960 --> 01:19:42,759
Speaker 1: There's a lot of statistics you can see. But somehow

1511
01:19:42,800 --> 01:19:47,479
Speaker 1: the crowd has decided that number three is even money favorite,

1512
01:19:47,520 --> 01:19:49,040
Speaker 1: which is a fifty e d chance to win. A

1513
01:19:49,160 --> 01:19:51,519
Speaker 1: number six, who looks pretty good too, is like seventy

1514
01:19:51,600 --> 01:19:55,760
Speaker 1: to one, and they're mostly right. So you know, part

1515
01:19:55,800 --> 01:19:58,439
Speaker 1: of why I got into economics and psychology was thinking

1516
01:19:58,479 --> 01:20:01,280
Speaker 1: about episodes like that, how does the market put this

1517
01:20:01,400 --> 01:20:06,120
Speaker 1: information together? And are the mistakes? Like how do you

1518
01:20:06,200 --> 01:20:06,800
Speaker 1: beat the market?

1519
01:20:07,000 --> 01:20:09,799
Speaker 2: So Fama turns out to be more or less right about.

1520
01:20:09,560 --> 01:20:12,200
Speaker 1: You about twenty in Maryland. And there were other interesting

1521
01:20:12,320 --> 01:20:14,560
Speaker 1: lessons too, like so on the if you go with

1522
01:20:14,640 --> 01:20:16,320
Speaker 1: like around the third race. You know, I was I

1523
01:20:16,520 --> 01:20:19,800
Speaker 1: was a kid, so I just broke and my poor mom,

1524
01:20:19,880 --> 01:20:21,680
Speaker 1: my irish mom, was worried I was going to you know,

1525
01:20:21,880 --> 01:20:25,240
Speaker 1: lose too much money. I kept telling you, it's tuition, mom,

1526
01:20:25,320 --> 01:20:30,040
Speaker 1: it's tuition. But if you go in the third race,

1527
01:20:30,080 --> 01:20:31,760
Speaker 1: there were these people who would sell tip sheets for

1528
01:20:31,880 --> 01:20:33,080
Speaker 1: like five dollars, right.

1529
01:20:33,040 --> 01:20:35,679
Speaker 2: And you know, because because they know what's going to happen,

1530
01:20:35,800 --> 01:20:38,080
Speaker 2: they're selling the tip sheets, not making the bets.

1531
01:20:37,920 --> 01:20:40,640
Speaker 1: Exactly the customer's yachts exactly. But if you go like

1532
01:20:40,720 --> 01:20:43,880
Speaker 1: in the you know, the third or fourth race, they

1533
01:20:43,880 --> 01:20:46,000
Speaker 1: would quit selling them. They would just give them to

1534
01:20:46,040 --> 01:20:49,240
Speaker 1: you really well, like a lost leader. Maybe you'll you'll

1535
01:20:49,320 --> 01:20:52,040
Speaker 1: maybe next time you'll buy it. And so I'm sitting here,

1536
01:20:52,040 --> 01:20:55,559
Speaker 1: here's my little cynical twelve thirteen year old brain thinking,

1537
01:20:56,680 --> 01:20:59,080
Speaker 1: why are you giving away for free tips that you

1538
01:20:59,240 --> 01:21:01,800
Speaker 1: claim can make me money? Like this does not the

1539
01:21:01,880 --> 01:21:05,880
Speaker 1: math does not math. And I think that's a good lesson,

1540
01:21:06,960 --> 01:21:10,280
Speaker 1: like for markets. Right yeah, but you know, just just

1541
01:21:10,360 --> 01:21:13,240
Speaker 1: to clear away like the most naive, you know, immunize

1542
01:21:13,240 --> 01:21:15,720
Speaker 1: yourself to the most naive schemes.

1543
01:21:16,120 --> 01:21:19,000
Speaker 2: You know, you would think if the tips were valuable,

1544
01:21:19,160 --> 01:21:21,439
Speaker 2: rather than waste your time printing it up and selling them,

1545
01:21:21,520 --> 01:21:25,640
Speaker 2: you would just bet on the wing horses, especially in.

1546
01:21:25,680 --> 01:21:30,840
Speaker 1: A peramutual system, right because you know, the more the

1547
01:21:30,960 --> 01:21:34,120
Speaker 1: more your tip sheet buyers are betting on your horses.

1548
01:21:33,800 --> 01:21:35,479
Speaker 2: The lower guts right exactly.

1549
01:21:35,920 --> 01:21:37,040
Speaker 1: They're betting against.

1550
01:21:38,200 --> 01:21:40,800
Speaker 2: Our final question. Our final question, what do you know

1551
01:21:40,880 --> 01:21:44,679
Speaker 2: about the world of neuroeconomics today, might have been helpful

1552
01:21:45,200 --> 01:21:48,080
Speaker 2: when you were first getting started back in the nineteen eighties.

1553
01:21:50,240 --> 01:21:52,800
Speaker 1: You know, I'll answer that like a politicial answer a

1554
01:21:53,000 --> 01:21:54,760
Speaker 1: question I have a better answer for, which is about

1555
01:21:54,760 --> 01:21:55,639
Speaker 1: behavioral finance.

1556
01:21:55,800 --> 01:21:58,880
Speaker 2: Sure, so either or be fire or sure?

1557
01:21:59,320 --> 01:22:01,479
Speaker 1: I got it. So in your economics, I don't think

1558
01:22:01,640 --> 01:22:03,760
Speaker 1: we made too many mistakes. I think I wish we had.

1559
01:22:04,640 --> 01:22:07,120
Speaker 1: You know, we got a lot of grand support. Caltech

1560
01:22:07,240 --> 01:22:10,040
Speaker 1: was very supportive. I got to know a lot of

1561
01:22:10,120 --> 01:22:12,000
Speaker 1: interesting people who are generous with their time, who were

1562
01:22:12,120 --> 01:22:15,360
Speaker 1: kind of my tutors on neuroscience. I never took any formal,

1563
01:22:16,160 --> 01:22:18,639
Speaker 1: you know, coursework on it. It was came way, way

1564
01:22:18,680 --> 01:22:22,000
Speaker 1: way after my original rad training. So thank you everyone.

1565
01:22:23,280 --> 01:22:25,439
Speaker 1: I wish we had. We have not had much impact

1566
01:22:25,479 --> 01:22:29,560
Speaker 1: in academic economics particularly, and that's something we're kind of

1567
01:22:29,840 --> 01:22:32,640
Speaker 1: working on. Maybe we can do better behavioral finance. I

1568
01:22:32,680 --> 01:22:35,720
Speaker 1: think I started graduate school in the late seventies. In

1569
01:22:35,760 --> 01:22:38,920
Speaker 1: nineteen seventy eight, Mike Jensen published a very influential paper.

1570
01:22:40,000 --> 01:22:42,599
Speaker 1: It was an intruction to a special issue, and one

1571
01:22:42,640 --> 01:22:46,160
Speaker 1: of the first sentences is the market efficiency apothesis is

1572
01:22:46,280 --> 01:22:49,240
Speaker 1: one of the most well established empirical regularities in economics.

1573
01:22:51,120 --> 01:22:55,120
Speaker 1: But but that was like the high water mark, and

1574
01:22:55,240 --> 01:22:58,320
Speaker 1: the special issue was about there's some things that are anomalists,

1575
01:22:58,439 --> 01:23:02,400
Speaker 1: like earnings drift. He got a weird earnings announcement. The

1576
01:23:02,520 --> 01:23:05,200
Speaker 1: market reacts, but then the market reaction drifts up for it.

1577
01:23:05,320 --> 01:23:07,720
Speaker 1: It takes a couple of weeks, almost like food for

1578
01:23:07,800 --> 01:23:10,720
Speaker 1: the market so so absorbed it should not take a

1579
01:23:10,760 --> 01:23:13,400
Speaker 1: couple of weeks, right, right, There were other things where

1580
01:23:13,520 --> 01:23:16,479
Speaker 1: we see, you know, like one within one hour markets

1581
01:23:16,520 --> 01:23:22,960
Speaker 1: are repricing really well. But despite this Jensen article, the

1582
01:23:24,240 --> 01:23:27,520
Speaker 1: hostility to ba Heybalk finance was ferocious.

1583
01:23:28,840 --> 01:23:30,479
Speaker 2: That's a big word at that time. It was that

1584
01:23:30,760 --> 01:23:32,479
Speaker 2: so late seventies, early late.

1585
01:23:32,360 --> 01:23:34,080
Speaker 1: Seventies, early eighties, and so that's when I was kind

1586
01:23:34,080 --> 01:23:35,880
Speaker 1: of deciding do I want to stay in finance or

1587
01:23:35,960 --> 01:23:38,120
Speaker 1: mix it with and I remember having a discussion. I

1588
01:23:38,120 --> 01:23:40,680
Speaker 1: don't know if Jeane remembers it the same way with

1589
01:23:41,000 --> 01:23:43,000
Speaker 1: I had to write a paper for Eugene Fama's course,

1590
01:23:43,040 --> 01:23:44,840
Speaker 1: who was also kind of a mentor in this sense.

1591
01:23:45,120 --> 01:23:47,280
Speaker 1: Even though I didn't end up doing work that was close,

1592
01:23:48,200 --> 01:23:51,000
Speaker 1: you know, he was he was really relentless and very

1593
01:23:51,200 --> 01:23:54,360
Speaker 1: empirically driven, and he had a really good idea. When

1594
01:23:54,400 --> 01:23:57,240
Speaker 1: he started, people were thought he was crazy because there

1595
01:23:57,320 --> 01:23:59,840
Speaker 1: was all this stuff on, you know, there was even

1596
01:24:00,080 --> 01:24:02,320
Speaker 1: he wrote some papers on dividends, like well, the Optimal

1597
01:24:02,439 --> 01:24:05,200
Speaker 1: Dividend Payment Policy, and of course Miller and him would like,

1598
01:24:06,240 --> 01:24:08,360
Speaker 1: what be dividends at all? You just like take money

1599
01:24:08,360 --> 01:24:10,040
Speaker 1: from one bucket and put it in the other.

1600
01:24:11,040 --> 01:24:14,080
Speaker 2: Well, back in the early days of widows and orphan stocks,

1601
01:24:14,120 --> 01:24:15,240
Speaker 2: you people lived on.

1602
01:24:15,280 --> 01:24:17,800
Speaker 1: Their digiti Yeah, exactly because of the liquidity.

1603
01:24:17,600 --> 01:24:19,439
Speaker 2: Right, you don't want to sell do you want to

1604
01:24:19,479 --> 01:24:20,040
Speaker 2: hold on to it?

1605
01:24:20,120 --> 01:24:22,200
Speaker 1: And then the dividends, you know, it is enough to

1606
01:24:22,280 --> 01:24:22,479
Speaker 1: live on.

1607
01:24:22,920 --> 01:24:27,040
Speaker 2: Now the theory has shifted towards uh, it's more efficient

1608
01:24:27,200 --> 01:24:32,160
Speaker 2: return of capital to shareholders doing buybox than dividends. But

1609
01:24:32,520 --> 01:24:36,040
Speaker 2: that's only total return. If you're looking for that income stream,

1610
01:24:36,200 --> 01:24:37,760
Speaker 2: buybacks don't necessarily help you.

1611
01:24:37,880 --> 01:24:40,000
Speaker 1: Right, right exactly. So that's and that's also where the

1612
01:24:40,080 --> 01:24:42,600
Speaker 1: hero economic comes in with you know, why can't you

1613
01:24:42,760 --> 01:24:46,080
Speaker 1: just like create whatever income stream you want by borrowing

1614
01:24:46,160 --> 01:24:49,000
Speaker 1: and selling, right, that's right? And if you know, if

1615
01:24:49,040 --> 01:24:51,439
Speaker 1: you're really liquidity constrained or credit constrained, you can't. But

1616
01:24:51,600 --> 01:24:55,200
Speaker 1: for most people that's not a big deal anyway. So

1617
01:24:55,720 --> 01:24:59,120
Speaker 1: if I had known behavioral finance, would it didn't take

1618
01:24:59,160 --> 01:25:02,080
Speaker 1: off quickly. From nineteen seventy eight, which is Jensen nineteen

1619
01:25:02,120 --> 01:25:05,240
Speaker 1: eighty one, I graduated nineteen eighty five was the failure

1620
01:25:05,280 --> 01:25:10,680
Speaker 1: in DeMont paper about January effects. And even that was

1621
01:25:10,760 --> 01:25:15,200
Speaker 1: published as a It was in the Proceedings issue, which

1622
01:25:15,320 --> 01:25:19,160
Speaker 1: meant that the President of the of the AFA could

1623
01:25:19,320 --> 01:25:22,479
Speaker 1: pan pick papers. So the preceding issue had the most

1624
01:25:22,560 --> 01:25:26,479
Speaker 1: radical papers that were the foundation of aper economics. Fisher

1625
01:25:26,560 --> 01:25:30,360
Speaker 1: Black wrote a paper called Noise Traders. I thot it

1626
01:25:30,439 --> 01:25:33,200
Speaker 1: might have just been called noise. And then Dick Roll

1627
01:25:33,240 --> 01:25:36,040
Speaker 1: wrote a paper got R Squared, and he said, you know,

1628
01:25:36,160 --> 01:25:39,720
Speaker 1: if only news moves the market, right, then the R

1629
01:25:39,800 --> 01:25:42,240
Speaker 1: squared On days with no news, you know, you shouldn't

1630
01:25:42,280 --> 01:25:45,760
Speaker 1: have any volatility, And of course days with big news

1631
01:25:45,880 --> 01:25:50,040
Speaker 1: and small news similar to the story you were telling

1632
01:25:50,040 --> 01:25:53,040
Speaker 1: you in the beginning, Days with big news, big obvious

1633
01:25:53,160 --> 01:25:55,760
Speaker 1: news and hardly any news move about the same.

1634
01:25:57,600 --> 01:25:59,840
Speaker 2: The assumption being by the time it's in the front

1635
01:26:00,040 --> 01:26:03,320
Speaker 2: age of the new York Times, it's already reflected the.

1636
01:26:03,320 --> 01:26:05,360
Speaker 1: Markets, right, But also there may be things that are

1637
01:26:05,400 --> 01:26:08,320
Speaker 1: not newsy at all, Like in October eighty seven crash,

1638
01:26:08,840 --> 01:26:11,840
Speaker 1: you know, the Bundesbank moved rates by a quarter of

1639
01:26:11,880 --> 01:26:12,840
Speaker 1: a point or something.

1640
01:26:13,040 --> 01:26:15,680
Speaker 2: Who cares? That was the big news, right, but you know,

1641
01:26:16,000 --> 01:26:19,240
Speaker 2: you never know when that last straw breaks the camels correct.

1642
01:26:19,360 --> 01:26:21,439
Speaker 1: But but so all those ideas now that that we

1643
01:26:22,040 --> 01:26:23,920
Speaker 1: we you know, we feel like we have an understanding

1644
01:26:24,000 --> 01:26:27,160
Speaker 1: and examples. There was a lot of hostility to that.

1645
01:26:27,280 --> 01:26:33,800
Speaker 1: So I remember asking Gene, I'd like to study market psychology,

1646
01:26:33,920 --> 01:26:36,599
Speaker 1: like what do you know about market psychology? And he said,

1647
01:26:37,520 --> 01:26:41,640
Speaker 1: what's that, Mike? And psychology is Boston accent? You know.

1648
01:26:42,600 --> 01:26:45,120
Speaker 1: I think it's just a word they use on the news,

1649
01:26:45,800 --> 01:26:47,600
Speaker 1: like in Bloomberg. It's just a word they use on

1650
01:26:47,640 --> 01:26:48,800
Speaker 1: the news when the market moved.

1651
01:26:48,800 --> 01:26:51,600
Speaker 2: They don't know why, right, Well, no one wants to

1652
01:26:51,680 --> 01:26:55,160
Speaker 2: admit it's fairly random day to day. We're very humans

1653
01:26:55,200 --> 01:26:58,800
Speaker 2: are very I know that humans are very uncomfortable.

1654
01:26:58,360 --> 01:27:00,920
Speaker 1: And we're good at pattern right.

1655
01:27:01,000 --> 01:27:03,040
Speaker 2: We make up patterns. We come up with a narrative

1656
01:27:03,080 --> 01:27:10,240
Speaker 2: to explain it. I recall Dick Thaylor quoting maybe it

1657
01:27:10,320 --> 01:27:15,519
Speaker 2: was Max Planck, who's talking about physics scientis one funeral

1658
01:27:15,560 --> 01:27:19,280
Speaker 2: at a time. Taylor said the same thing about behavioral finance,

1659
01:27:19,360 --> 01:27:22,400
Speaker 2: and he also said, I'm bypassing the current generation and

1660
01:27:22,439 --> 01:27:25,600
Speaker 2: going right to the kids so they'll adapt a wholesale

1661
01:27:25,760 --> 01:27:29,680
Speaker 2: and literally he said, I'm teaching grads and undergrads this

1662
01:27:30,240 --> 01:27:32,240
Speaker 2: so we don't even have to wait for the funeral.

1663
01:27:32,360 --> 01:27:36,599
Speaker 2: And it seems to have worked. Oh yeah, no, absolutely, Colin,

1664
01:27:36,920 --> 01:27:39,439
Speaker 2: thank you so much for being so generous with your time.

1665
01:27:39,560 --> 01:27:44,080
Speaker 2: This has been absolutely fascinating. I'm glad we finally managed

1666
01:27:44,120 --> 01:27:47,200
Speaker 2: to do this. We have been speaking with Professor Colin

1667
01:27:47,240 --> 01:27:53,120
Speaker 2: Camera of California Institute of Technology. If you enjoy this conversation,

1668
01:27:53,360 --> 01:27:56,599
Speaker 2: well check out any of the five hundred previous interviews

1669
01:27:56,680 --> 01:27:59,599
Speaker 2: we've done over the past ten and a half years.

1670
01:27:59,640 --> 01:28:04,360
Speaker 2: You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever

1671
01:28:04,520 --> 01:28:07,720
Speaker 2: you find your favorite podcast, And be sure and check

1672
01:28:07,800 --> 01:28:11,799
Speaker 2: out my new short form podcast at the Money, short

1673
01:28:12,200 --> 01:28:18,200
Speaker 2: single subject conversations with experts about issues that affect your money, earning, spending,

1674
01:28:18,360 --> 01:28:21,600
Speaker 2: and investing it at the Money in the Masters in

1675
01:28:21,640 --> 01:28:25,640
Speaker 2: Business podcast feed, or wherever you find your favorite podcast.

1676
01:28:25,960 --> 01:28:27,519
Speaker 2: I would be remiss if I now to thank the

1677
01:28:27,600 --> 01:28:30,280
Speaker 2: Crack team that helps with these conversations together each week.

1678
01:28:30,680 --> 01:28:34,839
Speaker 2: John Wasserman is my audio engineer. Anna Luke is my producer.

1679
01:28:35,120 --> 01:28:38,800
Speaker 2: Sean Russo is my researcher. Sage Bauman is the head

1680
01:28:38,800 --> 01:28:42,920
Speaker 2: of podcasts at Bloomberg. I'm Barry Ridhelts. You've been listening

1681
01:28:43,040 --> 01:28:46,000
Speaker 2: to Masters in Business on Bloomberg Radio.