1
00:00:02,240 --> 00:00:06,800
Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio.

2
00:00:09,560 --> 00:00:12,400
Speaker 1: This weekend. On the podcast, I have an extra special guest.

3
00:00:12,800 --> 00:00:15,360
Speaker 1: His name is Tom Gilovich. He is a professor of

4
00:00:15,360 --> 00:00:20,200
Speaker 1: psychology at Cornell University, and I have to say he

5
00:00:20,360 --> 00:00:24,000
Speaker 1: is a person who has had over the years, tremendous

6
00:00:24,000 --> 00:00:29,920
Speaker 1: influence on my career, although admittedly unknowingly. I began in

7
00:00:30,000 --> 00:00:34,240
Speaker 1: this industry as a trader, and I was frequently perplexed

8
00:00:34,280 --> 00:00:37,600
Speaker 1: and fascinated by why the people on the desk around

9
00:00:37,600 --> 00:00:42,600
Speaker 1: me were doing either poorly or well any given day, week, month.

10
00:00:43,240 --> 00:00:47,120
Speaker 1: And I eventually figured out it wasn't that one guy

11
00:00:47,200 --> 00:00:50,639
Speaker 1: was smarter than another or someone had more knowledge. It

12
00:00:50,720 --> 00:00:54,440
Speaker 1: was their own behavior that led to their successful failures.

13
00:00:54,880 --> 00:00:59,720
Speaker 1: And so I started hunting for some information about why

14
00:00:59,800 --> 00:01:05,000
Speaker 1: people made certain behavioral decisions that they did. I ended

15
00:01:05,160 --> 00:01:09,800
Speaker 1: up tracking down a book of his from How We

16
00:01:09,920 --> 00:01:12,240
Speaker 1: Know It Isn't So, and that pretty much sent me

17
00:01:12,319 --> 00:01:17,959
Speaker 1: down the rabbit hole of behavioral economics, which has been

18
00:01:18,000 --> 00:01:21,399
Speaker 1: a tremendous asset to me uh both in the world

19
00:01:21,520 --> 00:01:26,280
Speaker 1: of finance and media. Understanding what motivates people to make

20
00:01:26,840 --> 00:01:30,480
Speaker 1: either good or bad decisions with their money UH is

21
00:01:30,520 --> 00:01:33,840
Speaker 1: a tremendous asset both what you should be doing with

22
00:01:33,920 --> 00:01:37,520
Speaker 1: your money and what you should be advising other people

23
00:01:37,640 --> 00:01:41,119
Speaker 1: to do with their money. This is to me one

24
00:01:41,160 --> 00:01:44,759
Speaker 1: of the more fascinating conversations UH that you'll hear if

25
00:01:44,800 --> 00:01:50,960
Speaker 1: you are all interested in filling the blank psychology, behavioral economics, heuristics, biases,

26
00:01:51,320 --> 00:01:53,960
Speaker 1: et cetera. I could have gone on for another three

27
00:01:53,960 --> 00:01:56,720
Speaker 1: hours with him. I barely got to scratch the surface

28
00:01:57,440 --> 00:02:01,280
Speaker 1: of all my questions with no further ado my conversation

29
00:02:01,360 --> 00:02:08,120
Speaker 1: with Tom Gilovich. I have an extra special guest this week,

30
00:02:08,280 --> 00:02:11,880
Speaker 1: and his name is Professor Thomas Gilovich. He is a

31
00:02:11,919 --> 00:02:17,560
Speaker 1: professor of psychology at Cornell University. He has published numerous

32
00:02:18,120 --> 00:02:22,480
Speaker 1: pure reviewed works on cognition and heuristics and is among

33
00:02:22,560 --> 00:02:26,760
Speaker 1: the most cited academics in the field. Working on behavioral

34
00:02:26,840 --> 00:02:31,160
Speaker 1: psychology and economics. His work has debunked the idea of

35
00:02:31,200 --> 00:02:35,160
Speaker 1: the hot hand in basketball, the spotlight effect, the bias,

36
00:02:35,240 --> 00:02:39,799
Speaker 1: blind site, clustering, illusion, and numerous other cognitive issues are

37
00:02:39,919 --> 00:02:43,360
Speaker 1: in his purview. He is the author of numerous books,

38
00:02:43,400 --> 00:02:47,600
Speaker 1: including a textbook on heuristics and biases UH. He is

39
00:02:47,639 --> 00:02:50,720
Speaker 1: the co author of Why Smart People make big bunny

40
00:02:50,760 --> 00:02:53,640
Speaker 1: mistakes and what you can do to correct them. I

41
00:02:53,720 --> 00:02:56,520
Speaker 1: became familiar with his work How We Know It Isn't So,

42
00:02:57,160 --> 00:03:01,280
Speaker 1: The Fallibility of Human Reason in Everyday. It was one

43
00:03:01,320 --> 00:03:05,359
Speaker 1: of the first popular books on behavioral economics and one

44
00:03:05,400 --> 00:03:11,000
Speaker 1: that I found incredibly influential. Thomas Killovich, Welcome to Bloomberg.

45
00:03:11,200 --> 00:03:13,880
Speaker 1: Happy to be here. Let's start out with how we

46
00:03:13,960 --> 00:03:16,760
Speaker 1: Know what is? And so you begin the book with

47
00:03:16,800 --> 00:03:21,360
Speaker 1: a quote from Artemus Ward. It ain't the things we

48
00:03:21,440 --> 00:03:24,400
Speaker 1: don't know that get us into trouble. It's the things

49
00:03:24,440 --> 00:03:29,440
Speaker 1: we know that just ain't so. Tell us about that. Well,

50
00:03:29,880 --> 00:03:33,000
Speaker 1: if we're convinced of things that aren't true, we're gonna

51
00:03:33,160 --> 00:03:36,120
Speaker 1: go down certain paths that aren't going to be productive.

52
00:03:36,280 --> 00:03:40,720
Speaker 1: And uh, that quote captures that idea very well. And

53
00:03:40,760 --> 00:03:44,120
Speaker 1: what I like about that quote is that most people

54
00:03:44,680 --> 00:03:50,760
Speaker 1: attribute it to Mark Twins or Will Rogers, and the

55
00:03:51,040 --> 00:03:56,280
Speaker 1: source I thought at the time was Artemus Ward. And

56
00:03:56,880 --> 00:04:00,400
Speaker 1: maybe two years after the publication of the book, reader

57
00:04:00,480 --> 00:04:03,520
Speaker 1: wrote to me and said, Uh, well, it's really interesting.

58
00:04:03,680 --> 00:04:05,960
Speaker 1: You're sort of telling everyone they got it wrong with

59
00:04:06,440 --> 00:04:09,360
Speaker 1: Will Rogers and Mark Twain, but in fact it goes

60
00:04:09,440 --> 00:04:13,960
Speaker 1: back even earlier than Artemis forward to someone named Josh

61
00:04:14,000 --> 00:04:17,000
Speaker 1: Billings that I hadn't heard of, so uh, it's kind

62
00:04:17,040 --> 00:04:20,880
Speaker 1: of ironic. Started off a book on illusion and air

63
00:04:21,040 --> 00:04:24,320
Speaker 1: to the very first sentence of the book contains a

64
00:04:24,960 --> 00:04:28,800
Speaker 1: citation air at least, but the that citation errors did

65
00:04:28,800 --> 00:04:32,000
Speaker 1: not lead you down a path filled with errors. Let's

66
00:04:32,040 --> 00:04:34,960
Speaker 1: discuss a little bit about the things that you discovered

67
00:04:35,560 --> 00:04:38,039
Speaker 1: uh and published in the book, and will begin with

68
00:04:38,080 --> 00:04:43,360
Speaker 1: a very simple question, what are heuristics and biases? Well,

69
00:04:43,400 --> 00:04:45,760
Speaker 1: the bias part is quite easy. It's a term that

70
00:04:45,920 --> 00:04:51,080
Speaker 1: people are familiar with when there's a systematic departure between um,

71
00:04:51,440 --> 00:04:54,360
Speaker 1: a belief that you have in reality, or a tendency

72
00:04:54,440 --> 00:04:57,760
Speaker 1: to choose to veer in one direction when you should

73
00:04:57,800 --> 00:05:00,960
Speaker 1: be varying in another direction. So it's assist thematic departure

74
00:05:01,080 --> 00:05:06,440
Speaker 1: from uh reality or the best assessment of reality. Heuristics

75
00:05:06,520 --> 00:05:10,800
Speaker 1: that's a little more complicated for most folks. UM. It's

76
00:05:10,880 --> 00:05:15,560
Speaker 1: generally defined in the behavioral economics world as a rough

77
00:05:15,600 --> 00:05:21,119
Speaker 1: approximation seed of the pants uh rule of thumbs another

78
00:05:21,160 --> 00:05:24,600
Speaker 1: way of describing it. Yes, and why do these heuristics

79
00:05:24,680 --> 00:05:30,040
Speaker 1: lead people down the wrong path? Because there they generally

80
00:05:30,080 --> 00:05:33,559
Speaker 1: work pretty well. In One of the earliest examples used

81
00:05:33,960 --> 00:05:39,400
Speaker 1: UH applied to psychology was Daniel Khneman and Amos Tversky's

82
00:05:39,440 --> 00:05:43,320
Speaker 1: example to we use the clarity of the of an

83
00:05:43,320 --> 00:05:46,320
Speaker 1: image as a cue for how far away it is.

84
00:05:46,480 --> 00:05:50,320
Speaker 1: Farther things are, the harder it is to see them clearly,

85
00:05:50,400 --> 00:05:55,240
Speaker 1: so they will see more indistinct and that generally works

86
00:05:55,279 --> 00:05:57,840
Speaker 1: pretty well. We're able to see whether something's very far

87
00:05:57,839 --> 00:06:02,520
Speaker 1: away or relatively close up. But on a hazy day,

88
00:06:02,560 --> 00:06:07,120
Speaker 1: that's gonna make things seem farther away than they really are. Um.

89
00:06:07,240 --> 00:06:11,520
Speaker 1: And conversely, on a spectacularly clear day, you often have

90
00:06:11,640 --> 00:06:15,480
Speaker 1: the reaction of whoa those mountains are. I didn't realize

91
00:06:15,520 --> 00:06:19,720
Speaker 1: they were that close. Let's use another example of of

92
00:06:19,760 --> 00:06:23,400
Speaker 1: some biases. Your online in the supermarket and your line

93
00:06:23,400 --> 00:06:26,159
Speaker 1: doesn't seem to be moving, the line next to you

94
00:06:26,240 --> 00:06:28,240
Speaker 1: really looks like it's flying, or you're waiting for a

95
00:06:28,279 --> 00:06:30,640
Speaker 1: toll booth. I know parts of the country still have

96
00:06:30,800 --> 00:06:34,960
Speaker 1: toll booths. Um. And you switch lines and suddenly the

97
00:06:35,000 --> 00:06:38,880
Speaker 1: line you're on comes to a dead halt and the

98
00:06:38,960 --> 00:06:42,080
Speaker 1: line you just left seems to be moving. What is

99
00:06:42,120 --> 00:06:47,279
Speaker 1: it about our life experience that causes that illusion or

100
00:06:47,440 --> 00:06:51,960
Speaker 1: is it an illusion? Um that one. As far as

101
00:06:52,040 --> 00:06:55,040
Speaker 1: the grocery lines, I don't know if anyone has formally

102
00:06:55,160 --> 00:06:58,000
Speaker 1: studied it, but you have to ask what principle of

103
00:06:58,040 --> 00:07:03,159
Speaker 1: the universe would there be that it systematically uh distort

104
00:07:03,279 --> 00:07:05,839
Speaker 1: things such that whenever you move to a line, it

105
00:07:05,839 --> 00:07:07,839
Speaker 1: would slow down and the line that you were in

106
00:07:08,120 --> 00:07:11,400
Speaker 1: suddenly sped up. Uh. But it's easy to explain why

107
00:07:11,400 --> 00:07:14,240
Speaker 1: people would believe that, even if it's not true. That

108
00:07:14,400 --> 00:07:17,640
Speaker 1: is to say, uh, those times when you stay in

109
00:07:17,800 --> 00:07:22,560
Speaker 1: your overly busy line, you're tempted to move to another one, UM,

110
00:07:22,800 --> 00:07:25,960
Speaker 1: and it turns out that that you can see that

111
00:07:25,960 --> 00:07:29,040
Speaker 1: that would have been a better thing. Your line stays slow,

112
00:07:29,440 --> 00:07:31,880
Speaker 1: you could see people speeding through the other line. That

113
00:07:32,000 --> 00:07:35,720
Speaker 1: bothers you, but you get over it. If, on the

114
00:07:35,720 --> 00:07:38,160
Speaker 1: other hand, you make the opposite mistake, you switch to

115
00:07:38,400 --> 00:07:42,040
Speaker 1: the seemingly speedy line and it slows down, the line

116
00:07:42,080 --> 00:07:45,560
Speaker 1: you were in suddenly speeds up, you're gonna kick yourself

117
00:07:45,600 --> 00:07:47,440
Speaker 1: and say, Wow, why did I do that? I was

118
00:07:47,480 --> 00:07:51,239
Speaker 1: in the right line. I brought this on myself, And

119
00:07:51,440 --> 00:07:55,240
Speaker 1: it's more annoying. And because it's more annoying, it's more memorable,

120
00:07:55,680 --> 00:07:59,080
Speaker 1: and therefore, UH, you're gonna have a distorted sense in

121
00:07:59,120 --> 00:08:02,160
Speaker 1: your head of how common it is. UM. Very much

122
00:08:02,200 --> 00:08:06,760
Speaker 1: like UH, there's a belief in the sports world, baseball

123
00:08:06,800 --> 00:08:09,280
Speaker 1: world that if your team, your picture has a no

124
00:08:09,400 --> 00:08:13,960
Speaker 1: hitter in progress, don't comment on it. And that's partly

125
00:08:14,000 --> 00:08:17,520
Speaker 1: fed by the idea that if you're picture does have

126
00:08:17,560 --> 00:08:19,680
Speaker 1: a no hitter and you say, oh, we've got a

127
00:08:19,720 --> 00:08:21,760
Speaker 1: no hitter going, this is great, and then they lose it,

128
00:08:22,440 --> 00:08:25,480
Speaker 1: you draw an association between those two and those are

129
00:08:25,480 --> 00:08:28,600
Speaker 1: gonna stand out, and you're gonna think that it's uh,

130
00:08:28,640 --> 00:08:32,160
Speaker 1: what you've done has played some determinative role, which of

131
00:08:32,160 --> 00:08:34,679
Speaker 1: course hasn't. So so let's talk a little bit about

132
00:08:34,800 --> 00:08:38,319
Speaker 1: mean reversion. Very often, when we see things that are

133
00:08:38,400 --> 00:08:41,920
Speaker 1: outliers to the upside of the downside, we're sort of

134
00:08:41,960 --> 00:08:47,400
Speaker 1: surprised when the next item in that series is not

135
00:08:47,559 --> 00:08:50,480
Speaker 1: as extreme, be it how fast the line is moving,

136
00:08:50,600 --> 00:08:54,120
Speaker 1: or how easily a no hitter UH is lost and

137
00:08:54,160 --> 00:08:57,760
Speaker 1: goes back to normal issues. Why do people have such

138
00:08:57,760 --> 00:09:00,959
Speaker 1: a hard time with mean reversion that It's a great question,

139
00:09:01,120 --> 00:09:04,560
Speaker 1: and there are a number of things that contribute to UH.

140
00:09:04,800 --> 00:09:08,880
Speaker 1: This belief. The failure to recognize the fact that UM

141
00:09:09,160 --> 00:09:13,160
Speaker 1: regression to the mean is happening UM. And one of

142
00:09:13,200 --> 00:09:17,120
Speaker 1: them is that UH is the same story that I've

143
00:09:17,160 --> 00:09:22,040
Speaker 1: described before, which is when it reverts UM, and particularly

144
00:09:22,080 --> 00:09:26,080
Speaker 1: when it reverts after you've done something that that stands

145
00:09:26,120 --> 00:09:29,200
Speaker 1: out in your memory more and distorts your the intuitive

146
00:09:29,280 --> 00:09:33,720
Speaker 1: database that you have in your head. And so UH.

147
00:09:33,760 --> 00:09:37,200
Speaker 1: There are a lot of superstitions that are essentially a

148
00:09:37,240 --> 00:09:41,359
Speaker 1: failure to recognize the operation of regression, the Sports Illustrated

149
00:09:41,440 --> 00:09:44,920
Speaker 1: jinks being one of them. That it's believed that if

150
00:09:44,920 --> 00:09:47,440
Speaker 1: you get your picture on the cover of Sports Illustrated,

151
00:09:47,840 --> 00:09:51,400
Speaker 1: that that's bad luck UM. And it doesn't take that

152
00:09:51,520 --> 00:09:55,120
Speaker 1: much insight to recognize that really is a mean aversion account.

153
00:09:55,160 --> 00:09:57,160
Speaker 1: That is, you only get your picture on the cover

154
00:09:57,240 --> 00:10:01,040
Speaker 1: of Sports Illustrated if you've had a run of success

155
00:10:01,120 --> 00:10:05,360
Speaker 1: and extraordinary success at time one is going to be

156
00:10:05,440 --> 00:10:09,840
Speaker 1: followed not by abject failure but by somewhat less extreme

157
00:10:11,160 --> 00:10:14,320
Speaker 1: a run of success afterwards. So you're right there on

158
00:10:14,360 --> 00:10:17,880
Speaker 1: the peak. On average, people are going to do less

159
00:10:17,880 --> 00:10:20,199
Speaker 1: well the next time, and that gives rise to this

160
00:10:20,280 --> 00:10:24,040
Speaker 1: belief that um, it's bad luck to be pictured on

161
00:10:24,080 --> 00:10:28,040
Speaker 1: the cover of Sports Illustrated magazine, and people athletes truly

162
00:10:28,120 --> 00:10:31,080
Speaker 1: believe it. Some of them have turned down the opportunity

163
00:10:31,120 --> 00:10:34,120
Speaker 1: to be on the cover of Sports Illustrated simply because

164
00:10:34,160 --> 00:10:37,000
Speaker 1: they thought it was bad luck. Let's talk a little

165
00:10:37,000 --> 00:10:41,120
Speaker 1: bit about the hot hands in basketball. You first wrote

166
00:10:41,160 --> 00:10:45,120
Speaker 1: about this effect with Amos Tversky. Is it about thirty

167
00:10:45,200 --> 00:10:48,840
Speaker 1: years ago? Is that right? Yes, paper came out, so

168
00:10:48,840 --> 00:10:52,400
Speaker 1: so tell us what your studies found and how you

169
00:10:52,440 --> 00:10:55,920
Speaker 1: went about proving that the hot hands wasn't everything it

170
00:10:56,000 --> 00:11:01,480
Speaker 1: appeared to be. Uh. The the belief in the hot

171
00:11:01,559 --> 00:11:05,600
Speaker 1: hand is just one of the most powerful and firmly

172
00:11:05,640 --> 00:11:09,440
Speaker 1: held beliefs that we have. If you've ever played or

173
00:11:09,440 --> 00:11:13,160
Speaker 1: watched the game of basketball, it just seems like people

174
00:11:13,320 --> 00:11:15,920
Speaker 1: get on these streaks where they can do no wrong.

175
00:11:16,080 --> 00:11:20,280
Speaker 1: I am in the zone, absolutely, and it's incredibly compelling

176
00:11:20,360 --> 00:11:24,280
Speaker 1: that you've made a few shots, the game just seems easier.

177
00:11:24,320 --> 00:11:27,240
Speaker 1: It seems like you don't even have to attend to

178
00:11:27,320 --> 00:11:30,080
Speaker 1: the basket as much as normal, and it just goes in.

179
00:11:30,240 --> 00:11:33,400
Speaker 1: It's absolutely compelling. Michael Jordan used to say, there are

180
00:11:33,480 --> 00:11:39,920
Speaker 1: times when the rim looks bigger, absolutely um, and we

181
00:11:40,000 --> 00:11:46,160
Speaker 1: wondered whether there was less to that belief than basketball

182
00:11:46,200 --> 00:11:51,360
Speaker 1: players think. UM, we're not. It was we don't want

183
00:11:51,360 --> 00:11:56,240
Speaker 1: to challenge the feeling. There's no question that when you've

184
00:11:56,280 --> 00:12:00,000
Speaker 1: done well, you feel different differently, and when you've done poor,

185
00:12:00,000 --> 00:12:04,040
Speaker 1: really you feel differently. The basket seems to have shrunk.

186
00:12:04,080 --> 00:12:06,599
Speaker 1: It seems like the best aim shot is going to

187
00:12:06,679 --> 00:12:10,479
Speaker 1: go partly in the cylinder and pop right out. UM,

188
00:12:10,480 --> 00:12:13,200
Speaker 1: no questioning that. But the belief in the hot hand

189
00:12:13,280 --> 00:12:17,240
Speaker 1: is really a three link chain. Previous performance affects how

190
00:12:17,320 --> 00:12:20,600
Speaker 1: you feel. How you feel affects subsequent performance, and we

191
00:12:20,600 --> 00:12:24,200
Speaker 1: were interested in the link between the second and third,

192
00:12:24,320 --> 00:12:27,679
Speaker 1: and the thought was that we exaggerate that for many

193
00:12:27,760 --> 00:12:32,440
Speaker 1: of the same UH psychological principles we've already talked about UM.

194
00:12:32,559 --> 00:12:36,960
Speaker 1: And when we tested it, UH, the our hypothesis was

195
00:12:37,000 --> 00:12:40,400
Speaker 1: that people are going to exaggerate how much heat there is,

196
00:12:40,440 --> 00:12:44,240
Speaker 1: how much streakiness there is in performance. And when we

197
00:12:44,320 --> 00:12:48,760
Speaker 1: did the analyzes, turned out there was no connection between

198
00:12:48,840 --> 00:12:51,880
Speaker 1: link two and three. That is, how you feel doesn't

199
00:12:51,880 --> 00:12:56,360
Speaker 1: seem to influence how UH you perform in the future,

200
00:12:56,400 --> 00:12:59,520
Speaker 1: at least at the professional level. So let's let's be

201
00:12:59,679 --> 00:13:02,520
Speaker 1: really precise with that. When you say there is no link.

202
00:13:03,000 --> 00:13:07,080
Speaker 1: There's not even the slight improvement. Hey, I'm lose some

203
00:13:07,320 --> 00:13:12,080
Speaker 1: hitting shots. I'm feeling good each subsequent basket. What is

204
00:13:12,160 --> 00:13:15,200
Speaker 1: the correlation with the future success whether I made a

205
00:13:15,200 --> 00:13:17,600
Speaker 1: few in a row where I missed a few row. UM.

206
00:13:17,920 --> 00:13:21,240
Speaker 1: Two things are important there. One is um, there may

207
00:13:21,280 --> 00:13:25,360
Speaker 1: be some ways in which you've experienced some success, you

208
00:13:25,480 --> 00:13:29,120
Speaker 1: feel different, and it might influence your future behavior that

209
00:13:29,200 --> 00:13:32,760
Speaker 1: we haven't studied. We put aside from the hot hand idea,

210
00:13:32,840 --> 00:13:36,440
Speaker 1: which is I've done well. Maybe I'm a more energetic player,

211
00:13:36,480 --> 00:13:39,520
Speaker 1: I played better defense, I get more rebounds. We don't

212
00:13:39,520 --> 00:13:41,760
Speaker 1: know about that, and that may be true. What we

213
00:13:41,800 --> 00:13:44,600
Speaker 1: did look at do you become a better shooter? Not

214
00:13:44,720 --> 00:13:46,640
Speaker 1: do you take more shots? You may you probably do

215
00:13:46,720 --> 00:13:49,400
Speaker 1: take more shots because you're feeling hot. The question is

216
00:13:49,400 --> 00:13:52,880
Speaker 1: are you more likely to make a shot um after

217
00:13:53,000 --> 00:13:56,600
Speaker 1: having succeeded in the past versus after having failed. And

218
00:13:56,679 --> 00:14:00,120
Speaker 1: the data show and this remains, you know, controversial or

219
00:14:00,200 --> 00:14:04,120
Speaker 1: thirty years, there's been back and forth. All the challenges

220
00:14:04,160 --> 00:14:06,720
Speaker 1: to it for decades have not stood up. There's a

221
00:14:06,720 --> 00:14:10,120
Speaker 1: recent challenge that's among the more interesting ones. So I

222
00:14:10,120 --> 00:14:14,840
Speaker 1: have to say it remains controversial. Our original hypothesis UM

223
00:14:15,040 --> 00:14:20,600
Speaker 1: isn't controversial. That is to say, UM people wildly overestimate

224
00:14:21,080 --> 00:14:24,680
Speaker 1: the extent to which people are hot or how streaky

225
00:14:24,760 --> 00:14:29,560
Speaker 1: people are. Whether there's any sort of carryover that remains

226
00:14:30,000 --> 00:14:33,320
Speaker 1: a bit controversial. So some of the pushback has been,

227
00:14:33,400 --> 00:14:36,920
Speaker 1: you know, a player gets hot and the defense collapses

228
00:14:37,000 --> 00:14:39,880
Speaker 1: on on them and they have less good looks at

229
00:14:39,880 --> 00:14:43,600
Speaker 1: the basket, and so naturally after a certain streak they're

230
00:14:43,640 --> 00:14:47,160
Speaker 1: gonna start missing. But you don't have that same defensive

231
00:14:47,160 --> 00:14:50,000
Speaker 1: pressure with foul shooting, do you. That's right? And what

232
00:14:50,120 --> 00:14:52,400
Speaker 1: is the data show with that? The data show that

233
00:14:52,480 --> 00:14:55,400
Speaker 1: the outcome of the second free throw is completely independent

234
00:14:55,480 --> 00:14:58,760
Speaker 1: of the out outcome of the first totally. So if

235
00:14:58,800 --> 00:15:01,720
Speaker 1: you hit or miss the US one, the outcome of

236
00:15:01,760 --> 00:15:05,840
Speaker 1: the second one is statistically no different. Right, And a

237
00:15:05,880 --> 00:15:09,000
Speaker 1: lot of basketball UM players and fans will say, well,

238
00:15:09,040 --> 00:15:11,440
Speaker 1: I'm not so impressed by that, because you can't really

239
00:15:11,480 --> 00:15:14,000
Speaker 1: be streaky when it comes to free throws. Now they

240
00:15:14,000 --> 00:15:17,000
Speaker 1: say that after the fact, but I mean, you're standing there,

241
00:15:17,080 --> 00:15:21,040
Speaker 1: nobody's guarding you. There are certain think of Reggie Miller

242
00:15:21,080 --> 00:15:24,920
Speaker 1: of the Yetta Paces used to shoot like free throws.

243
00:15:25,080 --> 00:15:30,120
Speaker 1: He was an outstanding free throw shooter. Why would people

244
00:15:30,320 --> 00:15:33,240
Speaker 1: not assume if there's a streak when you're on the

245
00:15:33,360 --> 00:15:35,560
Speaker 1: on the court, why would you not assume there's a

246
00:15:35,560 --> 00:15:39,960
Speaker 1: streak at the foul line. I think they say that

247
00:15:40,040 --> 00:15:42,800
Speaker 1: after having seen the data UM, and they want to

248
00:15:42,920 --> 00:15:46,120
Speaker 1: maintain that belief. But we went a step further and

249
00:15:46,120 --> 00:15:48,560
Speaker 1: we conducted UM and other people have done this too,

250
00:15:48,680 --> 00:15:51,560
Speaker 1: where UM, you have people shoot in the gym for you,

251
00:15:51,840 --> 00:15:54,960
Speaker 1: and the feeling for all of us who've played basketball

252
00:15:55,080 --> 00:15:57,040
Speaker 1: is you can feel it in warm up Sometime it

253
00:15:57,040 --> 00:15:58,880
Speaker 1: doesn't have to be in the heat of the game

254
00:15:58,920 --> 00:16:02,440
Speaker 1: that you can feel hot. And uh. We have people

255
00:16:02,520 --> 00:16:06,040
Speaker 1: take a series of shots along an arc equidistance from

256
00:16:06,040 --> 00:16:10,280
Speaker 1: the basket, and before each shot they place a bet

257
00:16:10,320 --> 00:16:12,960
Speaker 1: on themselves, take a risky bet if they're feeling hot,

258
00:16:13,040 --> 00:16:15,240
Speaker 1: or a more conservative bet if they are not feeling

259
00:16:15,280 --> 00:16:17,840
Speaker 1: so hot. It turns out we can predict the bets

260
00:16:17,840 --> 00:16:19,680
Speaker 1: they're going to make. That is, if they've made several

261
00:16:19,680 --> 00:16:22,560
Speaker 1: shots in a row, that is a very strong predictor

262
00:16:22,760 --> 00:16:26,400
Speaker 1: of whether they're going to choose the risky bet or not. However,

263
00:16:26,880 --> 00:16:29,840
Speaker 1: the bets that they choose are not very good predictors

264
00:16:29,880 --> 00:16:32,800
Speaker 1: of what's going to happen next? Again, this three linked chain,

265
00:16:33,440 --> 00:16:36,320
Speaker 1: no problem between the first and the second. How you've

266
00:16:36,360 --> 00:16:40,440
Speaker 1: done influences how you feel, But surprisingly how you feel

267
00:16:40,520 --> 00:16:44,640
Speaker 1: has um either no or very little impact on your

268
00:16:44,720 --> 00:16:46,920
Speaker 1: likelihood of making the next shot. And again I want

269
00:16:46,920 --> 00:16:49,480
Speaker 1: to stress it has to do with your ability to,

270
00:16:50,120 --> 00:16:53,520
Speaker 1: as they now say, score the basketball, get the ball

271
00:16:53,640 --> 00:16:55,520
Speaker 1: in the cylinder. It may affect you in other ways,

272
00:16:55,560 --> 00:16:58,120
Speaker 1: may make you a better defender, better pass or whatever.

273
00:16:58,760 --> 00:17:01,040
Speaker 1: So about a year a half ago I read the

274
00:17:01,080 --> 00:17:04,679
Speaker 1: book by Michael Lewis, The Undoing Project about Danny Khneman,

275
00:17:04,720 --> 00:17:07,760
Speaker 1: any of us Amos Tversky. You wrote the Hot Hand

276
00:17:07,760 --> 00:17:13,120
Speaker 1: paper with Amos Tversky? What was it like working with him?

277
00:17:13,160 --> 00:17:17,439
Speaker 1: I mean, it was certainly a highlight of my career,

278
00:17:17,480 --> 00:17:21,040
Speaker 1: and subsequently had the great opportunity to have worked with

279
00:17:21,119 --> 00:17:26,800
Speaker 1: Danny Khneman as well. Um and Amos, who's brilliant, as

280
00:17:26,880 --> 00:17:29,760
Speaker 1: you know, as as comes across in The Undoing Project,

281
00:17:30,160 --> 00:17:33,040
Speaker 1: and one of the things that I most liked about

282
00:17:33,880 --> 00:17:36,840
Speaker 1: uh working with him one of the great lessons. And

283
00:17:36,880 --> 00:17:41,240
Speaker 1: this applies equally as well to Koneman. Um. They're both

284
00:17:41,240 --> 00:17:45,320
Speaker 1: brilliant and it's fun to be in close proximity to brilliance.

285
00:17:46,000 --> 00:17:49,440
Speaker 1: But that's not enough. Both of these guys were incredibly

286
00:17:49,600 --> 00:17:53,119
Speaker 1: hard working and detailed oriented. And one of the things

287
00:17:53,200 --> 00:17:56,880
Speaker 1: I learned from Amos it's kind of similar to this,

288
00:17:57,240 --> 00:18:00,919
Speaker 1: uh saying people have about gall that you know, you

289
00:18:01,040 --> 00:18:05,720
Speaker 1: drive for show and you put for dough. Um that uh,

290
00:18:05,760 --> 00:18:09,480
Speaker 1: you know, when we were basically done with our hot

291
00:18:09,560 --> 00:18:12,000
Speaker 1: hand paper. In fact, as a young person, I thought

292
00:18:12,040 --> 00:18:15,320
Speaker 1: we were done. He would several times, Tom, come in here.

293
00:18:15,320 --> 00:18:17,840
Speaker 1: I've moved this sentence around here, and I've changed this word.

294
00:18:18,000 --> 00:18:20,879
Speaker 1: And the thought was the lesson was if someone of

295
00:18:21,000 --> 00:18:24,760
Speaker 1: his stature is really sweating the details right here at

296
00:18:24,840 --> 00:18:28,119
Speaker 1: the very end, just making the paper a little better

297
00:18:28,320 --> 00:18:31,720
Speaker 1: in these marginal ways, I can do that too. And

298
00:18:31,760 --> 00:18:34,399
Speaker 1: it's just been a very helpful lesson. You're never really

299
00:18:34,440 --> 00:18:38,120
Speaker 1: done and things can always be made better. And uh,

300
00:18:38,320 --> 00:18:43,240
Speaker 1: both Condimn and Diversky are you know, they they're they're

301
00:18:43,280 --> 00:18:46,600
Speaker 1: into what they're doing, passionately care about it, and it

302
00:18:46,640 --> 00:18:49,840
Speaker 1: shows in their ability to throw themselves into it. And

303
00:18:49,880 --> 00:18:54,520
Speaker 1: there's there's no being brilliants great, and both of them are,

304
00:18:54,560 --> 00:18:57,320
Speaker 1: but to get to their level, you also have to

305
00:18:57,359 --> 00:19:01,400
Speaker 1: have a great capacity for hard work and great enthusiasm

306
00:19:01,440 --> 00:19:03,960
Speaker 1: for what you're doing, which both of them did do.

307
00:19:04,480 --> 00:19:08,800
Speaker 1: Brilliance is necessary, but not sufficient, well said. Let's talk

308
00:19:08,840 --> 00:19:13,879
Speaker 1: a little bit about some of your research on happiness

309
00:19:14,040 --> 00:19:19,760
Speaker 1: and how do either get happy or avoid unhappiness. Let's

310
00:19:19,800 --> 00:19:23,840
Speaker 1: begin with the discussion of regret. What is it that

311
00:19:23,920 --> 00:19:27,720
Speaker 1: people regret more the things they do or the things

312
00:19:27,760 --> 00:19:31,320
Speaker 1: that they decide not to do. I think one thing

313
00:19:31,359 --> 00:19:35,720
Speaker 1: that's interesting about that question is that it depends upon

314
00:19:36,200 --> 00:19:40,960
Speaker 1: um when you're talking about it, that in the immediate

315
00:19:41,000 --> 00:19:44,679
Speaker 1: aftermath of a mistake of action, that just hurts a

316
00:19:44,720 --> 00:19:49,120
Speaker 1: lot more than omission. And uh, you see that everywhere.

317
00:19:49,280 --> 00:19:51,520
Speaker 1: It plays out in the example that you started with

318
00:19:51,560 --> 00:19:54,400
Speaker 1: about the grocery store line, that if you switch from

319
00:19:54,400 --> 00:19:58,439
Speaker 1: a slow line to one that's seemingly faster and it

320
00:19:58,520 --> 00:20:00,760
Speaker 1: slows down, and you just kick yourself. I was in

321
00:20:00,800 --> 00:20:03,720
Speaker 1: a better line, Why did I switch? Uh? The example

322
00:20:03,800 --> 00:20:06,120
Speaker 1: at a university, of course that I think everyone can

323
00:20:06,200 --> 00:20:09,919
Speaker 1: relate to is people taking multiple choice tests and you

324
00:20:10,000 --> 00:20:12,879
Speaker 1: have the common experience of zipping along, Oh this one's

325
00:20:12,920 --> 00:20:16,440
Speaker 1: B and oh wait a minute, maybe maybe it's D,

326
00:20:16,600 --> 00:20:18,520
Speaker 1: and you go back and forth. Should I stick with

327
00:20:18,600 --> 00:20:21,760
Speaker 1: my initial hunt or should I switch to now what

328
00:20:21,880 --> 00:20:24,919
Speaker 1: I think that it is? Turns out people have studied that,

329
00:20:25,160 --> 00:20:28,600
Speaker 1: and if you face that dilemma, Uh, you're much more

330
00:20:28,640 --> 00:20:32,400
Speaker 1: likely to get the right answer by switching than staying.

331
00:20:32,960 --> 00:20:35,920
Speaker 1: Students believe the complete opposite is better to go with

332
00:20:36,000 --> 00:20:41,280
Speaker 1: your initial gut instinct. And UM, So their belief is incorrect,

333
00:20:41,320 --> 00:20:45,240
Speaker 1: doesn't fit the data that's been observed. Um. But it's

334
00:20:45,240 --> 00:20:47,399
Speaker 1: easy to see why they would believe that. That is,

335
00:20:47,480 --> 00:20:50,280
Speaker 1: if you thought it was be and then you convince

336
00:20:50,320 --> 00:20:53,280
Speaker 1: yourself let me switch to D. You erase it. Now

337
00:20:53,359 --> 00:20:55,720
Speaker 1: you're endorsing B. And it turns out you were right.

338
00:20:56,160 --> 00:21:00,760
Speaker 1: You're gonna kick yourself. I had I had the right answer.

339
00:21:00,800 --> 00:21:03,560
Speaker 1: I knew it you did. Um. So, short term, the

340
00:21:03,600 --> 00:21:08,080
Speaker 1: bias against action is a manifestation of regret aversion. We

341
00:21:08,160 --> 00:21:11,679
Speaker 1: don't want to do something that we ultimately regret. But

342
00:21:11,880 --> 00:21:15,080
Speaker 1: then how does that play out with the sort of

343
00:21:15,240 --> 00:21:20,120
Speaker 1: deathbed statements we've seen from people I should have done X. Yeah,

344
00:21:20,240 --> 00:21:23,320
Speaker 1: that's the that's the interesting part. This change from the

345
00:21:23,720 --> 00:21:28,399
Speaker 1: close temporal proximity to a distant perspective, and over time

346
00:21:28,880 --> 00:21:31,399
Speaker 1: we do things with our because our mistakes of action

347
00:21:31,440 --> 00:21:34,840
Speaker 1: are so painful, we do things about them, We make

348
00:21:34,880 --> 00:21:38,600
Speaker 1: amends to other people, We engage in what psychologists called

349
00:21:38,680 --> 00:21:42,240
Speaker 1: cognitive dissonance reduction to make ourselves feel better about it.

350
00:21:42,440 --> 00:21:46,000
Speaker 1: We identify and part of that is identifying a silver lining.

351
00:21:46,040 --> 00:21:49,000
Speaker 1: If you ask people about some of their biggest regrets,

352
00:21:49,080 --> 00:21:53,199
Speaker 1: people will say, oh, I married the wrong person. But

353
00:21:53,480 --> 00:21:55,680
Speaker 1: they will say, yeah, that was a mistake, but I

354
00:21:55,680 --> 00:21:57,879
Speaker 1: wouldn't have these great kids if I hadn't done that.

355
00:21:58,000 --> 00:22:00,760
Speaker 1: So the marriage ended but turned out to have been

356
00:22:00,760 --> 00:22:02,680
Speaker 1: a good thing because I have these kids that I love.

357
00:22:03,160 --> 00:22:06,600
Speaker 1: Um And so there's all this mental stuff you do

358
00:22:06,760 --> 00:22:11,440
Speaker 1: to make yourself more comfortable with many, at least mistakes

359
00:22:11,480 --> 00:22:14,960
Speaker 1: of action. Whereas things that you didn't do, rather than

360
00:22:15,000 --> 00:22:19,200
Speaker 1: shrinking over time, they often grow. You often think, oh,

361
00:22:19,359 --> 00:22:21,119
Speaker 1: you know, if I had only taken that class, I

362
00:22:21,119 --> 00:22:23,440
Speaker 1: would have gone into this profession rather than this one,

363
00:22:23,480 --> 00:22:27,159
Speaker 1: and you imagine all the great things that could have happened.

364
00:22:27,160 --> 00:22:31,919
Speaker 1: So over time, regrets of inaction either don't shrink like

365
00:22:32,000 --> 00:22:34,760
Speaker 1: the action ones do, or they even grow over time.

366
00:22:34,800 --> 00:22:38,040
Speaker 1: And so you, as you said, on people's deathbeds, they

367
00:22:39,640 --> 00:22:42,240
Speaker 1: regret more of the things that they didn't do. I

368
00:22:42,240 --> 00:22:44,159
Speaker 1: could have been a contender. You know, I could have

369
00:22:44,200 --> 00:22:46,600
Speaker 1: been this, I could have been that. So so actions

370
00:22:47,119 --> 00:22:50,720
Speaker 1: become rationalized and we learned to deal with our mistakes,

371
00:22:51,160 --> 00:22:54,199
Speaker 1: but in actions blow up in our minds to become

372
00:22:54,280 --> 00:22:58,840
Speaker 1: these mythic if only so, let's talk about something sort

373
00:22:58,840 --> 00:23:01,520
Speaker 1: of related to that. You've you've created a little bit

374
00:23:01,520 --> 00:23:06,840
Speaker 1: of a stir not too long ago, expressing the belief

375
00:23:06,960 --> 00:23:11,119
Speaker 1: that you know, we're a consumer society and everybody wants

376
00:23:11,200 --> 00:23:13,720
Speaker 1: the two point three kids in the four bedroom house

377
00:23:13,760 --> 00:23:16,600
Speaker 1: and the convertible car and the boat or what have you.

378
00:23:16,720 --> 00:23:24,119
Speaker 1: But your conclusion is experiences are more valuable to individuals

379
00:23:24,359 --> 00:23:28,639
Speaker 1: than these babbles and goods. Explain how you came to

380
00:23:28,680 --> 00:23:31,720
Speaker 1: that conclusion and what it means. That line of research

381
00:23:32,359 --> 00:23:36,240
Speaker 1: stemmed from a finding, probably the biggest finding in the

382
00:23:36,440 --> 00:23:41,520
Speaker 1: now quite extensive research on happiness or well being, which

383
00:23:41,600 --> 00:23:45,879
Speaker 1: is that we have a remarkable capacity to adapt to things,

384
00:23:46,160 --> 00:23:50,199
Speaker 1: and that's a good capacity when bad things happened to us.

385
00:23:50,560 --> 00:23:54,560
Speaker 1: Oh no, I've lost my job, life's going to fall apart. Well,

386
00:23:54,600 --> 00:23:56,560
Speaker 1: that is a bad thing, and you are miserable for

387
00:23:56,600 --> 00:23:59,680
Speaker 1: a while, but we tend to adapt to it. Or

388
00:24:00,359 --> 00:24:02,440
Speaker 1: many people think Oh, I don't think i'd even want

389
00:24:02,440 --> 00:24:05,720
Speaker 1: to live if I lost the use of my legs.

390
00:24:05,840 --> 00:24:08,359
Speaker 1: Let's say, well, it turns out that people who have

391
00:24:08,440 --> 00:24:12,639
Speaker 1: experienced that, yes, they are miserable right away, but they

392
00:24:12,680 --> 00:24:16,800
Speaker 1: adapt and live lives that are as fulfilling as people

393
00:24:16,840 --> 00:24:20,159
Speaker 1: who haven't had that misfortune. Um. And this is just

394
00:24:20,680 --> 00:24:26,639
Speaker 1: the probably biggest fact about the study of happiness, and

395
00:24:26,760 --> 00:24:31,520
Speaker 1: so it applying it to consumption, the things that we buy.

396
00:24:31,600 --> 00:24:34,720
Speaker 1: You know, if I trade in my camera and get Alexis,

397
00:24:34,760 --> 00:24:37,240
Speaker 1: I'll be happier. Yeah, that's true, you will be for

398
00:24:37,280 --> 00:24:39,800
Speaker 1: a little while. Pretty soon it's just a car like

399
00:24:39,880 --> 00:24:41,880
Speaker 1: the other car, and you don't notice it as much.

400
00:24:42,200 --> 00:24:46,720
Speaker 1: And so one challenge for happiness for searchers is, um,

401
00:24:46,760 --> 00:24:49,800
Speaker 1: if adaptation is an enemy of happiness, how do you

402
00:24:49,840 --> 00:24:54,879
Speaker 1: combat that enemy? And the great judgment and decision making

403
00:24:54,960 --> 00:24:57,639
Speaker 1: researcher Robin Dawes had a thought experiment where he was

404
00:24:57,680 --> 00:25:01,000
Speaker 1: talking about this hedonic treadmill. I need more and more

405
00:25:01,040 --> 00:25:04,920
Speaker 1: to get the same level of happiness, he said. Imagine

406
00:25:04,960 --> 00:25:10,240
Speaker 1: you devoted your life not too selfish pursuits accumulating more,

407
00:25:10,440 --> 00:25:13,720
Speaker 1: but selfless ones. You were just trying to do good

408
00:25:13,760 --> 00:25:16,560
Speaker 1: in the world. I don't you know, I don't know

409
00:25:16,600 --> 00:25:18,359
Speaker 1: if he used I think he did use the Mother

410
00:25:18,480 --> 00:25:22,919
Speaker 1: Teresa example. Mother Teresa saves five people this week, she

411
00:25:23,000 --> 00:25:25,359
Speaker 1: probably isn't the next week going f five people. That

412
00:25:25,440 --> 00:25:27,080
Speaker 1: doesn't cut it. I need to save more. I need

413
00:25:27,119 --> 00:25:30,360
Speaker 1: to save more. And it's a compelling thought experiment. And

414
00:25:30,520 --> 00:25:35,159
Speaker 1: it seemed like, um, those kinds of experiences at least

415
00:25:35,560 --> 00:25:40,480
Speaker 1: weren't subject to this adaptation, this idonic treadmill. And so

416
00:25:40,680 --> 00:25:44,240
Speaker 1: the question became, how broadly does that apply to experiences?

417
00:25:44,280 --> 00:25:48,560
Speaker 1: And the hypothesis was, you don't adapt to the money

418
00:25:48,640 --> 00:25:51,320
Speaker 1: that you spend, the pleasure that you get out of

419
00:25:51,359 --> 00:25:54,840
Speaker 1: your experiences, Uh, you don't adapt to them as much,

420
00:25:54,840 --> 00:25:57,720
Speaker 1: which in some ways is paradoxical. That is, most people

421
00:25:57,760 --> 00:26:00,560
Speaker 1: have limited resources, and people often say, you know, I know,

422
00:26:00,640 --> 00:26:04,119
Speaker 1: I would love a vacation right now, but uh, we

423
00:26:04,160 --> 00:26:07,560
Speaker 1: really need a new set of bookcases, or we need

424
00:26:07,640 --> 00:26:10,480
Speaker 1: a new bed, or we need a new car or whatever,

425
00:26:11,080 --> 00:26:13,880
Speaker 1: and at least that will always be there. And that's

426
00:26:13,920 --> 00:26:17,760
Speaker 1: true in a material sense, but psychologically it's the reverse.

427
00:26:17,840 --> 00:26:20,320
Speaker 1: You adapt to the new bookcase that you adapt to

428
00:26:20,880 --> 00:26:24,320
Speaker 1: the upgrade in your car, the things that you buy

429
00:26:24,680 --> 00:26:26,679
Speaker 1: and it turns out you don't adapt as much to

430
00:26:26,720 --> 00:26:30,280
Speaker 1: your experiences. Your experiences change who you are, and you're

431
00:26:30,280 --> 00:26:34,760
Speaker 1: a changed person, and you continue to benefit from that.

432
00:26:35,160 --> 00:26:38,760
Speaker 1: Your experiences connect you to other people in ways that

433
00:26:38,840 --> 00:26:43,479
Speaker 1: you're material goods don't, and that continues to be a

434
00:26:43,520 --> 00:26:46,800
Speaker 1: gift that keeps on giving. Uh. And so it turns

435
00:26:46,800 --> 00:26:49,639
Speaker 1: out uh, and a lot of research has shown this

436
00:26:49,800 --> 00:26:54,320
Speaker 1: that even though the experience comes and goes, possibly in

437
00:26:54,320 --> 00:26:58,480
Speaker 1: a flash, UH, it lives on psychologically and provides more

438
00:26:58,720 --> 00:27:04,360
Speaker 1: enduring satisfaction and enjoyment. How much of the material issues

439
00:27:04,720 --> 00:27:09,960
Speaker 1: are based on the very natural tendency of us to

440
00:27:10,960 --> 00:27:14,639
Speaker 1: not be very good at predicting our own future happiness

441
00:27:14,720 --> 00:27:18,520
Speaker 1: or own future emotional state. It seems we build up

442
00:27:18,600 --> 00:27:21,600
Speaker 1: these things we want be at a house of car, whatever,

443
00:27:22,200 --> 00:27:25,960
Speaker 1: and then when we actually get it, it disappoints our

444
00:27:26,040 --> 00:27:32,440
Speaker 1: expectations of this grand change of lifestyle. Well, you've identified

445
00:27:32,480 --> 00:27:35,840
Speaker 1: a very interesting line of research of it would seem

446
00:27:35,920 --> 00:27:38,040
Speaker 1: like one of the easiest things to predict would be

447
00:27:38,080 --> 00:27:41,240
Speaker 1: how happy we're going to be Making predictions about ourselves.

448
00:27:41,240 --> 00:27:43,840
Speaker 1: Predicting the world, of course, is hard, but it would

449
00:27:43,840 --> 00:27:48,320
Speaker 1: seem to be easy to predict how we're going to react.

450
00:27:48,440 --> 00:27:52,760
Speaker 1: And I think it's just a very compelling, uh line

451
00:27:52,760 --> 00:27:56,240
Speaker 1: of research that now many investigators have done showing that

452
00:27:56,600 --> 00:28:00,159
Speaker 1: we're not so good prognosticators about our own enjoyment. And

453
00:28:00,160 --> 00:28:02,960
Speaker 1: one of the examples that I most like in this

454
00:28:03,080 --> 00:28:09,040
Speaker 1: area is that people feel like, I'll be happier if

455
00:28:09,080 --> 00:28:11,920
Speaker 1: I have a bigger house, and when you first move

456
00:28:11,960 --> 00:28:16,240
Speaker 1: into it, you will be happier again before you adapt

457
00:28:16,320 --> 00:28:21,040
Speaker 1: to this new amount of space, and uh, that becomes

458
00:28:21,080 --> 00:28:23,280
Speaker 1: the norm, this bigger house, and now you feel like

459
00:28:23,280 --> 00:28:26,520
Speaker 1: you need even more and more. Anyway, in order to

460
00:28:27,240 --> 00:28:31,639
Speaker 1: afford that bigger house, people often have to live farther

461
00:28:31,720 --> 00:28:34,240
Speaker 1: away from their job and they have a longer commute.

462
00:28:34,520 --> 00:28:38,240
Speaker 1: And one of the exceptions in this tendency that I described,

463
00:28:38,320 --> 00:28:42,760
Speaker 1: this remarkable capacity for adaptation, is that people don't tend

464
00:28:42,760 --> 00:28:48,080
Speaker 1: to adapt to the trauma of a long commute. And

465
00:28:48,480 --> 00:28:51,640
Speaker 1: it's not surprising why that would be the case. That is,

466
00:28:51,680 --> 00:28:56,040
Speaker 1: it's a it's a a morphous, highly variable thing. It's

467
00:28:56,040 --> 00:28:59,360
Speaker 1: a different version of hell each time you're driving. Sometimes

468
00:28:59,400 --> 00:29:03,160
Speaker 1: it's a snarl over here, other time it's a nasty

469
00:29:03,200 --> 00:29:06,840
Speaker 1: motorist there. It's different, but and you don't adapt to it,

470
00:29:07,120 --> 00:29:10,120
Speaker 1: and so you've made this terrible trade off. You think

471
00:29:10,120 --> 00:29:12,680
Speaker 1: you're gonna be happier with the bigger house. You get

472
00:29:12,720 --> 00:29:15,520
Speaker 1: the bigger house, you are happy for a while than

473
00:29:15,600 --> 00:29:18,520
Speaker 1: you adapt to it. You're saddled with this long commute

474
00:29:18,520 --> 00:29:21,640
Speaker 1: to which you don't adapt, and so you've lost out

475
00:29:21,640 --> 00:29:24,040
Speaker 1: in the bargain. So here's a quote from one of

476
00:29:24,080 --> 00:29:28,440
Speaker 1: your books. People do not hold questionable beliefs because they

477
00:29:28,480 --> 00:29:32,959
Speaker 1: have not been exposed to the relevant evidence. Why do

478
00:29:33,040 --> 00:29:38,400
Speaker 1: they hold questionable beliefs? Well, for a variety of reasons,

479
00:29:38,440 --> 00:29:42,120
Speaker 1: and and the that's what the book was about, was exploring, um,

480
00:29:42,160 --> 00:29:47,560
Speaker 1: those different reasons. Um. And one is that the world

481
00:29:47,600 --> 00:29:51,640
Speaker 1: doesn't play fair. That is, it doesn't it's not doesn't

482
00:29:51,920 --> 00:29:55,840
Speaker 1: put you through a series of controlled experiments. Um. It

483
00:29:56,320 --> 00:29:59,240
Speaker 1: highlights certain data and says, hey, look at me, and

484
00:29:59,560 --> 00:30:04,280
Speaker 1: puts their data in the shadows. And as a result

485
00:30:04,320 --> 00:30:07,840
Speaker 1: of that, the database we have in our head is

486
00:30:07,880 --> 00:30:11,600
Speaker 1: going to be somewhat biased. And we've already talked about

487
00:30:11,600 --> 00:30:14,440
Speaker 1: a few examples of that. That is to say, when

488
00:30:14,480 --> 00:30:17,560
Speaker 1: you switch lines and it slows down, that's gonna leave

489
00:30:17,680 --> 00:30:20,160
Speaker 1: more of an impression than when you switch lines and

490
00:30:20,240 --> 00:30:25,040
Speaker 1: it uh speeds up, and that's going to distort your database. Um.

491
00:30:25,920 --> 00:30:29,560
Speaker 1: Cornell University, when it decides who gets to go there,

492
00:30:29,760 --> 00:30:33,280
Speaker 1: it probably congratulates itself because we look around at the

493
00:30:33,280 --> 00:30:35,440
Speaker 1: student body and they're terrific, and we say, boy, we're

494
00:30:35,440 --> 00:30:38,080
Speaker 1: really good at selecting people. Now, of course, we don't

495
00:30:38,120 --> 00:30:40,960
Speaker 1: really know that because we don't know about the people

496
00:30:41,080 --> 00:30:44,600
Speaker 1: who we rejected who could be there. And maybe they

497
00:30:44,640 --> 00:30:48,040
Speaker 1: do just as well. That is, people self select them

498
00:30:48,320 --> 00:30:51,960
Speaker 1: only really good students by and large, or even applying

499
00:30:52,000 --> 00:30:54,360
Speaker 1: to Cornell, and maybe we don't do as good a

500
00:30:54,440 --> 00:30:57,440
Speaker 1: job is we think the world can't show us the

501
00:30:57,520 --> 00:31:02,640
Speaker 1: people that we didn't accept, and therefore it's um hard

502
00:31:02,760 --> 00:31:05,360
Speaker 1: to really know whether we're doing a good job. But

503
00:31:05,520 --> 00:31:10,240
Speaker 1: the impression is every bit. It's there. We look around

504
00:31:10,240 --> 00:31:12,560
Speaker 1: at the student body. You can't turn that impression off,

505
00:31:12,600 --> 00:31:14,160
Speaker 1: and the students look great, and so we think we

506
00:31:14,160 --> 00:31:17,840
Speaker 1: did a good job. You once, cold confirmation bias, the

507
00:31:17,920 --> 00:31:22,200
Speaker 1: mother of all biases. Tell us why, um boy, that's

508
00:31:22,240 --> 00:31:25,840
Speaker 1: a term that has been in the news all over lately,

509
00:31:26,080 --> 00:31:32,200
Speaker 1: and there there are many things uh that people mean

510
00:31:32,440 --> 00:31:35,680
Speaker 1: by the subject confirmation bias. So let me break that

511
00:31:35,720 --> 00:31:39,240
Speaker 1: down to three things go. There is some nuance in

512
00:31:39,240 --> 00:31:43,520
Speaker 1: in confirmation bias and in the academic study of that.

513
00:31:43,680 --> 00:31:47,000
Speaker 1: So I think what most people think of when they

514
00:31:47,000 --> 00:31:50,560
Speaker 1: think of confirmation bias, if we're talking about Facebook and

515
00:31:50,600 --> 00:31:56,520
Speaker 1: fake news, people go out seeking things that confirm existing beliefs.

516
00:31:56,880 --> 00:32:00,440
Speaker 1: That that's a broad definition of confirmation bias. But in

517
00:32:00,520 --> 00:32:03,120
Speaker 1: some of your research and some of your studies, I

518
00:32:03,200 --> 00:32:07,360
Speaker 1: was kind of fascinated by this. When people are presented

519
00:32:07,400 --> 00:32:12,000
Speaker 1: with a problem, rather than seeking disproving evidence, they go

520
00:32:12,080 --> 00:32:16,479
Speaker 1: out and seek confirming evidence, even though you can end

521
00:32:16,560 --> 00:32:19,760
Speaker 1: up with the exact wrong answer or the same answer

522
00:32:19,800 --> 00:32:22,920
Speaker 1: regardless of the question, based on how it is. The

523
00:32:22,960 --> 00:32:26,160
Speaker 1: example in one of the books is you have a

524
00:32:26,200 --> 00:32:29,200
Speaker 1: court that has to decide which parent to award custody

525
00:32:29,200 --> 00:32:33,400
Speaker 1: of a child. Too parents A is pretty much average

526
00:32:33,440 --> 00:32:37,440
Speaker 1: across all the major characteristics. The court looks at. Parent

527
00:32:37,520 --> 00:32:40,240
Speaker 1: B has some very high points and some very low points,

528
00:32:40,720 --> 00:32:44,280
Speaker 1: depending on how the question is phrased, which parents should

529
00:32:44,280 --> 00:32:48,400
Speaker 1: be awarded custody, which parents should not be awarded custody.

530
00:32:48,880 --> 00:32:52,320
Speaker 1: Everybody ends up looking at the parent with the high

531
00:32:52,360 --> 00:32:55,560
Speaker 1: points and low points, because it's confirming whatever the question

532
00:32:55,720 --> 00:32:58,440
Speaker 1: is asking is that what you were referring to the

533
00:32:58,480 --> 00:33:02,360
Speaker 1: more nuanced aspect. Very, that's great that you broke it

534
00:33:02,400 --> 00:33:05,440
Speaker 1: down that way. That is to say, UM, people are

535
00:33:05,640 --> 00:33:09,400
Speaker 1: very familiar with the idea that we're easier on evidence

536
00:33:09,440 --> 00:33:12,000
Speaker 1: that supports what we want to believe, and we're hard

537
00:33:12,080 --> 00:33:15,360
Speaker 1: on evidence that robuts what we want to believe. And

538
00:33:15,400 --> 00:33:18,480
Speaker 1: that's now part of the confirmation bias. That's the thing

539
00:33:18,560 --> 00:33:22,680
Speaker 1: we recognize that. Great people are also aware of the

540
00:33:22,720 --> 00:33:25,880
Speaker 1: fact that, UM, if I want something to be true,

541
00:33:26,360 --> 00:33:29,920
Speaker 1: I actively go out and look for evidence consistent with it.

542
00:33:30,080 --> 00:33:32,920
Speaker 1: If I want something not to be true, I'll go

543
00:33:33,160 --> 00:33:36,560
Speaker 1: actively look for evidence that's inconsistent with it. We can

544
00:33:36,600 --> 00:33:39,960
Speaker 1: call that the confirmation bias. That's true, there's evidence to

545
00:33:40,040 --> 00:33:44,560
Speaker 1: support that. But the confirmation bias is even more insidious,

546
00:33:44,600 --> 00:33:47,320
Speaker 1: more pervasive than that. That is, even if you don't

547
00:33:47,360 --> 00:33:52,480
Speaker 1: care about what the um the situation or question what

548
00:33:52,560 --> 00:33:55,520
Speaker 1: the answer, what the right answer might be like should

549
00:33:55,520 --> 00:33:58,680
Speaker 1: you award custody to this person or that person, we

550
00:33:58,800 --> 00:34:01,640
Speaker 1: still engage in. So One example that I think makes

551
00:34:01,680 --> 00:34:06,360
Speaker 1: this clear is, UM, suppose I'm hosting a dinner party

552
00:34:06,840 --> 00:34:10,120
Speaker 1: and I tell you you're gonna be sitting next to John.

553
00:34:10,239 --> 00:34:13,799
Speaker 1: I think John's politically conservative. You just might want to

554
00:34:14,000 --> 00:34:15,680
Speaker 1: but you might want to test that out. I'm not sure.

555
00:34:15,960 --> 00:34:18,239
Speaker 1: How would you test that out? Well, you would ask

556
00:34:18,840 --> 00:34:23,680
Speaker 1: conservative oriented questions. You would say, John, don't you think

557
00:34:23,719 --> 00:34:27,839
Speaker 1: the government is too intrusive sometimes? And then he, as

558
00:34:27,840 --> 00:34:32,160
Speaker 1: a conservative, would say yes. But even a liberal would say, well, sometime, sure,

559
00:34:32,200 --> 00:34:36,200
Speaker 1: the government's too intrusive. If I told you, hey, you're

560
00:34:36,200 --> 00:34:38,600
Speaker 1: gonna be sitting next to John, I think he's politically liberal,

561
00:34:38,640 --> 00:34:41,439
Speaker 1: you might want to check that out. You might ask

562
00:34:41,480 --> 00:34:44,480
Speaker 1: a very different question, John, don't you think we need

563
00:34:44,520 --> 00:34:46,759
Speaker 1: to do something about global warming? Don't you think we

564
00:34:46,800 --> 00:34:49,920
Speaker 1: need to do something about income inequality? And if he's liberal,

565
00:34:49,960 --> 00:34:53,040
Speaker 1: he'll say yes. But if you're a conservative, at least

566
00:34:53,040 --> 00:34:55,239
Speaker 1: when it comes to income inequality, you're gonna say, yeah,

567
00:34:55,239 --> 00:34:56,840
Speaker 1: we need to do things about it. Might be different

568
00:34:56,840 --> 00:35:00,399
Speaker 1: things than a liberal, but the thrust is you're gonna

569
00:35:00,400 --> 00:35:05,680
Speaker 1: get answers that support what you initially believed. Um And

570
00:35:05,800 --> 00:35:08,120
Speaker 1: it's so. All of this is to say that the

571
00:35:08,120 --> 00:35:11,680
Speaker 1: confirmation bias runs really deep. And one of my favorite

572
00:35:11,680 --> 00:35:15,200
Speaker 1: examples of this is an old study of conomen divers

573
00:35:15,239 --> 00:35:18,799
Speaker 1: keys excuse me of amos diverse. Key's who this was

574
00:35:18,880 --> 00:35:23,000
Speaker 1: before he was interested in the kind of judgmental biases

575
00:35:23,040 --> 00:35:27,120
Speaker 1: that economists have become interested in. He was just interested

576
00:35:27,120 --> 00:35:31,080
Speaker 1: in judgments of similarity. How do we say that? Why

577
00:35:31,120 --> 00:35:36,879
Speaker 1: do we say that, um, um, having a kid is

578
00:35:37,280 --> 00:35:41,080
Speaker 1: a little bit like having a dog, But having a

579
00:35:41,080 --> 00:35:43,640
Speaker 1: dog is nothing like having a kid. Uh? You know

580
00:35:43,719 --> 00:35:47,560
Speaker 1: why that discrepancy? Why is North Korea a lot like China?

581
00:35:47,640 --> 00:35:50,680
Speaker 1: But China is not really much like North Korea. And

582
00:35:51,200 --> 00:35:54,080
Speaker 1: so he did this study where he asked people which

583
00:35:54,120 --> 00:35:56,560
Speaker 1: two countries this was in the nineteen eighties are more

584
00:35:56,600 --> 00:35:59,640
Speaker 1: similar to one another East Germany and West Germany or Ceylon.

585
00:35:59,680 --> 00:36:02,360
Speaker 1: And the all the respondents no more about East Germany

586
00:36:02,400 --> 00:36:04,759
Speaker 1: and West Germany, so they can think of more reasons,

587
00:36:05,280 --> 00:36:07,839
Speaker 1: more ways in which they are similar. And so they say,

588
00:36:07,840 --> 00:36:10,080
Speaker 1: East Germany and West Germany is more similar. Okay, no

589
00:36:10,160 --> 00:36:12,359
Speaker 1: big deal there, No one's made a problem, No one's

590
00:36:12,360 --> 00:36:15,480
Speaker 1: made in air. Um. He asked another group of participants

591
00:36:15,480 --> 00:36:17,799
Speaker 1: which two countries are more dissimilar to one another, East

592
00:36:17,840 --> 00:36:21,200
Speaker 1: Germany and West Germany. And they say East Germany West

593
00:36:21,200 --> 00:36:23,560
Speaker 1: Germany are more dissimilar to one another because they know

594
00:36:23,600 --> 00:36:26,040
Speaker 1: more about East Germany and West Germany. They can think

595
00:36:26,040 --> 00:36:28,560
Speaker 1: of ways in which they are different, and you end

596
00:36:28,640 --> 00:36:32,320
Speaker 1: up with the paradoxical result, with uh East Germany and

597
00:36:32,320 --> 00:36:36,719
Speaker 1: West Germany being both more similar and more dissimilar. Logically impossible,

598
00:36:37,040 --> 00:36:41,759
Speaker 1: but psychologically the rules of similarity and dissimilarity judgments are

599
00:36:41,800 --> 00:36:44,319
Speaker 1: such that both can seem very compelling. And it's the

600
00:36:44,360 --> 00:36:47,000
Speaker 1: confirmation bias. You asked me how similar they are. I

601
00:36:47,040 --> 00:36:49,560
Speaker 1: look for evidence of similarity, and I can find it

602
00:36:49,680 --> 00:36:51,960
Speaker 1: for the countries I know more about than the countries

603
00:36:52,000 --> 00:36:54,520
Speaker 1: I know less about. You ask me which two countries

604
00:36:54,560 --> 00:36:58,759
Speaker 1: are more dissimilar. I'm looking for evidence of dissimilarity. I

605
00:36:58,800 --> 00:37:01,840
Speaker 1: don't care which to entries are more similar dissimilar. Just

606
00:37:01,880 --> 00:37:05,439
Speaker 1: shows you how deep the confirmation bias runs. So there

607
00:37:05,520 --> 00:37:08,040
Speaker 1: was a slip of the tongue that you referenced where

608
00:37:08,080 --> 00:37:11,320
Speaker 1: you said somebody said I'll see it when I believe it,

609
00:37:11,440 --> 00:37:13,600
Speaker 1: as opposed to saying I believe it when I see it.

610
00:37:13,920 --> 00:37:18,120
Speaker 1: But really there's some Freudian truth in that i'll see

611
00:37:18,120 --> 00:37:21,719
Speaker 1: it when I believe it. Tells us how much our

612
00:37:21,800 --> 00:37:25,640
Speaker 1: own um, ownership of ideas and desire to see things

613
00:37:25,640 --> 00:37:30,879
Speaker 1: we already believe actually impacts that. How does this manifest

614
00:37:30,960 --> 00:37:35,759
Speaker 1: itself in the field of investing? How do you see

615
00:37:35,840 --> 00:37:42,839
Speaker 1: confirmation bias impacting the way people put capital at risk? Um, Well,

616
00:37:42,840 --> 00:37:45,560
Speaker 1: it plays out there the way it plays out everywhere.

617
00:37:45,600 --> 00:37:48,080
Speaker 1: That is to say, or let's back up a step

618
00:37:48,200 --> 00:37:50,960
Speaker 1: that you know, when you cite that quote, it can

619
00:37:51,120 --> 00:37:54,640
Speaker 1: sound silly in some ways, it does sound silly. On

620
00:37:54,680 --> 00:37:58,560
Speaker 1: the other hand, what job does our brain have. It's

621
00:37:58,640 --> 00:38:01,280
Speaker 1: to make sense of the world. And we draw upon

622
00:38:01,320 --> 00:38:05,799
Speaker 1: every possible cues that would make that job easier. And

623
00:38:06,000 --> 00:38:07,920
Speaker 1: one cue that we draw upon is what we already know,

624
00:38:08,120 --> 00:38:11,359
Speaker 1: or what we already think we know. And therefore what

625
00:38:11,480 --> 00:38:15,319
Speaker 1: we know has to influence new information that comes in.

626
00:38:15,360 --> 00:38:18,319
Speaker 1: And so if we have a strong prior belief that

627
00:38:18,480 --> 00:38:23,560
Speaker 1: in this industry is better positioned for the environment we

628
00:38:23,640 --> 00:38:27,000
Speaker 1: now face rather than that industry, of course we should

629
00:38:27,560 --> 00:38:32,040
Speaker 1: take that into account. Pre existing beliefs, Uh, if they're

630
00:38:32,120 --> 00:38:36,319
Speaker 1: based on a solid foundation, they should influence our evaluation

631
00:38:36,360 --> 00:38:39,799
Speaker 1: of new information. So let me ask you, um a

632
00:38:39,880 --> 00:38:43,680
Speaker 1: question about the money illusion. Why is it that we

633
00:38:43,760 --> 00:38:49,120
Speaker 1: have these unfounded beliefs about values of different things? Define

634
00:38:49,200 --> 00:38:53,040
Speaker 1: what the money illusion is? The money illusions really interesting

635
00:38:53,800 --> 00:38:59,719
Speaker 1: to psychologists because it's another version of uh, something you

636
00:38:59,760 --> 00:39:03,120
Speaker 1: see all over the place, which is a difficulty we

637
00:39:03,200 --> 00:39:09,120
Speaker 1: have in taking context into account. And so in psychology

638
00:39:09,120 --> 00:39:13,520
Speaker 1: there's this phenomenon known as the fundamental attribution air that UM,

639
00:39:13,640 --> 00:39:16,640
Speaker 1: if you see, UM, a dad and a kid in

640
00:39:16,719 --> 00:39:19,000
Speaker 1: line at the grocery store and the dad yells at

641
00:39:19,000 --> 00:39:22,400
Speaker 1: the kid, you can't help but think that's a mean father.

642
00:39:23,360 --> 00:39:25,960
Speaker 1: We don't know what the context was that led up

643
00:39:25,960 --> 00:39:28,520
Speaker 1: to it, what's gone on in the person's life, and

644
00:39:28,640 --> 00:39:31,560
Speaker 1: how exceptional that might have been the only time that

645
00:39:31,600 --> 00:39:35,160
Speaker 1: the dad ever yelled at his kid. But we can't

646
00:39:35,160 --> 00:39:37,879
Speaker 1: help but look at the stimulus in front of us

647
00:39:37,920 --> 00:39:43,560
Speaker 1: and draw conclusions. Um. And with respect to the money illusion,

648
00:39:43,680 --> 00:39:47,480
Speaker 1: twenty thous dollars sounds a lot more than ten thousand dollars,

649
00:39:47,480 --> 00:39:50,319
Speaker 1: and we can lose sight of the context, that is

650
00:39:50,360 --> 00:39:53,960
Speaker 1: to say, how much has inflation changed from when you

651
00:39:54,000 --> 00:39:59,520
Speaker 1: had ten thousand to UH. They're obviously circumstances in which

652
00:40:00,120 --> 00:40:02,960
Speaker 1: real dollars, the twenty thousand is less than the ten

653
00:40:03,000 --> 00:40:06,160
Speaker 1: thousand used to have, but it's hard to to get

654
00:40:06,200 --> 00:40:10,160
Speaker 1: over that first impression of just being taken by the

655
00:40:10,160 --> 00:40:14,080
Speaker 1: the monetary value and to ignore context. What is the

656
00:40:14,160 --> 00:40:18,800
Speaker 1: issue with mental accounting? Well, economists tell us UH. Decision

657
00:40:18,840 --> 00:40:20,680
Speaker 1: makers tell us, if we want to make the best

658
00:40:20,680 --> 00:40:23,480
Speaker 1: decisions with our money, we should think of all of

659
00:40:23,520 --> 00:40:28,160
Speaker 1: our assets and liabilities in terms of one overall integrated

660
00:40:28,360 --> 00:40:31,200
Speaker 1: balance sheet. And in fact that turns out to be true,

661
00:40:31,200 --> 00:40:34,840
Speaker 1: we will make better decisions that way. Great, But those

662
00:40:35,400 --> 00:40:38,439
Speaker 1: we don't have the kinds of minds that UH either

663
00:40:38,480 --> 00:40:41,560
Speaker 1: are capable of doing that or inclined to do that. Instead,

664
00:40:41,600 --> 00:40:46,040
Speaker 1: we put money into different accounts um and we treat

665
00:40:46,080 --> 00:40:49,680
Speaker 1: it differently. We treat money differently depending upon where it

666
00:40:49,840 --> 00:40:53,880
Speaker 1: came from, how we got it, um and. There are

667
00:40:53,920 --> 00:40:57,279
Speaker 1: all sorts of examples. It's very easy to illustrate. If

668
00:40:57,360 --> 00:41:01,839
Speaker 1: you received an inheritance UM and aunt, let's say, who

669
00:41:01,920 --> 00:41:05,839
Speaker 1: was very careful with her money, You're probably gonna be

670
00:41:05,920 --> 00:41:10,080
Speaker 1: more careful with that money than if your aunt was

671
00:41:10,400 --> 00:41:14,720
Speaker 1: a wild and crazy person who liked to spread money around.

672
00:41:14,840 --> 00:41:18,080
Speaker 1: You're gonna feel like I should spread this around. Money

673
00:41:18,160 --> 00:41:23,759
Speaker 1: comes with almost a personality um and. People often talk

674
00:41:23,840 --> 00:41:27,400
Speaker 1: about this in the context of getting a smallish bonus.

675
00:41:27,480 --> 00:41:30,440
Speaker 1: You didn't expect you get a tax return, and it's small.

676
00:41:30,880 --> 00:41:33,880
Speaker 1: It feels like, Hey, this is great, let's go spend it,

677
00:41:33,920 --> 00:41:37,120
Speaker 1: And people do, and they often spend it several times. Uh.

678
00:41:37,280 --> 00:41:40,880
Speaker 1: Just it's used to justify going to this restaurant you

679
00:41:40,920 --> 00:41:44,240
Speaker 1: always wanted to go to, getting tickets to the Celtic

680
00:41:44,280 --> 00:41:47,160
Speaker 1: game or whatever, and you've spent it several times. If

681
00:41:47,239 --> 00:41:50,640
Speaker 1: in fact it were a big inheritance, it comes with

682
00:41:50,680 --> 00:41:53,719
Speaker 1: a lot of waitiness associated with it, and you're even

683
00:41:53,760 --> 00:41:55,640
Speaker 1: though you have more money, you're less likely to spend

684
00:41:55,719 --> 00:41:59,439
Speaker 1: it on that restaurant or those Celtic tickets and instead, um,

685
00:42:00,400 --> 00:42:03,240
Speaker 1: do something serious with it, because it's a serious amount

686
00:42:03,280 --> 00:42:06,480
Speaker 1: of money. In white more people make big money mistakes.

687
00:42:07,040 --> 00:42:11,680
Speaker 1: You tell the joke of the honeymooners in Vegas. Uh,

688
00:42:11,719 --> 00:42:15,760
Speaker 1: with mental accounting, the groom, while the wife is sleeping,

689
00:42:15,880 --> 00:42:19,200
Speaker 1: takes five dollars and goes down to the roulette table.

690
00:42:19,280 --> 00:42:23,040
Speaker 1: Share that story. I love that story. Uh well, do

691
00:42:23,120 --> 00:42:25,400
Speaker 1: you recall it? Yeah, of course I recall. It's a

692
00:42:25,480 --> 00:42:29,040
Speaker 1: it's a it's a long story. Takes the money goes

693
00:42:29,160 --> 00:42:36,160
Speaker 1: you know, bets here wins long double animulation of money,

694
00:42:36,280 --> 00:42:40,800
Speaker 1: UM that ultimately goes one round too far and loses

695
00:42:40,840 --> 00:42:43,160
Speaker 1: when he returns. How honey how did you do? Oh

696
00:42:43,239 --> 00:42:46,040
Speaker 1: not bad. I lost five dollars right the last round

697
00:42:46,239 --> 00:42:48,440
Speaker 1: it doubled and doubled and doubled. It was tens of

698
00:42:48,480 --> 00:42:50,240
Speaker 1: millions of dollars. He had to go to a different

699
00:42:50,280 --> 00:42:54,000
Speaker 1: hotel that would take the bigger stakes, and he bets it,

700
00:42:54,080 --> 00:42:57,799
Speaker 1: loses it all. Honey, I lost five dollars. I love

701
00:42:57,880 --> 00:42:59,640
Speaker 1: that story. I find that and you tell it better

702
00:42:59,640 --> 00:43:04,080
Speaker 1: than I. We have been speaking with Professor Tom Gilovich

703
00:43:04,200 --> 00:43:07,799
Speaker 1: of Cornell University. If you enjoy this conversation, be sure

704
00:43:07,800 --> 00:43:10,400
Speaker 1: and check out our podcast extras when we keep the

705
00:43:10,400 --> 00:43:15,080
Speaker 1: tape rolling and continue discussing all things cognitive. Be sure

706
00:43:15,080 --> 00:43:17,440
Speaker 1: and check out my daily column. You can find that

707
00:43:17,520 --> 00:43:20,319
Speaker 1: on Bloomberg View dot com. You can follow me on

708
00:43:20,320 --> 00:43:23,960
Speaker 1: Twitter at rit Halts. We love your comments, feedback and

709
00:43:24,080 --> 00:43:28,800
Speaker 1: suggestions right to us at m IB podcast at Bloomberg

710
00:43:28,880 --> 00:43:32,520
Speaker 1: dot net. I'm Barry rid Halts. You're listening to Master's

711
00:43:32,560 --> 00:43:50,120
Speaker 1: in Business on Bloomberg Radio. Welcome to the podcast. Thank you,

712
00:43:50,440 --> 00:43:53,279
Speaker 1: as Tom. I feel odd calling you Tom because I

713
00:43:53,320 --> 00:43:57,040
Speaker 1: know you as Professor Gillibit. I only say Thomas when

714
00:43:57,080 --> 00:43:59,319
Speaker 1: I've made him With Thomas, I've made a mistake of

715
00:43:59,360 --> 00:44:03,080
Speaker 1: something get that backhand into the net. So so there's

716
00:44:03,120 --> 00:44:05,000
Speaker 1: a lot of stuff I want to go over with

717
00:44:05,040 --> 00:44:08,719
Speaker 1: you that we didn't get to during the broadcast. But

718
00:44:08,840 --> 00:44:14,439
Speaker 1: I have to start with the discussion about experiences over

719
00:44:15,080 --> 00:44:20,480
Speaker 1: consumer um material goods, because there are some stories from

720
00:44:20,560 --> 00:44:24,400
Speaker 1: that really have stayed with me, that have experiences that

721
00:44:24,440 --> 00:44:26,600
Speaker 1: have stayed with me that have forced me down that

722
00:44:26,719 --> 00:44:30,120
Speaker 1: rabbit hole. A little bit about five years ago, my

723
00:44:30,120 --> 00:44:33,439
Speaker 1: wife and I were on vacation in St. Lucia and

724
00:44:33,560 --> 00:44:36,880
Speaker 1: one of the things that was an option at the hotel.

725
00:44:36,920 --> 00:44:38,360
Speaker 1: We were all the way on the north part of

726
00:44:38,360 --> 00:44:41,200
Speaker 1: the island and the Grand Tetons are on the south part,

727
00:44:41,760 --> 00:44:45,080
Speaker 1: and you could rent a sailboat and a crew, a

728
00:44:45,120 --> 00:44:48,400
Speaker 1: two man crew for the day. And at the time

729
00:44:48,440 --> 00:44:50,840
Speaker 1: it seemed like an ungodly amount of money. It was

730
00:44:50,920 --> 00:44:53,600
Speaker 1: less than a thousand dollars, but it's a day. That's

731
00:44:53,600 --> 00:44:56,440
Speaker 1: a lot of money, and I ended up just grinding

732
00:44:56,520 --> 00:45:00,160
Speaker 1: my teeth and doing it, and that experience she Ill

733
00:45:00,239 --> 00:45:04,640
Speaker 1: talks about consistently. I could have brought a thousand dollar

734
00:45:04,719 --> 00:45:07,960
Speaker 1: piece of jewelry or spent it on any babble, would

735
00:45:08,000 --> 00:45:12,160
Speaker 1: never have gotten the ongoing mileage out of that, and

736
00:45:12,320 --> 00:45:16,160
Speaker 1: that's my favorite example of that. On the other hand,

737
00:45:16,600 --> 00:45:21,800
Speaker 1: there are purchases I've made where I continue to derive

738
00:45:22,560 --> 00:45:28,600
Speaker 1: years later pleasure from the consumer purchase. And my best

739
00:45:28,640 --> 00:45:33,280
Speaker 1: explanation for that is has to do with my expectations

740
00:45:33,320 --> 00:45:35,000
Speaker 1: of what I'm gonna get. And I'm gonna give you

741
00:45:35,400 --> 00:45:39,400
Speaker 1: two quick examples. Uh. We we moved houses, and we

742
00:45:39,480 --> 00:45:42,960
Speaker 1: lived in a fairly suburban neighborhood where you feel like

743
00:45:43,000 --> 00:45:46,560
Speaker 1: you're the center square um with houses next to you,

744
00:45:46,640 --> 00:45:49,920
Speaker 1: behind you forward, And we hadn't plans on moving, and

745
00:45:49,960 --> 00:45:54,920
Speaker 1: this really fascinating modern house popped up. My wife trolls

746
00:45:55,000 --> 00:45:58,560
Speaker 1: Zillo all the time, and we ended up. It's adjacent

747
00:45:58,640 --> 00:46:02,600
Speaker 1: to a big preserve, so during in the winter, I

748
00:46:02,600 --> 00:46:05,160
Speaker 1: could see the house across the street from me on

749
00:46:05,239 --> 00:46:08,120
Speaker 1: the other about a hundred yards away. But during the summer,

750
00:46:08,640 --> 00:46:11,520
Speaker 1: I feel like I'm living in a forest and you

751
00:46:11,560 --> 00:46:14,719
Speaker 1: can't see any neighbors and I and I've only been

752
00:46:14,760 --> 00:46:18,200
Speaker 1: there three years, but I still walk around and think,

753
00:46:18,239 --> 00:46:20,600
Speaker 1: I can't believe we live here. I'm astonished by it.

754
00:46:21,480 --> 00:46:27,480
Speaker 1: That house has not hit the adaptation level yet. But

755
00:46:27,640 --> 00:46:29,600
Speaker 1: when we're in the old house and I was shopping

756
00:46:29,600 --> 00:46:32,320
Speaker 1: for new stereo. We were in that house for seven years.

757
00:46:32,360 --> 00:46:36,680
Speaker 1: My wife was constantly canceling the stairs. No, no, don't

758
00:46:36,719 --> 00:46:40,080
Speaker 1: spend that money on this, and ultimately said, tell you what,

759
00:46:40,200 --> 00:46:42,799
Speaker 1: when you moved. We moved to a new house one day,

760
00:46:43,120 --> 00:46:46,520
Speaker 1: get whatever you want. So we move and I remind

761
00:46:46,600 --> 00:46:49,839
Speaker 1: her of this and go out and spent probably too

762
00:46:49,880 --> 00:46:53,280
Speaker 1: much money on an audio system, and truth be told,

763
00:46:53,880 --> 00:46:57,600
Speaker 1: I hardly ever use it. Who has time to pop

764
00:46:57,640 --> 00:47:01,320
Speaker 1: in a CD and sit and listen uninterrupted to music

765
00:47:01,800 --> 00:47:05,439
Speaker 1: for forty five minutes? It just doesn't happen. And I'm

766
00:47:05,560 --> 00:47:11,279
Speaker 1: shocked at how little actual pleasure I've derived from that

767
00:47:11,360 --> 00:47:15,840
Speaker 1: purchase versus other things cars and houses and what have you.

768
00:47:16,200 --> 00:47:21,680
Speaker 1: And it it's always intriguing me how adaptable are we

769
00:47:21,760 --> 00:47:26,359
Speaker 1: to things and how much is it reliance upon our

770
00:47:26,440 --> 00:47:31,319
Speaker 1: own expectations or whether they're too high or too low. Well,

771
00:47:31,440 --> 00:47:34,600
Speaker 1: we're doing research. It's interesting that you raise that question.

772
00:47:34,640 --> 00:47:40,920
Speaker 1: We're doing research right now on people's reasons for making

773
00:47:40,960 --> 00:47:44,680
Speaker 1: certain purchases, and are those reasons born out in the

774
00:47:44,800 --> 00:47:49,840
Speaker 1: particular question is um We saw in our other research

775
00:47:50,000 --> 00:47:54,040
Speaker 1: that people often by certain material possessions with the thought

776
00:47:54,560 --> 00:47:57,640
Speaker 1: that that's going to give a boost to their social life.

777
00:47:57,640 --> 00:48:01,040
Speaker 1: The prototypical example is, yeah, and need to get forty

778
00:48:01,360 --> 00:48:05,280
Speaker 1: flat screen TV, UM because I want to have everyone

779
00:48:05,320 --> 00:48:09,000
Speaker 1: over for the Oscars and the super Bowl. And uh,

780
00:48:09,320 --> 00:48:13,080
Speaker 1: sometimes that does happen, but people end up watching the

781
00:48:13,080 --> 00:48:16,359
Speaker 1: Oscars by themselves or the super Bowl by themselves more

782
00:48:16,400 --> 00:48:20,960
Speaker 1: often than they expect. So the prediction is that you

783
00:48:21,040 --> 00:48:24,040
Speaker 1: buy certain material things with the expectation it's going to

784
00:48:24,120 --> 00:48:26,680
Speaker 1: connect you to other people, and that turns out to

785
00:48:26,800 --> 00:48:30,520
Speaker 1: be true less often than you expect, Whereas the experience

786
00:48:31,360 --> 00:48:34,320
Speaker 1: thinks some of those you buy also with an eye

787
00:48:34,360 --> 00:48:37,120
Speaker 1: toward doing it with other people, and those tend to

788
00:48:37,120 --> 00:48:41,440
Speaker 1: get confirmed more and that's part of the experiential advantage. UM.

789
00:48:41,560 --> 00:48:44,200
Speaker 1: So we have several studies in the hopper are ready

790
00:48:44,200 --> 00:48:47,480
Speaker 1: to run on on that subject. And I think your

791
00:48:47,840 --> 00:48:50,799
Speaker 1: example of buying this place that you love so much

792
00:48:50,840 --> 00:48:54,520
Speaker 1: and it hasn't diminished you right away talked about we

793
00:48:54,640 --> 00:48:57,040
Speaker 1: moved to this neighborhood and we're sort of connected to

794
00:48:57,160 --> 00:49:00,799
Speaker 1: the and like the other people around you. And that's

795
00:49:00,800 --> 00:49:04,720
Speaker 1: a case where you bought it for a social reason

796
00:49:04,760 --> 00:49:07,160
Speaker 1: and it turns out to have been confirmed and you've

797
00:49:07,200 --> 00:49:11,319
Speaker 1: got the great hiking ability and vista in the back. Um.

798
00:49:11,600 --> 00:49:14,799
Speaker 1: Compare that to a frequent motivation of people buying a

799
00:49:14,840 --> 00:49:17,239
Speaker 1: new house, So we need more square footage, or we

800
00:49:17,280 --> 00:49:20,960
Speaker 1: need three bathrooms rather than two. Well, people with big

801
00:49:20,960 --> 00:49:23,239
Speaker 1: families used to live in really tiny houses and never

802
00:49:23,320 --> 00:49:26,239
Speaker 1: really one They yeah, and they functioned just fine. We

803
00:49:26,280 --> 00:49:28,920
Speaker 1: could function just fine. And those are the kinds of

804
00:49:28,960 --> 00:49:32,160
Speaker 1: things that people adapt to. But if you move your

805
00:49:32,200 --> 00:49:36,120
Speaker 1: house and suddenly you're in a better neighborhood in the woods,

806
00:49:36,200 --> 00:49:39,080
Speaker 1: I'm literally you know, there are foxes running down the

807
00:49:39,080 --> 00:49:41,560
Speaker 1: street and we sort of I live in Long Island.

808
00:49:41,600 --> 00:49:43,800
Speaker 1: There was not supposed to be deer in Nassau County.

809
00:49:44,120 --> 00:49:47,160
Speaker 1: A deer went bounding across the front yard the other day.

810
00:49:47,680 --> 00:49:49,319
Speaker 1: I was trying to figure out where the dogs were

811
00:49:49,360 --> 00:49:51,880
Speaker 1: going crazy? Why is there a deer in our street?

812
00:49:51,920 --> 00:49:55,560
Speaker 1: That it never ceases to amaze me? And those bigger

813
00:49:55,600 --> 00:49:58,880
Speaker 1: TV you adapt to three months you don't even notice

814
00:49:58,880 --> 00:50:01,279
Speaker 1: the size of the TV. Whereas for you, it's gonna

815
00:50:01,320 --> 00:50:03,239
Speaker 1: be a fox one day, it's going to be a

816
00:50:03,360 --> 00:50:06,840
Speaker 1: sunset another day, a sunrise a third day, et cetera.

817
00:50:07,040 --> 00:50:09,640
Speaker 1: It's going to be a different little gift each time

818
00:50:10,160 --> 00:50:14,440
Speaker 1: that has provided this continuing enjoyment that you've received. I'm

819
00:50:14,440 --> 00:50:21,520
Speaker 1: curious as to how our Since you've discussed investing and

820
00:50:22,520 --> 00:50:28,200
Speaker 1: monetary issues, financial issues, I'm curious how our own recognition

821
00:50:28,280 --> 00:50:30,560
Speaker 1: of whether or not we're getting a bargain or not.

822
00:50:30,719 --> 00:50:34,920
Speaker 1: What whether you know there are lots of fast, beautiful cars.

823
00:50:35,680 --> 00:50:38,160
Speaker 1: I have a hard time wrapping my head around walking

824
00:50:38,200 --> 00:50:41,520
Speaker 1: into a Ferrari dealer and saying, here's two hundred and

825
00:50:41,560 --> 00:50:44,400
Speaker 1: fifty thousand dollars, I'll take the red one. Seems like

826
00:50:44,440 --> 00:50:47,359
Speaker 1: an awful lot of money. On the other hands, when

827
00:50:47,400 --> 00:50:49,200
Speaker 1: if you pick up a car that's two or three

828
00:50:49,239 --> 00:50:52,640
Speaker 1: years old and it's half of the new price, it

829
00:50:52,760 --> 00:50:56,800
Speaker 1: seems like a little more of a rational um decision.

830
00:50:56,920 --> 00:50:59,760
Speaker 1: I'm curious if you've looked at how people feel about

831
00:51:00,360 --> 00:51:03,480
Speaker 1: the cost or relative cost of what the purchasing to

832
00:51:03,680 --> 00:51:06,839
Speaker 1: its value, if that has any impact on how they

833
00:51:06,960 --> 00:51:12,160
Speaker 1: either adapt or continue to enjoy whatever that consumer bubble is. Yeah, sure,

834
00:51:12,280 --> 00:51:15,319
Speaker 1: there's um. You know, we get utility from all sorts

835
00:51:15,360 --> 00:51:18,279
Speaker 1: of things, and there's utility from the simple knowledge that

836
00:51:18,360 --> 00:51:22,720
Speaker 1: it's a deal that that provides pleasure, and that fact

837
00:51:22,840 --> 00:51:28,680
Speaker 1: is related to I think the single biggest psychological fact

838
00:51:28,719 --> 00:51:33,560
Speaker 1: about human beings. It's so simple. It's such a simple idea,

839
00:51:34,239 --> 00:51:37,840
Speaker 1: but it's really powerful. Um says something about us, and

840
00:51:38,000 --> 00:51:41,680
Speaker 1: UM it's sort of fallen to psychologists to remind the

841
00:51:41,719 --> 00:51:45,080
Speaker 1: world of this. Economists often want to say that, well,

842
00:51:45,120 --> 00:51:47,640
Speaker 1: let's just incentivize it and then we'll get more of

843
00:51:47,680 --> 00:51:51,800
Speaker 1: this behavior. Incentives work pretty well for things, but people

844
00:51:51,840 --> 00:51:55,520
Speaker 1: don't respond to the incentives themselves. They respond to the

845
00:51:55,560 --> 00:51:59,680
Speaker 1: meaning that they assigned to uh those incentives. And more broadly,

846
00:51:59,719 --> 00:52:03,600
Speaker 1: we don't respond to the stimuli we encounter. We respond

847
00:52:03,680 --> 00:52:06,920
Speaker 1: to the meaning that we assigned to them. Man, there's

848
00:52:06,960 --> 00:52:11,320
Speaker 1: so much flexibility in terms of how we construe things

849
00:52:11,960 --> 00:52:17,120
Speaker 1: that um, the same purchase thought of for whatever reason

850
00:52:17,239 --> 00:52:20,200
Speaker 1: as a deal versus not thought that way. It just

851
00:52:21,200 --> 00:52:24,600
Speaker 1: makes all the difference in terms of how much how

852
00:52:24,640 --> 00:52:27,960
Speaker 1: we feel about it, how much continued enjoyment we get it. Uh.

853
00:52:28,040 --> 00:52:32,719
Speaker 1: The same thing about virtually everything in in our lives. UH.

854
00:52:32,760 --> 00:52:37,319
Speaker 1: And therefore, the determinants of understanding how people are going

855
00:52:37,360 --> 00:52:41,240
Speaker 1: to interpret a given stimulus is the key to understanding

856
00:52:41,239 --> 00:52:42,920
Speaker 1: how people are going to behave. It's the key to

857
00:52:43,120 --> 00:52:45,719
Speaker 1: running a successful company, and certainly it's the key to

858
00:52:45,840 --> 00:52:50,799
Speaker 1: running a successful political campaign. So the other issue I

859
00:52:50,840 --> 00:52:53,880
Speaker 1: wanted to bring up relevant to this was the endowment

860
00:52:53,920 --> 00:52:58,439
Speaker 1: effects and how people place more value on things their own.

861
00:52:58,520 --> 00:53:01,319
Speaker 1: And and we'll stay with cars for a moment. My

862
00:53:01,520 --> 00:53:05,600
Speaker 1: favorite experience with this is speaking someone. I get this

863
00:53:05,640 --> 00:53:08,640
Speaker 1: all the time. Hey, you're a car guy. I'm thinking

864
00:53:08,680 --> 00:53:11,719
Speaker 1: about buying this or that. The most recent time it

865
00:53:11,800 --> 00:53:14,040
Speaker 1: was I'm looking at a cut. You mentioned a camera

866
00:53:14,640 --> 00:53:19,360
Speaker 1: or the Honda Accord. Which which car do you suggest? Um?

867
00:53:19,600 --> 00:53:21,880
Speaker 1: I get And I've learned it's like when someone says,

868
00:53:22,280 --> 00:53:24,439
Speaker 1: I'm thinking of leaving my wife, what do you think?

869
00:53:24,760 --> 00:53:26,799
Speaker 1: Whatever answer is going to come back to bite you.

870
00:53:27,160 --> 00:53:29,600
Speaker 1: So I usually say, those are both really good cars.

871
00:53:30,080 --> 00:53:32,319
Speaker 1: What's your experience with each of them? And I'll get

872
00:53:32,320 --> 00:53:34,520
Speaker 1: a laundry list of all the great things about the

873
00:53:34,600 --> 00:53:38,120
Speaker 1: Camera and then all the great things about the Accord. Well,

874
00:53:38,280 --> 00:53:40,239
Speaker 1: I don't think you could do poorly with either of them.

875
00:53:40,320 --> 00:53:43,200
Speaker 1: Let me know what you decide. Six months later, you

876
00:53:43,239 --> 00:53:47,120
Speaker 1: speak to that person and whichever car they picked, I'm

877
00:53:47,160 --> 00:53:50,120
Speaker 1: so glad I picked the Accord. It is the greatest

878
00:53:50,200 --> 00:53:53,279
Speaker 1: thing since that was that's a boring plane. This is

879
00:53:53,360 --> 00:53:56,640
Speaker 1: really it doesn't matter which one they select, but whicheveryone

880
00:53:56,760 --> 00:54:00,000
Speaker 1: they select, that's the one, that's the winning car, that's

881
00:54:00,000 --> 00:54:03,120
Speaker 1: the one they should have picked. It's amazing how what

882
00:54:03,239 --> 00:54:05,640
Speaker 1: was a coin to us at one point? Oh no,

883
00:54:05,760 --> 00:54:09,000
Speaker 1: this was the only way the decision could have gone. Yeah,

884
00:54:09,000 --> 00:54:11,200
Speaker 1: And I think there's an important lesson there that is,

885
00:54:11,239 --> 00:54:14,879
Speaker 1: when we find ourselves in these circumstances where, oh my god,

886
00:54:14,920 --> 00:54:18,120
Speaker 1: this is a weighty decision, I don't know, uh, you know,

887
00:54:18,680 --> 00:54:20,560
Speaker 1: six of one half dozen of the other. Well, if

888
00:54:20,600 --> 00:54:23,719
Speaker 1: you find yourself where you really are torn and there

889
00:54:23,719 --> 00:54:28,200
Speaker 1: are strong arguments either way it's a close call, then

890
00:54:28,280 --> 00:54:31,799
Speaker 1: just pick one. And what you just described, all that

891
00:54:31,920 --> 00:54:35,359
Speaker 1: psychology that uh you bring to bear to make you

892
00:54:35,440 --> 00:54:38,680
Speaker 1: happy with what you've chosen, will be brought to bear

893
00:54:38,719 --> 00:54:42,640
Speaker 1: and you'll be pretty happy with it. Um. And those decisions,

894
00:54:42,680 --> 00:54:45,719
Speaker 1: because it's six of one half dozen of the other,

895
00:54:45,760 --> 00:54:49,480
Speaker 1: they feel like hard ones, but in reality they aren't.

896
00:54:49,640 --> 00:54:54,960
Speaker 1: Um because if it's so much uh an unbalanced decision,

897
00:54:55,320 --> 00:54:56,920
Speaker 1: you can go either way and you'll end up feeling

898
00:54:56,920 --> 00:55:00,840
Speaker 1: pretty good about it. So there's a concept discussed, and

899
00:55:01,360 --> 00:55:03,640
Speaker 1: I believe it was how we know what isn't so

900
00:55:04,400 --> 00:55:07,440
Speaker 1: that I have to bring up because It just cracks

901
00:55:07,480 --> 00:55:10,440
Speaker 1: me up so much. We're all familiar with the Kubler

902
00:55:10,560 --> 00:55:14,640
Speaker 1: Ross five Stages of grief that has been, you know,

903
00:55:14,880 --> 00:55:20,319
Speaker 1: dogma for years. Your research more or less fines that

904
00:55:20,760 --> 00:55:24,000
Speaker 1: it's pretty meaningless and there is no data that backs

905
00:55:24,040 --> 00:55:26,680
Speaker 1: it up. Am I overstating that or is that a

906
00:55:26,680 --> 00:55:31,279
Speaker 1: fair assessment of that? I talk about that in the

907
00:55:31,280 --> 00:55:33,239
Speaker 1: book in the same chapter that I talked about the

908
00:55:33,280 --> 00:55:35,400
Speaker 1: hot hand, And both of them are reflection of the

909
00:55:35,440 --> 00:55:38,880
Speaker 1: fact that, Um, we tend to see more patterns in

910
00:55:38,920 --> 00:55:42,719
Speaker 1: the world than are actually there. Um. We've got this

911
00:55:42,880 --> 00:55:48,080
Speaker 1: incredible pattern detection machinery in our heads and it goes

912
00:55:48,120 --> 00:55:52,080
Speaker 1: out and finds patterns, but no system is perfect, and

913
00:55:52,120 --> 00:55:55,560
Speaker 1: so it overshoots sometimes, and so we see faces and

914
00:55:55,640 --> 00:55:58,319
Speaker 1: clouds and a man on the moon and canals on

915
00:55:58,440 --> 00:56:02,640
Speaker 1: Mars that aren't there are. UM and the hot hand,

916
00:56:03,160 --> 00:56:06,359
Speaker 1: Uh is you are seeing more streakiness than is really

917
00:56:06,400 --> 00:56:11,560
Speaker 1: There's another manifestation of that. Psychologists are very fond of

918
00:56:11,640 --> 00:56:15,200
Speaker 1: stage theories that we go through the systematic stages to

919
00:56:15,760 --> 00:56:18,960
Speaker 1: take us from the starting point to the endpoint. Kubler

920
00:56:19,040 --> 00:56:22,560
Speaker 1: Ross's is just one of them, and it I'm not

921
00:56:22,600 --> 00:56:25,959
Speaker 1: saying it's random. That is to say, people willy narly

922
00:56:26,040 --> 00:56:28,879
Speaker 1: go through all sorts of stages at different times. Maybe

923
00:56:28,880 --> 00:56:33,120
Speaker 1: there is some order there, but certainly there's less than people.

924
00:56:33,239 --> 00:56:35,520
Speaker 1: People are complicated and their act in all sorts of

925
00:56:35,560 --> 00:56:38,600
Speaker 1: different ways. And there is a lot of research on

926
00:56:38,760 --> 00:56:42,760
Speaker 1: people's reactions to grief, not so much about in reactions

927
00:56:42,800 --> 00:56:45,600
Speaker 1: to their own death, and there's research on that too, um.

928
00:56:45,760 --> 00:56:48,200
Speaker 1: And people are all over the map. Uh. Some people

929
00:56:48,400 --> 00:56:51,719
Speaker 1: the most common pattern, of course is you're devastated initially

930
00:56:52,160 --> 00:56:57,000
Speaker 1: and then you get over it and um. But many

931
00:56:57,000 --> 00:57:00,360
Speaker 1: people never get over it, and the world treats them like,

932
00:57:00,400 --> 00:57:02,000
Speaker 1: what's wrong with you? I who It's supposed to take

933
00:57:02,000 --> 00:57:04,560
Speaker 1: about a year, and you seem to have you seem

934
00:57:04,600 --> 00:57:07,080
Speaker 1: to still be troubled by this. We're adding an extra

935
00:57:07,160 --> 00:57:10,600
Speaker 1: burden onto those people. Some people aren't troubled right to

936
00:57:10,800 --> 00:57:13,160
Speaker 1: be from the beginning, and that can seem perverse weight.

937
00:57:13,239 --> 00:57:15,600
Speaker 1: You just lost a child, What what's wrong with you

938
00:57:15,640 --> 00:57:20,000
Speaker 1: that you didn't feel that way? Well, that happens, um.

939
00:57:20,720 --> 00:57:24,800
Speaker 1: And the problem with a firm belief in stage theories

940
00:57:24,960 --> 00:57:28,040
Speaker 1: is that they take on this normative stance, this is

941
00:57:28,080 --> 00:57:32,000
Speaker 1: what you should do. In fact, there are certain practitioners.

942
00:57:32,280 --> 00:57:35,160
Speaker 1: That sounds like I'm making this up, but it's uh,

943
00:57:35,200 --> 00:57:40,240
Speaker 1: and I wish I were UM, But there's a practitioners

944
00:57:40,320 --> 00:57:44,360
Speaker 1: manual for nurses that refers to people who don't go

945
00:57:44,480 --> 00:57:52,680
Speaker 1: through Kuberas's five stages in the order specified as pathological diers. Uh.

946
00:57:52,720 --> 00:57:54,960
Speaker 1: And just think how insidious that is. You've gotten the

947
00:57:54,960 --> 00:57:58,520
Speaker 1: worst news you could possibly get that your life here

948
00:57:58,640 --> 00:58:01,560
Speaker 1: is you don't know if much time left on earth.

949
00:58:01,840 --> 00:58:03,840
Speaker 1: You've got to deal with that. And now, in addition

950
00:58:03,880 --> 00:58:05,920
Speaker 1: to that, you have to deal with the fact that

951
00:58:05,960 --> 00:58:08,920
Speaker 1: the people presumably there to help you think that you

952
00:58:08,960 --> 00:58:12,800
Speaker 1: aren't doing it right. It's just adding a horrible injury

953
00:58:12,800 --> 00:58:16,960
Speaker 1: insult to an already terrible injury. And you link in

954
00:58:17,040 --> 00:58:19,440
Speaker 1: one of your presentations, you linked to the video of

955
00:58:19,600 --> 00:58:23,680
Speaker 1: Homer Simpson going through the five stages, which is absolutely hilarious.

956
00:58:23,680 --> 00:58:26,840
Speaker 1: I'll add the link to this. I have to ask

957
00:58:26,880 --> 00:58:30,440
Speaker 1: the related question, what sort of pushback did you get

958
00:58:30,520 --> 00:58:35,760
Speaker 1: from the the establishment uh in in this area when

959
00:58:35,800 --> 00:58:40,200
Speaker 1: you basically said, hey, the data doesn't really support the

960
00:58:40,240 --> 00:58:43,800
Speaker 1: classic Kubler Ross five stages as much as we think

961
00:58:43,840 --> 00:58:46,400
Speaker 1: it does. Compared to the amount of pushback I got

962
00:58:46,400 --> 00:58:49,040
Speaker 1: on the hot hand hardly any at all. Really, Yeah,

963
00:58:49,440 --> 00:58:52,960
Speaker 1: that's amazing. Well, basketball is serious dying. Hey, you know,

964
00:58:53,080 --> 00:58:55,800
Speaker 1: we can't really pay much attention to that. So so

965
00:58:55,920 --> 00:58:59,560
Speaker 1: let me ask you the question about UM. About the

966
00:58:59,600 --> 00:59:03,880
Speaker 1: Basket Bowl hot hand issue. You mentioned, there's some new

967
00:59:03,920 --> 00:59:08,439
Speaker 1: evidence that perhaps moderates a little bit. I know, when

968
00:59:08,440 --> 00:59:11,600
Speaker 1: you first came out with it, everybody from Red Hour

969
00:59:11,720 --> 00:59:15,680
Speaker 1: back to whoever, weighed in on it. What was some

970
00:59:15,800 --> 00:59:19,480
Speaker 1: of the crazier push back you got on this and

971
00:59:20,320 --> 00:59:26,520
Speaker 1: how accurate do you think the original assessment is? Um? Well,

972
00:59:26,520 --> 00:59:29,480
Speaker 1: I wouldn't say any of the pushback is crazy. It was.

973
00:59:29,600 --> 00:59:32,200
Speaker 1: It was very firm, and you know I understand that.

974
00:59:32,320 --> 00:59:37,680
Speaker 1: And UM, again be precisely because it's such a compelling phenomenon.

975
00:59:37,880 --> 00:59:39,480
Speaker 1: When you're out there in the heat of the game

976
00:59:39,560 --> 00:59:42,160
Speaker 1: and you've made several shots in a row, UM, and

977
00:59:42,200 --> 00:59:47,320
Speaker 1: then you hear about these psychologist statisticians doing some analysis,

978
00:59:47,320 --> 00:59:51,919
Speaker 1: it's easy to um just that that can't be and

979
00:59:52,080 --> 00:59:56,440
Speaker 1: just dismiss it. UM. And they're very dismissive that what

980
00:59:56,480 --> 01:00:00,000
Speaker 1: do they know, they're a bunch of psychologists. Well, um,

981
01:00:00,000 --> 01:00:03,840
Speaker 1: ecologists play basketball too, and and UM, you know, maybe

982
01:00:03,840 --> 01:00:06,000
Speaker 1: we wouldn't have the hot hand as much as a

983
01:00:06,080 --> 01:00:09,440
Speaker 1: more skilled player, but we do have it and UM,

984
01:00:09,480 --> 01:00:13,320
Speaker 1: so it wasn't crazy, but it was pretty um dismissive.

985
01:00:13,480 --> 01:00:19,080
Speaker 1: And again with respect to our initial hypothesis, are people

986
01:00:19,640 --> 01:00:23,320
Speaker 1: overestimating any streaked pinus that might be there? That the

987
01:00:23,400 --> 01:00:25,640
Speaker 1: answer to that is very clear. In fact, we're running

988
01:00:26,000 --> 01:00:30,880
Speaker 1: additional studies right now where um, we have people shoot

989
01:00:30,920 --> 01:00:33,520
Speaker 1: for us, UM and they identify just tell us when

990
01:00:33,520 --> 01:00:36,120
Speaker 1: you're hot, um, and then afterwards we say, you know

991
01:00:36,160 --> 01:00:38,760
Speaker 1: you said you were hot, Uh, some number of times

992
01:00:38,840 --> 01:00:41,840
Speaker 1: you didn't say that, some number of times. What percentage

993
01:00:41,920 --> 01:00:43,880
Speaker 1: of the shots do you think you made after you

994
01:00:43,920 --> 01:00:46,760
Speaker 1: said you were hot versus uh, the times when you

995
01:00:46,800 --> 01:00:51,200
Speaker 1: didn't say that? And the overestimation is staggering. They you know,

996
01:00:51,320 --> 01:00:54,200
Speaker 1: we know what percentage of shots they made when they

997
01:00:54,240 --> 01:00:58,320
Speaker 1: said they were hot, and they're wildly overestimating that. They're

998
01:00:58,400 --> 01:01:01,880
Speaker 1: modestly underestimating how well they did when they said they

999
01:01:01,880 --> 01:01:07,320
Speaker 1: weren't hot. So again a departure between ah belief in reality.

1000
01:01:07,640 --> 01:01:10,720
Speaker 1: So so let me not look for confirming data on

1001
01:01:10,720 --> 01:01:13,080
Speaker 1: on the thesis. Uh that there was a lot of

1002
01:01:13,120 --> 01:01:17,280
Speaker 1: pushback and ask did anybody at either the professional or

1003
01:01:17,360 --> 01:01:20,400
Speaker 1: college level get back to you and say, I want

1004
01:01:20,440 --> 01:01:26,040
Speaker 1: to adapt the data of the non hot hands. Here's

1005
01:01:26,080 --> 01:01:28,880
Speaker 1: how we think it could affect our player rotation or

1006
01:01:28,920 --> 01:01:31,600
Speaker 1: a game strategy. Did you ever hear anybody who said,

1007
01:01:32,680 --> 01:01:34,720
Speaker 1: as hard as this might be to believe, we think

1008
01:01:34,720 --> 01:01:37,560
Speaker 1: you're accurate and here's how it's going to change our

1009
01:01:37,680 --> 01:01:41,560
Speaker 1: coaching or playing strategy. Um, I you know, I've talked

1010
01:01:41,600 --> 01:01:44,840
Speaker 1: to a lot of coaches who will say, you know,

1011
01:01:44,880 --> 01:01:48,680
Speaker 1: one thing I have taken from that is that it

1012
01:01:48,720 --> 01:01:53,520
Speaker 1: reinforces really what my job is, which is to um

1013
01:01:53,560 --> 01:01:57,360
Speaker 1: to get my team the best shots. And I used

1014
01:01:57,400 --> 01:02:01,040
Speaker 1: to factor in how hot some one is. Now I'm

1015
01:02:01,080 --> 01:02:04,640
Speaker 1: gonna factor that in less it's really getting the ball

1016
01:02:04,680 --> 01:02:07,720
Speaker 1: in the hands of my best players on the best

1017
01:02:07,720 --> 01:02:11,200
Speaker 1: spots of the floor. And so it's it's reinforcing an

1018
01:02:11,200 --> 01:02:15,240
Speaker 1: alternative belief that they already have more than UM, trying

1019
01:02:15,240 --> 01:02:18,640
Speaker 1: to get them to extinguish this other belief. So there's

1020
01:02:18,680 --> 01:02:21,360
Speaker 1: a ton of other biases. I wanted to go over,

1021
01:02:21,520 --> 01:02:24,960
Speaker 1: the spotlight effect, the there's there's just a run of stuff.

1022
01:02:25,000 --> 01:02:28,040
Speaker 1: But I have to get to my favorite questions. Um,

1023
01:02:28,120 --> 01:02:31,600
Speaker 1: these are the standard questions we ask all of our guests. Okay,

1024
01:02:31,880 --> 01:02:34,840
Speaker 1: tell us the most important thing that people don't know

1025
01:02:35,040 --> 01:02:39,200
Speaker 1: about your background. I'm the first person in my family

1026
01:02:39,240 --> 01:02:41,640
Speaker 1: to go to college. That could sound like, oh, that's

1027
01:02:41,640 --> 01:02:46,240
Speaker 1: a bad thing. Um, I feel I've been studying gratitude

1028
01:02:46,240 --> 01:02:48,320
Speaker 1: a lot, and I think part of it stems from

1029
01:02:48,360 --> 01:02:51,760
Speaker 1: having grown up in an amazing part of the world

1030
01:02:51,760 --> 01:02:55,320
Speaker 1: at an amazing time. That is Silicon Valley before it

1031
01:02:55,360 --> 01:02:58,960
Speaker 1: was Silicon Valley. It's just a great place. It's pretty

1032
01:02:59,000 --> 01:03:02,760
Speaker 1: egalitary in world. Those of us whose parents didn't go

1033
01:03:02,800 --> 01:03:05,920
Speaker 1: to college mingled with those who did, and you never

1034
01:03:06,000 --> 01:03:08,240
Speaker 1: felt like you were part of a lower cast as

1035
01:03:08,240 --> 01:03:12,320
Speaker 1: a result of that. UM And obviously the weather there

1036
01:03:12,400 --> 01:03:16,360
Speaker 1: was sensational. You've got the beaches very close, Yosemite Valley

1037
01:03:16,480 --> 01:03:19,360
Speaker 1: very close. It was just an incredible place to have

1038
01:03:19,440 --> 01:03:24,000
Speaker 1: grown up. Tell us about some of your early mentors, Um,

1039
01:03:24,040 --> 01:03:27,920
Speaker 1: I've had the great pleasure of going to Stanford University,

1040
01:03:28,040 --> 01:03:31,880
Speaker 1: the number one psychology department then and now for a

1041
01:03:31,880 --> 01:03:35,760
Speaker 1: long time. It's it's had that ranking, UM at a

1042
01:03:35,880 --> 01:03:39,560
Speaker 1: great time. It was the height of the cognitive revolution

1043
01:03:39,760 --> 01:03:42,840
Speaker 1: in psychology. It's a lot of very exciting things happening,

1044
01:03:43,360 --> 01:03:45,680
Speaker 1: which is why I chose to go there, and then

1045
01:03:45,720 --> 01:03:49,320
Speaker 1: there were the surprising things. Uh Amos Diversky and Daniel

1046
01:03:49,360 --> 01:03:53,000
Speaker 1: Conneman were visiting there my very first year and started

1047
01:03:53,040 --> 01:03:55,520
Speaker 1: talking about this stuff I hadn't heard of that just

1048
01:03:55,560 --> 01:03:58,760
Speaker 1: seemed so compelling. It was sort of the dawn of

1049
01:03:58,800 --> 01:04:01,840
Speaker 1: the revolution and in judgment and decision making, and it

1050
01:04:01,920 --> 01:04:04,960
Speaker 1: was just a very heady time, and they were generous

1051
01:04:04,960 --> 01:04:09,880
Speaker 1: with their time. My advisers, Mark Leper and Lee Ross too,

1052
01:04:10,520 --> 01:04:15,640
Speaker 1: incredibly insightful psychologists, were very helpful. So it was just

1053
01:04:15,720 --> 01:04:20,280
Speaker 1: a felt like being in Wittgenstein's Vienna. I was just

1054
01:04:20,400 --> 01:04:24,880
Speaker 1: a great place at a great time, with terrific people

1055
01:04:25,160 --> 01:04:29,440
Speaker 1: and terrifically giving people. So let me diverge from my

1056
01:04:30,600 --> 01:04:33,320
Speaker 1: normal questions and ask you two things about this. First,

1057
01:04:34,000 --> 01:04:36,280
Speaker 1: you write that you originally planning on going to law

1058
01:04:36,320 --> 01:04:39,000
Speaker 1: school until you happen to see to Versking and Kaneman

1059
01:04:40,000 --> 01:04:44,480
Speaker 1: at school. Did did you really completely shift your career

1060
01:04:44,520 --> 01:04:48,240
Speaker 1: plans based on No, not not quite. Those are there's

1061
01:04:49,000 --> 01:04:52,440
Speaker 1: Those two components are true, but they're melded together. That

1062
01:04:52,560 --> 01:04:54,200
Speaker 1: is to say, I went to college again. I was

1063
01:04:54,240 --> 01:04:56,000
Speaker 1: the first most of my family to go to college.

1064
01:04:56,520 --> 01:04:58,400
Speaker 1: I didn't know what I wanted to do. But I

1065
01:04:58,680 --> 01:05:00,560
Speaker 1: kind of like to argue with people, and I thought

1066
01:05:00,600 --> 01:05:04,800
Speaker 1: that would be a good uh field the law, and

1067
01:05:05,160 --> 01:05:09,280
Speaker 1: but there's there's no pre law. You can take anything

1068
01:05:09,440 --> 01:05:12,240
Speaker 1: as a pre law person. And so I took a

1069
01:05:12,280 --> 01:05:16,680
Speaker 1: bunch of stuff and some of them psychology courses like them,

1070
01:05:16,720 --> 01:05:20,240
Speaker 1: took another, like that, took another, and then suddenly don

1071
01:05:20,320 --> 01:05:22,880
Speaker 1: dun me, Hey, I seem to like the psychology stuff.

1072
01:05:23,400 --> 01:05:25,960
Speaker 1: Can I make a career out of this? And took

1073
01:05:25,960 --> 01:05:28,960
Speaker 1: a year off after undergraduate to sort of do I

1074
01:05:29,000 --> 01:05:31,080
Speaker 1: want to take that plunge and go to graduate school?

1075
01:05:31,080 --> 01:05:34,440
Speaker 1: And the answer was yes. And I'm really really glad

1076
01:05:35,160 --> 01:05:38,440
Speaker 1: that the answer was yes. So one of the themes

1077
01:05:38,480 --> 01:05:41,720
Speaker 1: that comes up in all these conversations with people who

1078
01:05:41,760 --> 01:05:46,840
Speaker 1: have achieved either personal or financial success is surprisingly the

1079
01:05:46,960 --> 01:05:50,680
Speaker 1: role of luck and randomness in their careers. And I

1080
01:05:50,720 --> 01:05:53,680
Speaker 1: can't count how many people have said, you know, but

1081
01:05:53,960 --> 01:05:59,640
Speaker 1: for this one thing happening, my entire professional career, personal life, whatever,

1082
01:06:00,080 --> 01:06:03,280
Speaker 1: could have easily gone in a different direction you referenced

1083
01:06:03,520 --> 01:06:07,840
Speaker 1: in and again I think it was the white smart

1084
01:06:07,880 --> 01:06:11,960
Speaker 1: people make big money mistakes. The experience of of seeing

1085
01:06:12,040 --> 01:06:15,560
Speaker 1: to Versking condiment at Stanford am I reading too much

1086
01:06:15,600 --> 01:06:17,840
Speaker 1: into that. Was that a big deal? Or was that

1087
01:06:17,920 --> 01:06:20,720
Speaker 1: just hmm? This is really interesting and I want to

1088
01:06:20,760 --> 01:06:25,320
Speaker 1: stay with with psychology. I maybe put I may be

1089
01:06:25,400 --> 01:06:27,400
Speaker 1: reading too much into it. No, you're not. I mean

1090
01:06:27,520 --> 01:06:31,200
Speaker 1: it's uh well, I'll tell an embarrassing story about myself,

1091
01:06:31,280 --> 01:06:33,960
Speaker 1: which is I go to Stanford. It's chock full of

1092
01:06:34,040 --> 01:06:38,120
Speaker 1: these famous people. Walter Michelle of the Marshmallow Test, Gordon Bauer,

1093
01:06:38,200 --> 01:06:41,680
Speaker 1: a giant, and the cognitive revolution. The people I planned

1094
01:06:41,680 --> 01:06:45,080
Speaker 1: to study with, Mark Leper, Lee ross Um, and Lee

1095
01:06:45,200 --> 01:06:49,880
Speaker 1: Ross ran a seminar on an introduction to the faculty. Um.

1096
01:06:49,920 --> 01:06:52,520
Speaker 1: Each week you'd bring in two other members of the faculty.

1097
01:06:52,560 --> 01:06:56,080
Speaker 1: We'd read their papers and find out what's going on

1098
01:06:56,200 --> 01:07:00,680
Speaker 1: there among the faculty. Great way to start a program. Um,

1099
01:07:00,760 --> 01:07:04,960
Speaker 1: And in the first organizational meeting, Lee says, Okay, that's

1100
01:07:04,960 --> 01:07:07,160
Speaker 1: what we're gonna do. But the first week, we've got

1101
01:07:07,160 --> 01:07:11,360
Speaker 1: these visitors here, Amos Tversky and Daniel Kneman Um, and

1102
01:07:11,400 --> 01:07:14,280
Speaker 1: so we're gonna start with them. And internally, I say

1103
01:07:14,320 --> 01:07:17,320
Speaker 1: to myself, why these guys? Why I can't I want

1104
01:07:17,320 --> 01:07:20,520
Speaker 1: to hear from the famous people. Because my undergraduate education

1105
01:07:20,560 --> 01:07:23,000
Speaker 1: was such I hadn't heard of them. And I'm now

1106
01:07:23,080 --> 01:07:27,800
Speaker 1: embarrassed by that. Um but at least and they they

1107
01:07:28,440 --> 01:07:33,640
Speaker 1: had us read the now epic u N paper and

1108
01:07:33,720 --> 01:07:37,800
Speaker 1: science on heuristics and biases. And so it's an embarrassing

1109
01:07:37,840 --> 01:07:40,400
Speaker 1: story that I would say, um gee, when are we

1110
01:07:40,440 --> 01:07:42,920
Speaker 1: going to hear from the famous people? But one thing

1111
01:07:42,960 --> 01:07:44,920
Speaker 1: I can say in my defense, at least one thing

1112
01:07:44,960 --> 01:07:47,160
Speaker 1: that makes me feel better is at least I recognized

1113
01:07:47,640 --> 01:07:49,880
Speaker 1: when we read that paper, Hey man, this is this

1114
01:07:49,960 --> 01:07:53,520
Speaker 1: is really great stuff. Um. And and that changed what

1115
01:07:53,560 --> 01:07:56,840
Speaker 1: I planned to do in graduate school. That's fascinating. Tell

1116
01:07:56,920 --> 01:08:00,520
Speaker 1: us about some of your favorite books. Man, there's so

1117
01:08:00,560 --> 01:08:05,720
Speaker 1: many great books. Uh, well, I'll give you a category

1118
01:08:05,840 --> 01:08:09,960
Speaker 1: for one. Anything that Ian McEwen writes is just brilliant

1119
01:08:10,040 --> 01:08:13,160
Speaker 1: and and um you know it's it's it's fiction, but

1120
01:08:13,200 --> 01:08:17,240
Speaker 1: it's fiction that touches on and it's fiction that touches

1121
01:08:17,280 --> 01:08:20,280
Speaker 1: on so many different areas of life and so many

1122
01:08:20,320 --> 01:08:22,680
Speaker 1: different themes, but some of it having to do very

1123
01:08:22,720 --> 01:08:25,320
Speaker 1: related to judgment and decision making in game theory. The

1124
01:08:25,439 --> 01:08:30,519
Speaker 1: beginning the opening scene to his novel Enduring Love, it's

1125
01:08:31,560 --> 01:08:35,040
Speaker 1: a brilliant fictional depiction of game theory. It's all you

1126
01:08:35,080 --> 01:08:37,439
Speaker 1: need to know about game theory happens in that first

1127
01:08:37,640 --> 01:08:43,040
Speaker 1: very riveting uh scene, So anything by Ian McEwen. Another

1128
01:08:43,760 --> 01:08:47,479
Speaker 1: moving out of fiction to oh and his kind of

1129
01:08:47,520 --> 01:08:53,600
Speaker 1: recent one Nutshell that which is a little bizarrely fascinating

1130
01:08:53,680 --> 01:08:59,360
Speaker 1: take on the Hamlet tail is brilliant um. Moving out

1131
01:08:59,360 --> 01:09:03,479
Speaker 1: of fiction to nonfiction guns, germs and steel, just sort

1132
01:09:03,479 --> 01:09:05,880
Speaker 1: of the scope of it, and the you know, he

1133
01:09:05,920 --> 01:09:08,920
Speaker 1: asks so many questions that would never occur to me

1134
01:09:09,000 --> 01:09:13,559
Speaker 1: at least to ask and uh and then bring some

1135
01:09:13,880 --> 01:09:16,799
Speaker 1: interesting analysis to bear on them, some of them convincing,

1136
01:09:16,840 --> 01:09:19,439
Speaker 1: some of them less so. But the ambitiousness of it

1137
01:09:19,560 --> 01:09:26,240
Speaker 1: and the questions asked for just uh, just terrific um.

1138
01:09:26,280 --> 01:09:29,200
Speaker 1: And you know, we started with conoman diversky and so

1139
01:09:30,320 --> 01:09:34,040
Speaker 1: it feels right to say Thinking Fast and Slow is

1140
01:09:34,640 --> 01:09:40,800
Speaker 1: chock full of wisdom and just a brilliant explication of

1141
01:09:40,840 --> 01:09:44,800
Speaker 1: all the research in this area with h Danny Knomans

1142
01:09:44,880 --> 01:09:50,800
Speaker 1: just gift for putting things just right. I love that book.

1143
01:09:51,040 --> 01:09:53,640
Speaker 1: It was tremendous. Um. You work with a lot of

1144
01:09:53,640 --> 01:09:55,960
Speaker 1: students and a lot of millennials. What sort of advice

1145
01:09:55,960 --> 01:09:59,920
Speaker 1: would you give someone who was interested in psychology as

1146
01:10:00,000 --> 01:10:03,839
Speaker 1: a career? A bit of a complicated answer to that question.

1147
01:10:04,120 --> 01:10:07,680
Speaker 1: That is to say, people are often taught, oh, just

1148
01:10:07,760 --> 01:10:11,120
Speaker 1: follow your passion so on. And I chafe at that

1149
01:10:11,200 --> 01:10:13,719
Speaker 1: a little bit because I know a lot of kids.

1150
01:10:14,240 --> 01:10:16,519
Speaker 1: It makes a lot of kids feel bad because they think, oh,

1151
01:10:16,600 --> 01:10:18,600
Speaker 1: I don't have a passion, and then the problem is

1152
01:10:18,640 --> 01:10:21,360
Speaker 1: finding it. And it's okay to not have a passion.

1153
01:10:21,600 --> 01:10:25,600
Speaker 1: It's okay to be a person who does a variety

1154
01:10:25,640 --> 01:10:27,680
Speaker 1: of different jobs. And you know, you can be a

1155
01:10:27,720 --> 01:10:30,559
Speaker 1: good person and live a good life as just be

1156
01:10:30,640 --> 01:10:33,200
Speaker 1: a taxpayer, be someone who's a good neighbor, and so on.

1157
01:10:33,320 --> 01:10:36,439
Speaker 1: You don't have to to have a great passion and

1158
01:10:36,479 --> 01:10:39,519
Speaker 1: be a giant success. So I want to put that

1159
01:10:40,240 --> 01:10:44,280
Speaker 1: out there because I firmly believe that because the advice

1160
01:10:44,280 --> 01:10:47,000
Speaker 1: I'm going to give it could sound like, oh, you're

1161
01:10:47,120 --> 01:10:51,040
Speaker 1: recommending that and I'm not, which is, um, just do stuff.

1162
01:10:51,600 --> 01:10:54,439
Speaker 1: Do what's engaging to you. Take take you know. Um,

1163
01:10:54,479 --> 01:10:58,160
Speaker 1: if you're a student, don't worry so much about getting

1164
01:10:58,200 --> 01:11:01,200
Speaker 1: your undergraduate business May Jersey, so you can go right

1165
01:11:01,240 --> 01:11:04,320
Speaker 1: away into a job. Um, because if you do that

1166
01:11:05,200 --> 01:11:07,080
Speaker 1: and it's a job you like, they're probably gonna want

1167
01:11:07,120 --> 01:11:08,680
Speaker 1: you to get your m b a anyway, and you're

1168
01:11:08,680 --> 01:11:11,760
Speaker 1: gonna relearn that stuff. Um. You never know what you're

1169
01:11:11,760 --> 01:11:14,280
Speaker 1: gonna draw upon in your life. So take the kinds

1170
01:11:14,320 --> 01:11:17,840
Speaker 1: of courses, do the kinds of things that you're really

1171
01:11:18,200 --> 01:11:21,960
Speaker 1: engaged in. Push yourself a little bit um, That's what

1172
01:11:22,000 --> 01:11:24,120
Speaker 1: I would say. You know, the world changes so quickly

1173
01:11:24,200 --> 01:11:28,240
Speaker 1: you can't anticipate what the skills of the marketplace are

1174
01:11:28,280 --> 01:11:30,679
Speaker 1: going to be with that much accuracy. So just keep

1175
01:11:30,680 --> 01:11:34,400
Speaker 1: building your your intellectual capital and don't worry so much

1176
01:11:34,439 --> 01:11:37,680
Speaker 1: about the outcome. And our final question, what is it

1177
01:11:37,720 --> 01:11:41,240
Speaker 1: that you know about psychology today that you wish you

1178
01:11:41,320 --> 01:11:44,960
Speaker 1: knew thirty plus years ago when you were first starting out. Yeah,

1179
01:11:45,040 --> 01:11:47,760
Speaker 1: great question and a simple answer to it. Uh. I

1180
01:11:47,840 --> 01:11:50,960
Speaker 1: knew this a lot in psychology, but I kept it

1181
01:11:51,080 --> 01:11:56,400
Speaker 1: sort of walled off there um and didn't appreciate its breadth.

1182
01:11:56,439 --> 01:12:00,160
Speaker 1: Which is an idea often attributed to Kurt Lewin, which

1183
01:12:00,280 --> 01:12:04,280
Speaker 1: is when we're trying to change behavior other people's are

1184
01:12:04,280 --> 01:12:07,560
Speaker 1: our own, we often try to do it by increasing motivation.

1185
01:12:07,680 --> 01:12:12,000
Speaker 1: Psych people up higher motivational speakers to get the salesforce

1186
01:12:12,120 --> 01:12:14,760
Speaker 1: charged up and so on. And there are times in

1187
01:12:14,760 --> 01:12:18,880
Speaker 1: which that is helpful when motivation. When motivation is the problem.

1188
01:12:19,000 --> 01:12:22,759
Speaker 1: Getting more motivation is great, but oftentimes that's not the problem.

1189
01:12:22,800 --> 01:12:25,320
Speaker 1: People are perfectly well motivated and they just can't figure

1190
01:12:25,320 --> 01:12:29,400
Speaker 1: out how to translate their strong motivations into effective action.

1191
01:12:29,760 --> 01:12:33,439
Speaker 1: And Lewin's idea very simple one is when that's the case,

1192
01:12:33,640 --> 01:12:37,240
Speaker 1: don't try to push people more. Figure out what's preventing

1193
01:12:37,280 --> 01:12:42,240
Speaker 1: them and take away those blockages. And behavioral economists have

1194
01:12:42,320 --> 01:12:46,160
Speaker 1: been using that a lot, uh to exemplified best in

1195
01:12:46,240 --> 01:12:50,599
Speaker 1: the Richard Taylor and Cass Sunstein book Nudge. Figure out

1196
01:12:50,680 --> 01:12:56,120
Speaker 1: what's preventing this and uh rearrange the environment a little

1197
01:12:56,120 --> 01:12:58,800
Speaker 1: bit that makes the behavior easier. You want more of this,

1198
01:12:59,360 --> 01:13:03,480
Speaker 1: make it a little a bit easier. And I think if, um,

1199
01:13:03,520 --> 01:13:06,760
Speaker 1: if I knew that uh sooner, I would be more

1200
01:13:06,840 --> 01:13:11,320
Speaker 1: effective in you know, consulting on political campaigns or helping

1201
01:13:11,360 --> 01:13:15,640
Speaker 1: people in their personal lives as well. That that's absolutely fascinating.

1202
01:13:16,240 --> 01:13:21,160
Speaker 1: We have been speaking with Professor Tom Gilovich of Cornell University.

1203
01:13:21,200 --> 01:13:23,519
Speaker 1: If you enjoy this conversation, be sure and look up

1204
01:13:23,520 --> 01:13:26,559
Speaker 1: an Inch or down an inch on Apple, iTunes or

1205
01:13:26,600 --> 01:13:29,679
Speaker 1: wherever finer podcasts are sold, and you can see any

1206
01:13:29,720 --> 01:13:32,880
Speaker 1: of the other hundred and eighty or so such conversations

1207
01:13:32,880 --> 01:13:35,880
Speaker 1: that we've had. I would be remiss if I did

1208
01:13:35,880 --> 01:13:39,280
Speaker 1: not thank the Cracks staff who helps put this podcast

1209
01:13:39,320 --> 01:13:43,240
Speaker 1: together each week. Taylor Riggs is our booker, Michael Batnick

1210
01:13:43,320 --> 01:13:46,360
Speaker 1: is my head of research, and Medina Parwanner is our

1211
01:13:46,439 --> 01:13:50,640
Speaker 1: audio engineer slash producer. We love your comments, feedback in

1212
01:13:50,760 --> 01:13:54,760
Speaker 1: suggestions right to us at m IB podcast at Bloomberg

1213
01:13:54,800 --> 01:13:57,920
Speaker 1: dot net. I'm Barry Retults. You're listening to Masters in

1214
01:13:57,960 --> 01:14:06,479
Speaker 1: Business on Bloomberg Radio in the Canting Tin and the

1215
01:14:06,760 --> 01:14:06,800
Speaker 1: so