Speaker 1: Why are we humans so easy to deceive? What are
Speaker 1: the tricks of the trade, and how can we train
Speaker 1: ourselves to be more aware of these? And what does
Speaker 1: any of this have to do with fahnose or forging
Speaker 1: letters or the shell game. Welcome to Inner Cosmos with
Speaker 1: me David Eagleman. I'm a neuroscientist and an author at
Speaker 1: Stanford and in these episodes, I examine the intersection between
Speaker 1: our brains and our lives, and today's episode is about deception.
Speaker 1: You presumably wouldn't do something to cheat a stranger out
Speaker 1: of twenty dollars, So why are there people who would
Speaker 1: do that?
Speaker 2: And what can we do.
Speaker 1: To be a little more thoughtful and aware and immune
Speaker 1: against deception? So in a previous episode I talked about
Speaker 1: a really impactful event when I was a neuroscience graduate
Speaker 1: student getting my PhD. I was a second year student
Speaker 1: in the department and this new young woman came in
Speaker 1: as a first year student. We'll call her Tanya, and
Speaker 1: everyone could see that Tanya was great. She had great grades,
Speaker 1: top standardized test scores, terrific letters of recommendation, and in
Speaker 1: the interviews she even won over my graduate advisor, who
Speaker 1: was famously spiky towards people. And I tell her full
Speaker 1: story in episode sixteen, but the short version is that
Speaker 1: she faked everything on her graduate school application. She faked
Speaker 1: the school transcript and the GRE scores and the letters
Speaker 1: of recommendation, and she was only caught because an administrator
Speaker 1: at the school was so impressed with her that she
Speaker 1: decided to call the professors who had written the letters
Speaker 1: of recommendation to ask how they'd produced a student like Tanya.
Speaker 1: And that's how the whole house of cards came tumbling down. Now,
Speaker 1: for those of you who listened to episode sixteen, you'll
Speaker 1: remember that Tanya's story then got much weirder because she
Speaker 1: went to Yale University and tried to pull exactly the
Speaker 1: same trick, and when she was caught there, they put
Speaker 1: her in jail. And then she and her mother got
Speaker 1: caught doing a drug deal with two undercover agents. And
Speaker 1: then Tanya decided to try murdering a girl who looked
Speaker 1: vaguely like her to avoid going to prison. Now that
Speaker 1: plot failed, but only barely. So that's the quick recap
Speaker 1: of the story. But the part I want to concentrate
Speaker 1: on today is why did none of us see this coming?
Speaker 1: We all thought she was great. And this was a
Speaker 1: neuroscience graduate program full of people who were aspiring learners
Speaker 1: about the human brain and faculty who were presumably already
Speaker 1: experts in the brain. And yet every single one of
Speaker 1: us thought that Tanya was great. None of us even
Speaker 1: had the briefest glimpse of doubt or suspicion when she
Speaker 1: started school, And we were all maximally surprised when we
Speaker 1: saw how completely we had been fooled.
Speaker 2: So why were we so blind?
Speaker 1: Well, first of all, none of us would have thought
Speaker 1: about faking our transcripts and writing fake letters and so on.
Speaker 3: That kind of.
Speaker 1: Deception didn't exist in our mental models, and so it
Speaker 1: was totally invisible to us when it was sitting there
Speaker 1: right in front of us. And one of the themes
Speaker 1: of this podcast and of my next book is that
Speaker 1: we need to get better at seeing outside the garden
Speaker 1: walls of our own internal models. This is really what
Speaker 1: the passage into maturity is about, seeing the limitations of
Speaker 1: our own thinking and realizing that what's going on in
Speaker 1: someone else's head might be very different than what's going
Speaker 1: on inside hours even if we're not the kind of
Speaker 1: person to do something, even if it seems absolutely unimaginable
Speaker 1: to us, it doesn't mean that.
Speaker 2: It seems that way to someone else.
Speaker 1: And if you heard episodes twenty and twenty one, you'll
Speaker 1: know that we dove into some of the really awful
Speaker 1: things that happened during wartime. And again, just because you
Speaker 1: can't imagine hacking your neighbors to death with a machete
Speaker 1: or shooting your neighbors or bayonetting them, it doesn't mean
Speaker 1: that someone else can't imagine that and won't foment violence
Speaker 1: without having much compunction about it. So an understanding of
Speaker 1: history requires an expansion of our mental models, and that's
Speaker 1: what's required for navigating day to day life as well,
Speaker 1: because if not everyone is just like you on the inside.
Speaker 1: For example, psychopaths make up about one percent of the population,
Speaker 1: and by the way, they make up about twenty to
Speaker 1: thirty percent of the prison population. They don't care about you,
Speaker 1: they don't simulate what it is like to be you,
Speaker 1: and they can be violent towards you because they just
Speaker 1: see you as an obstacle to.
Speaker 2: Flow around to get what they want.
Speaker 1: And I'm going to do an episode on psychopathy soon,
Speaker 1: but the point I want to make right now is
Speaker 1: that if you are not a psychopath, this is very
Speaker 1: difficult to imagine someone behaving that way. But you'll be
Speaker 1: smarter in your daily life if you understand how other
Speaker 1: people can be different from you. Now, sometimes people are
Speaker 1: different in wonderful ways, like when you see some situation
Speaker 1: in which someone is braver than you, or just more
Speaker 1: charitable with a higher percentage of their money, or more
Speaker 1: willing to do the right thing, like to climb the
Speaker 1: side of a building to save the toddler hanging off
Speaker 1: the balcony, even though you would be more scared. But
Speaker 1: sometimes we see people different from us in the other direction,
Speaker 1: people who cheat and lie and steal, and it's hard
Speaker 1: to understand because we don't have a good model of that,
Speaker 1: and so we're often caught completely by surprise. I'll give
Speaker 1: you an example of this when I was young. When
Speaker 1: I was sixteen years old, I was traveling with my
Speaker 1: parents in Barcelona, and I was spending an afternoon walking
Speaker 1: around by myself, and I saw a crowd of people
Speaker 1: playing a shell game. You know, this is the game
Speaker 1: where a person puts a small ball under one of
Speaker 1: three cups and then rotates the cups around and around
Speaker 1: and then you have to guess which cup the ball
Speaker 1: is under. So I stopped to watch because there was
Speaker 1: a small crowd and the dealer was moving the cups around,
Speaker 1: and there was this pedestrian like me who had put
Speaker 1: down some money. And pedestrian watched the cups go around
Speaker 1: and round, and when they stopped, he pointed to a
Speaker 1: cup and it was.
Speaker 2: The wrong cup.
Speaker 1: But I could see where the cups had moved, and
Speaker 1: I knew it was the cup on the left, but
Speaker 1: this pedestrian pointed to the middle. So the dealer uncovered
Speaker 1: the middle cup, and the whole crowd made a whooping sound,
Speaker 1: and so the pedestrian put down more money to play
Speaker 1: another round, and the dealer shows the ball under the
Speaker 1: left cup, and then he rotates the cups around faster
Speaker 1: and faster, but I kept my eyes locked on the
Speaker 1: correct cup, and again the pedestrian guessed wrong, but I
Speaker 1: knew where the ball was. So this happens a few
Speaker 1: more rounds, and the pedestrian gives up, and the dealer
Speaker 1: looks at me and motions for me to put up
Speaker 1: some money, so I did so he shows me the
Speaker 1: ball and rotates the cups around and around, and I
Speaker 1: keep my eye on it, and when he stops, I
Speaker 1: point to the correct cup and the cup was empty.
Speaker 2: I got it wrong, what was going on?
Speaker 1: So he motions for me to put down more money,
Speaker 1: and I want to win my lost money back, so
Speaker 1: I put down more and he runs the rotations again,
Speaker 1: and I point to the cup where the ball should be,
Speaker 1: and again it's empty. And before I know it, someone
Speaker 1: in the crowd makes a whoop sound, and suddenly the
Speaker 1: dealer folds up the board and the entire crowd disappears,
Speaker 1: and I'm standing there all by myself in the street,
Speaker 1: and I felt like such a fool because I had
Speaker 1: just been deceived. Now this is embarrassing for me to
Speaker 1: tell the story, and even all these years later, there
Speaker 1: is some pain in the remembrance.
Speaker 3: But my hope in.
Speaker 1: Relating the story is that at least one teenage listener
Speaker 1: gets an expansion of their mental model from this and
Speaker 1: doesn't have to play this game. Just in case you
Speaker 1: don't know, there is no honest version of the shell game.
Speaker 1: It's always performed by hucksters who use sleight of hand
Speaker 1: to move the ball from one cup to another, and
Speaker 1: the whole crowd is in on the deception. Now, when
Speaker 1: I did some research on this, I found the shell
Speaker 1: game is very old, so the game of people trying
Speaker 1: to deceive other people is ancient.
Speaker 3: Now.
Speaker 1: Sometimes deception is planned in advance like this, and sometimes
Speaker 1: people are just trying to get out of bad situation.
Speaker 1: They make it worse. Sometimes it's hard to tell. I mean,
Speaker 1: look at the company, Thearrhannos, which you've probably heard of.
Speaker 1: They were a health tech company founded by a young
Speaker 1: woman named Elizabeth Holmes, and they were out to develop
Speaker 1: a biological test that could measure a whole bunch of
Speaker 1: things using just a drop or two of blood. So
Speaker 1: they raised seven hundred and twenty four million dollars from investors,
Speaker 1: including the media mogul Robert Murdoch and former Secretary of
Speaker 1: State Henry Kissinger, and all kinds of big players were
Speaker 1: enthusiastic about this revolutionary technology. But this all came crashing
Speaker 1: down in twenty fifteen when it surfaced that Sarahnos had
Speaker 1: been lying about what their technology could do. Holmes was
Speaker 1: charged with fraud and conspiracy, and she was found guilty
Speaker 1: in twenty twenty two. Now you probably know this story,
Speaker 1: but the interesting backside of this story is So many
Speaker 1: people here in Silicon Valley have said things to me
Speaker 1: suggesting that they would have never fallen for Farnos, like, oh,
Speaker 1: I would have known right away that couldn't work. But
Speaker 1: that's silly, and I generally don't believe them because it's
Speaker 1: easy to get douped. And if the other people who
Speaker 1: believe in it and invest in it and sit on
Speaker 1: the board are billionaires and big shots, what's to make
Speaker 1: you think that wouldn't have a great gravitational pull on you.
Speaker 1: Because the truth is, when we look at things that
Speaker 1: other people believe in, or even simply things that match
Speaker 1: our expectations, we often don't do any further looking into it. Now,
Speaker 1: this puts us in a tough spot because we have
Speaker 1: to trust other people. We really have no choice, because
Speaker 1: we can't disbelieve every thing or have time to check
Speaker 1: on everything. So how do we work around this bias?
Speaker 1: How can we take some of the tools of science,
Speaker 1: which are all about clear thinking and import these into
Speaker 1: our daily lives. Well, I'm no expert on this. I
Speaker 1: often err on the side of believing everyone, but I
Speaker 1: knew who to call. My colleagues Dan Simon's and Christopher
Speaker 1: Shubrie recently wrote a terrific book all about deception called
Speaker 1: Nobody's Fool. So I wrung them up.
Speaker 4: The most interesting thing we found about what all the
Speaker 4: cons and deceptions have in common is that the con artists,
Speaker 4: the scammers, the swindlers, whatever you want to call them,
Speaker 4: all seem to be taking advantage of sort of the
Speaker 4: same set of our cognitive proclivities and our attentional biases.
Speaker 4: What we like to pay attention to, what attracts us,
Speaker 4: and what mistakes we tend to make, and decision making
Speaker 4: that we may not be aware of.
Speaker 1: That's Christopher Shabri, a professor of psychology and director of
Speaker 1: Decision Sciences at Geisinger Research Institute.
Speaker 4: They may not be consciously aware of, but somehow they
Speaker 4: have sort of learned that adapted their schemes to things
Speaker 4: that tend to exploit these loopholes in our thinking. Not
Speaker 4: loopholes that are sort of design flaws necessarily, they're actually usually,
Speaker 4: you know, good things about how we think. But when
Speaker 4: someone is really trying to take advantage of us, they
Speaker 4: can cleverly exploit those and gain the advantage.
Speaker 1: So what are the important lessons for all of us
Speaker 1: to think about? Given that I'd say there are a few,
Speaker 1: and that's Dan Simons, a professor of psychology at the
Speaker 1: University of Illinois. One is that we have a tendency
Speaker 1: to assume that only the most gullible or naive or
Speaker 1: uneducated people fall for scams. And that's partly because we
Speaker 1: generally only see the results of cons and scams after
Speaker 1: they're over right, So it's easy to see what the
Speaker 1: red flags were when you knew it was a scam,
Speaker 1: you found out it was a scam in the same
Speaker 1: way that we can easily spot, you know, the obvious
Speaker 1: ones in advance, things like the Nigerian prints email scam
Speaker 1: that we know about. We can spot those red flags
Speaker 1: because we've seen them before. But we tend to assume
Speaker 1: that all scams prey on the people who are gullible.
Speaker 1: And one of the key insights we've across all of
Speaker 1: the sorts of scams that we've encountered is that scams
Speaker 1: can affect anybody. Cons can affect anybody if they're targeted
Speaker 1: in the right way to our wants and desires and needs. Yeah,
Speaker 1: you know, I thought about this a lot with Sarahnos
Speaker 1: here in Silicon Valley retrospectively, everyone acted like I would
Speaker 1: have never fallen for that. But it's obvious that a
Speaker 1: lot of good and smart people got sucked up into that,
Speaker 1: And so how do you how do you interpret that? Well?
Speaker 5: I think it's the way we see something like Sarahnose
Speaker 5: is in hindsight, after the fact, in the same way
Speaker 5: that we might watch a heist movie or a who
Speaker 5: Done It movie, where we know there's a heist, right,
Speaker 5: we know there's a con artist, we know that. In
Speaker 5: the context of the movie, we know to look for
Speaker 5: those red flags. We're trying to figure it out, and
Speaker 5: the characters in the movie aren't. But when it's viewed
Speaker 5: from the outside, it's kind of obvious, right, So Pharaohnose
Speaker 5: after the fact, Yeah, there are lots of red flags
Speaker 5: along the way, and they've been reported thoroughly, and it's
Speaker 5: great narrative. But when you're immersed in it and you're
Speaker 5: trying to figure out what's the next best investment or
Speaker 5: what do I want to get in on really quick?
Speaker 5: If you're a venture capitalist trying to kind of get
Speaker 5: in on the next big thing, spotting all those red
Speaker 5: flags is more difficult because you're incentivized to act with
Speaker 5: efficiency and to try and catch things before they take
Speaker 5: off and before people know about them. So those are
Speaker 5: the contexts in which they're the marks, rather than watching
Speaker 5: some interesting, engaging movie.
Speaker 4: In the case of Pharaohose. Also, you know, there were
Speaker 4: people who didn't invest and who didn't join the board
Speaker 4: of directors and so on. They don't get as much
Speaker 4: publicity as the unfortunate ones who did and look like
Speaker 4: marks and suckers and so on in retrospect. But some
Speaker 4: professional investors who specialized in biotech and healthcare investing, they
Speaker 4: asked a lot more questions about the product, about the technology,
Speaker 4: about clinical data, about all of that stuff, and then
Speaker 4: they walked away. And I think one other important point
Speaker 4: about their nose is I think, although I don't know,
Speaker 4: because I'm not inside the heads of all those people,
Speaker 4: but I think a lot of people didn't even consciously
Speaker 4: consider the idea that there might have been a scam
Speaker 4: or a fraud going on. Everything seemed good, everybody was optimistic,
Speaker 4: there was a great vision. Little things that seem kind
Speaker 4: of odd. Maybe you can explain away, this is just
Speaker 4: a quirky company. The CEO is a little odd. You know, Well,
Speaker 4: they've got all these famous people on the board. That
Speaker 4: must be a good thing. Simply considering the possibility in
Speaker 4: a big decision making situation that you maybe are being
Speaker 4: scammed or there's something going on that you're not aware of,
Speaker 4: you know, could be the first step towards like starting
Speaker 4: to see those red flags or look for those red flags,
Speaker 4: and maybe you can actually find some of them if
Speaker 4: you were even thinking about the possibility that they might
Speaker 4: be out there somewhere. We require a lot of trust
Speaker 4: just to get by in life. And so how do
Speaker 4: you guys think about striking a balance of trust and
Speaker 4: a little bit of suspicion.
Speaker 5: Well, trust is essential, right. In fact, we tend to
Speaker 5: assume that when we hear something from somebody it's true
Speaker 5: and until we take time to think about it otherwise.
Speaker 5: And most of the time that's a great thing because
Speaker 5: in most conversations, nobody's trying to lie to you. In
Speaker 5: most interactions, nobody's trying to con you. I mean, the
Speaker 5: odds of any of us being a victim of a
Speaker 5: bernie made off or a their noose is pretty low.
Speaker 5: The odds of any of us receiving fake information on
Speaker 5: social media is pretty high. But we tend to be
Speaker 5: trusting of the information we get, and it's a good
Speaker 5: thing that we are.
Speaker 4: Right.
Speaker 5: If we were constantly skeptical of everything we encountered, we
Speaker 5: could just never do anything. We could never have a conversation.
Speaker 5: You can't check everything. You can't be a perpetual skeptic
Speaker 5: or cynic about everything. You're not going to go check
Speaker 5: in the grocery store if you buy an organic apple, right,
Speaker 5: You're not going to go out to the farm and
Speaker 5: make sure they didn't use any pesticides. Right, It's too much.
Speaker 5: We can't really check that. We have to accept that
Speaker 5: some of the time we're going to have to be trusting.
Speaker 5: And the key is to kind of figure out when
Speaker 5: are those times when we're at the greatest risk, When
Speaker 5: are those times when the consequences could be bad enough
Speaker 5: that we really would want to check and see if
Speaker 5: we're being scammed.
Speaker 2: So before we go on to some other topics, just
Speaker 2: can you give a few examples of hoaxes or swindles
Speaker 2: or scams so that our listeners can understand what we're
Speaker 2: talking about.
Speaker 4: I have a good one that I think a lot
Speaker 4: of people probably haven't heard of, but they really should have,
Speaker 4: which is called sometimes the president scam or the CEO scam,
Speaker 4: and I didn't discover it. It's been it was going
Speaker 4: on for a while and it was documented elsewhere. But
Speaker 4: I think it's a great example of some of some
Speaker 4: of the key ideas. So this French Israeli fraudster named
Speaker 4: Jilbert Shickley developed a scam in which he would call
Speaker 4: up sort of mid level employees of French companies, pretending
Speaker 4: to be the CEO of the company, reaching down through
Speaker 4: the ranks and calling up some middle manager and giving
Speaker 4: them a task to do directly for him. And the
Speaker 4: task always wound up in money being transferred directly to
Speaker 4: some bank account or person or something like that, where
Speaker 4: of course it wound up with Shickley and whoever his
Speaker 4: associates were in their bank account somehow. And I think
Speaker 4: it's kind of an audacious con because it's one guy
Speaker 4: with a telephone calling people up who he's never met
Speaker 4: before and talking them into essentially giving him a lot
Speaker 4: of money. But it does illustrate sort of the idea
Speaker 4: of truth bias that Dan was just talking about, that
Speaker 4: if you don't believe that the person on the phone
Speaker 4: is the CEO calling you, the whole thing goes nowhere.
Speaker 4: But once you believe that, then the scam has a
Speaker 4: chance to get through. And it also illustrates sort of
Speaker 4: some of selection, sort of someone selection bias we see
Speaker 4: in cons Like we hear about the ones that worked,
Speaker 4: but we don't know about all the people who just
Speaker 4: like hung up the phone or deleted the email when
Speaker 4: he tried to talk to you know, to start talking
Speaker 4: to them into it. Just like the millions and millions
Speaker 4: of people who delete the Nigerian prints emails never get
Speaker 4: it mentioned anywhere. You know, it's just a few people
Speaker 4: who actually wind up going through with it. That CEO
Speaker 4: scam or President scam went along for quite a long
Speaker 4: time and sort of morphed and changed into different versions
Speaker 4: where eventually people were pretending to be the Defense Minister
Speaker 4: of France calling, you know, contacting wealthy individuals, especially with
Speaker 4: French ties, and saying that the government of France needed
Speaker 4: their help getting hostages, secret hostages out of Syria and
Speaker 4: Pharmisis and so on, and wound up taking i think
Speaker 4: something like eighty million dollars or eighty million euros in
Speaker 4: total from a number of you know, French companies and
Speaker 4: wealthy wealthy individuals by sort of similar tactics.
Speaker 5: There's a new modern version of this which is much
Speaker 5: dumber and much simpler. It doesn't require any sort of
Speaker 5: sophisticated persuasion. People just send an email purportedly from the
Speaker 5: boss of a company and saying, Hey, I'm in a
Speaker 5: meeting right now, but I need to transfer these funds
Speaker 5: right away or I need to close this sale. Can
Speaker 5: you just go ahead and make this transfer for me?
Speaker 5: And if the email happens to reach somebody who is
Speaker 5: responsible for doing that, then they might go ahead and
Speaker 5: do it without even double checking, when in reality the
Speaker 5: money would just get paid to some other count of
Speaker 5: the scammers. And this is so pervasive that I have
Speaker 5: a cousin who teaches tennis. She runs a tennis club
Speaker 5: and regularly gets her underlings. The other people teaching there
Speaker 5: regularly get emails from her, not really from her, but
Speaker 5: from her saying hey, I'm in a meeting, can you
Speaker 5: run this? Can you make this payment? And her employees
Speaker 5: and coworkers know that that's not true. She's pretty much
Speaker 5: never in meetings, and they're not the sorts of people
Speaker 5: who make purchases. But again, if you send it out
Speaker 5: to enough people, you're going to happen to hit. Some
Speaker 5: who in that moment are busy doing things are used
Speaker 5: to getting emails from their boss asking them to do
Speaker 5: something really quickly, and we'll go along with it without
Speaker 5: questioning it. And this is a major source of business.
Speaker 3: Run.
Speaker 1: Do you guys have any other examples, just something that
Speaker 1: some hoax or swinger or something that you came across
Speaker 1: that you think is really illustrative.
Speaker 4: I can give another example from the world of chess.
Speaker 3: So I'm a chess player. Well, Dan is also a
Speaker 3: chess player.
Speaker 4: I'm probably a more serious player than Dan is, and
Speaker 4: I'm a funnier player. Dan's a funny player. I'm a
Speaker 4: serious player. I'll try to be funnier though. When you
Speaker 4: play chess online, you don't see the person you're playing.
Speaker 4: It's just a screen name and all you see is
Speaker 4: the moves they play on an animated chess board, kind
Speaker 4: of like you're playing a video game, right, You just
Speaker 4: see the moose being made. And So I was playing
Speaker 4: a game once. This has happened to me more than once.
Speaker 4: But the occasion that I remember is I was playing
Speaker 4: a game and it was a guy I had never
Speaker 4: played before and the game started, and he had a
Speaker 4: similar rating to mine, meaning, you know, we were both
Speaker 4: pretty good players. I should be ready for a you know,
Speaker 4: I should be ready for a good game. And every
Speaker 4: move I made, it seemed like he always found like
Speaker 4: a great response, and he never made a mistake. And
Speaker 4: times when I thought I was winning, I really wasn't
Speaker 4: winning because he found the escape. And moreover, he was
Speaker 4: moving quite quickly, like he would make every move in
Speaker 4: like five to ten seconds. And I was like, wow,
Speaker 4: this guy's putting a lot of pressure on me. You know,
Speaker 4: I'm thinking for a minute sometimes on these moves, and
Speaker 4: he comes back in ten seconds all the time. And
Speaker 4: in the end I got checkmated and I lost the game,
Speaker 4: and I thought, wow, that guy like that guy played
Speaker 4: a really good game against me. But then when I
Speaker 4: looked at the game afterwards, chess dot com where we
Speaker 4: played this game shows you exactly how much time both
Speaker 4: players use in every move after the game, and I
Speaker 4: noticed that he was making all of his moves within
Speaker 4: that very tight band of five to ten seconds per move, basically,
Speaker 4: never less than five seconds, never more than ten seconds.
Speaker 4: Maybe it was twelve seconds, I don't remember, but if
Speaker 4: you looked at my own graph. There were a couple
Speaker 4: of moves that I took like one or two minutes
Speaker 4: on and some moves I played almost instantly, like one second.
Speaker 4: The consistency of his timing, and also the consistency of
Speaker 4: the fact that he never made a mistake. All of
Speaker 4: his moves were you know, almost the best move, if
Speaker 4: not the best move according to computer analysis, really reveals
Speaker 4: that all he was doing was being a conduit for
Speaker 4: a computer, Like he was just typing my moves into
Speaker 4: a computer and typing and putting back into chess dot
Speaker 4: com the moves that the computer told him to play.
Speaker 4: And here's an example where the behavior of a human
Speaker 4: you know, really ought to be more noisy in some
Speaker 4: fundamental way than the behavior of a computer. Humans never
Speaker 4: play moves with robotic cadences. They never play the correct
Speaker 4: move forty or fifty moves in a row, and there's
Speaker 4: just much more variability in human decision making and almost
Speaker 4: any human activity than there is in computer based activity.
Speaker 4: So I filed a report. You know, you can report
Speaker 4: any player, and sure enough, like a day or two later,
Speaker 4: chess dot com came back and said, we're giving you
Speaker 4: back the rating. Points you lost. This guy is violated,
Speaker 4: you know, violated our fair play policy. And that kind
Speaker 4: of thing happens all the time in online chess because
Speaker 4: computers are so good that they can be used easily
Speaker 4: to cheat. The real problem is are people using it
Speaker 4: sort of like in over the board and like serious
Speaker 4: tournaments and matches. And that's a whole other controversy. But
Speaker 4: certainly was a case where you know, I was essentially
Speaker 4: the victim of a little minor scam. I happened to
Speaker 4: figure it out, but as a scam based on people
Speaker 4: not noticing that, you know, not noticing the absence of variability,
Speaker 4: which is a critical thing and a lot of and
Speaker 4: a lot of cons I was.
Speaker 5: Gonna say one thing about that that particular case. It's
Speaker 5: a fairly minor scam, right, It's just one element of
Speaker 5: the kinds of habits and hooks that we find really compelling,
Speaker 5: the hook of consistency, right or that. Yeah, it's just
Speaker 5: something that we don't look for the noise when we should.
Speaker 5: But if you look at bigger scams, things like Bernie
Speaker 5: made Us, Ponzi Ski or fairness, they rely on a
Speaker 5: whole bunch of our cognitive tendencies and they appeal to
Speaker 5: a lot of kinds of information that we find really
Speaker 5: valuable and that do help us most of the time,
Speaker 5: but they take advantage of.
Speaker 3: Those to doup us.
Speaker 1: So can you unpacked that a little bit about noise
Speaker 1: in data and what we should be looking for.
Speaker 5: Well, I mean, really, in any human behavior, anything that's
Speaker 5: governed by interaction of people, we don't expect people to
Speaker 5: perform the same way every single time. We don't expect
Speaker 5: a three hundred hitter or three thirty three baseball hitter
Speaker 5: to hit one out of every single every one out
Speaker 5: of every three at bets, right, they will on average
Speaker 5: average about one out of every three. But in any
Speaker 5: game that doesn't guarantee they're going to get at least
Speaker 5: one hit, right, we tend to confuse the sort of
Speaker 5: on average performance with what happens every single time. So
Speaker 5: take the case of Bernie Madoff's Ponzi scheme. Right, this
Speaker 5: wasn't a sort of classic Ponzi scheme where he promised
Speaker 5: fifty percent returns in six months like a you know,
Speaker 5: current crypto scam. Would his returned eight to fourteen percent
Speaker 5: or eight to twelve percent almost every year for the
Speaker 5: entire life of the Ponzi scheme, with never a down
Speaker 5: year and almost never a down month. It was like
Speaker 5: a smooth, steady growth, And that's not what you expect
Speaker 5: for some thing is complex. As a financial system, you
Speaker 5: expect ups and downs. Sometimes you'll be up twenty five
Speaker 5: percent in a year, sometimes you'll be down ten percent
Speaker 5: in a year, and the average might be eight to
Speaker 5: twelve percent, and that'd be pretty good, But you don't
Speaker 5: expect the average to be true of every single case.
Speaker 5: And this plays out in many many contexts where usually
Speaker 5: consistency is a sign that we have great understanding of
Speaker 5: how things work. We can do it the same way
Speaker 5: every single time. We want things to be reliable, but
Speaker 5: the tendency to have things be too consistent can be
Speaker 5: so appealing to us that we don't realize when noise
Speaker 5: should be present. This is common in a lot of
Speaker 5: science frunt as well, that you find results that are
Speaker 5: just too consistent to be believable, but the people who
Speaker 5: are making it up don't realize that they need to
Speaker 5: make up noise too.
Speaker 1: So a lot of the reasons that we fall for
Speaker 1: hoaxes their scams is because of cognitive shortcuts that we're
Speaker 1: taking so tell us about that and what we can
Speaker 1: do about those shortcuts.
Speaker 4: Well, one of the most important shortcuts, I think is
Speaker 4: it's not even so much a shortcut, it's just an
Speaker 4: un standard operating procedure. We are very good at paying
Speaker 4: attention to things.
Speaker 3: You know.
Speaker 4: Attention is a wonderful thing. We can do things with
Speaker 4: attention that we can't do without attention. Like we couldn't
Speaker 4: even follow a soccer game or a football game or
Speaker 4: a basketball game without attention, and otherwise would just be
Speaker 4: a big blur of bodies moving around and the little
Speaker 4: round thing like you know, flying back and forth. Occasionally
Speaker 4: we'd have no hope of understanding it. But with attention
Speaker 4: we can focus on selected aspects of it and sort
Speaker 4: of put together the plot and the sequence of events
Speaker 4: and what people are trying to do, and understand the
Speaker 4: intentions behind it, all the way up to the strategies
Speaker 4: and so on.
Speaker 3: It's great.
Speaker 4: However, the downside of attention is that when we're paying
Speaker 4: attention to something, we may not notice other things that
Speaker 4: we're not paying attention to. And a Hoover fraudster knows that,
Speaker 4: and they know that if they can get our attention
Speaker 4: on one thing, kind of like a magician, then we
Speaker 4: might not notice other important things that are happening. And
Speaker 4: of course they're not doing magic, they're actually trying to
Speaker 4: deceive us for profit.
Speaker 3: So many of.
Speaker 4: The basic sort of deceptions in areas like marketing, where
Speaker 4: it's sort of not even deception in some cases, it's
Speaker 4: just kind of like the way business is done. You
Speaker 4: get the recipient to focus on what you're showing them,
Speaker 4: and you can count on them usually to not ask
Speaker 4: questions about what you're not showing them. So, for example,
Speaker 4: like a product demo video, like this startup company called Nicola,
Speaker 4: which is still around trying to build electric vehicles trucks.
Speaker 4: In this case, they created a demo video of one
Speaker 4: of their trucks tooling down a highway, looked like going
Speaker 4: at a nice rate of speed, and addingpressive music behind
Speaker 4: it and so on, counting on people not to realize,
Speaker 4: not to think, well, wait a minute, what happened before
Speaker 4: the demo started. What was the angle that the camera
Speaker 4: was at. Actually the camera was tilted a little bit,
Speaker 4: so actually what was appeared to be rolling down on
Speaker 4: a flat surface was rolling down a hill. So the
Speaker 4: thing actually had no functioning motor you know, and so on.
Speaker 4: It just rolled down a hill slowly, and then the
Speaker 4: positioning of the camera and the video, you know, cutting
Speaker 4: did the rest of the work. And those are things
Speaker 4: we just don't think about. Right, We're focusing on the truck.
Speaker 4: It looks nice, it's moving, nice background, and so on,
Speaker 4: and we don't ask what's missing, Like, what information are
Speaker 4: we missing about what's here? What information are they not
Speaker 4: providing to us? Are they telling us about all the
Speaker 4: times they tried to make the vehicle work but it
Speaker 4: just didn't work at all, and just showing us the
Speaker 4: one time that it did. So attention focus is really useful,
Speaker 4: but it creates, you know, it sort of creates a loophole.
Speaker 4: It creates a way for other people to, you know,
Speaker 4: to exploit that well.
Speaker 1: One cognitive shortcut that you mentioned in the book is prediction.
Speaker 1: So how is it that we become victims of our
Speaker 1: own life experience?
Speaker 5: Yeah, And I think that's a great way of phrasing it,
Speaker 5: that it's our life experience. It makes sense for us
Speaker 5: to have expectations based on our past experience and to
Speaker 5: use those predictions. And the vast majority of the time
Speaker 5: we can use our past behavior to predict what's going
Speaker 5: to happen in the future, right, that that's a really
Speaker 5: important thing to be able to do. The challenge comes
Speaker 5: in that we don't tend to question enough the information
Speaker 5: we get when it's perfectly consistent with what we predicted. So,
Speaker 5: and this is something that I think is really interesting
Speaker 5: in the context of scientific errors. So let's say you
Speaker 5: run an experiment and you've got an experimental group and
Speaker 5: a placebo group, and you want to see which one
Speaker 5: does better. Right, and you're predicting your new experimental intervention
Speaker 5: is going to do great. And let's say that you
Speaker 5: find that the placebo condition actually does better than the
Speaker 5: experimental condition. Well, you're going to really dig into those results.
Speaker 5: You're going to dig into the data. You're going to
Speaker 5: look at your code. You're going to make sure that
Speaker 5: everything was coded correctly, that there weren't any data points
Speaker 5: that didn't make sense. You're to make sure you didn't
Speaker 5: swap the names of the conditions so that you got
Speaker 5: it wrong. You're going to look into it pretty carefully
Speaker 5: because it didn't match what you were predicting.
Speaker 3: Had it come out.
Speaker 5: Exactly the way you predicted, you might not dig as
Speaker 5: closely and that's been something that's led to a lot
Speaker 5: of errors. Right, So you have a spreadsheet that produces
Speaker 5: the right results, and you don't double check to make
Speaker 5: sure you didn't fill down the column incorrectly because it
Speaker 5: matched what you were predicting. So that sort of error
Speaker 5: is I think a really common one. We're really good
Speaker 5: at applying our critical faculties when we see something we
Speaker 5: don't like that we didn't expect that we didn't predict.
Speaker 5: Somebody shares something on social media that was counter to
Speaker 5: your views, you can rip into that, and we're all
Speaker 5: pretty good at doing that, But when it perfectly matches,
Speaker 5: we're much more likely to just quickly pass it along
Speaker 5: and retweet it and not necessarily think through carefully is
Speaker 5: it really true.
Speaker 1: Indeed, when we see things that are familiar in matching
Speaker 1: our expectation, we don't look further into it. So how
Speaker 1: do we work around that bias?
Speaker 4: Well, I would say obviously the first thing is to
Speaker 4: be where that we're doing this. That's the first step.
Speaker 4: Second step is to and again like not every moment
Speaker 4: of every day, but when you're making a big decision,
Speaker 4: or when you think that the stakes are high, or
Speaker 4: when someone might be trying to deceive you ask consciously,
Speaker 4: explicitly whether you predicted what just happened, and if you
Speaker 4: did predict it, then actually check it out as well.
Speaker 4: I think a lot of times we don't even sort
Speaker 4: of stop to wonder whether this is coming out exactly
Speaker 4: the way I predict it, because you know, things rarely
Speaker 4: happen exactly the way we predict, you know, especially in
Speaker 4: an environment we don't have a lot of experience with before.
Speaker 4: When we're doing a new experiment, testing a new theory,
Speaker 4: should it really come out exactly like we predict I
Speaker 4: mean maybe if we're the big best scientist ever, you know,
Speaker 4: but often it doesn't go that way. So we should
Speaker 4: be vigilant at those points also to see like, is
Speaker 4: our code right, did we you know, did we make
Speaker 4: a mistake or something like that. The example that Dan
Speaker 4: gave about switching the columns, you know, or switching the
Speaker 4: variable names or something like that is actually exactly what
Speaker 4: happened in a you know, a fairly recently uncovered case
Speaker 4: where where the data totally did not support the claim
Speaker 4: that was being made. This was a study of the
Speaker 4: idea that signing a declaration at the top versus at
Speaker 4: the bottom would make you more honest in what you
Speaker 4: declared on that form. So, in this case it was
Speaker 4: an automobile insurance company. They were asking people to report
Speaker 4: how many miles they had driven their vehicles in the
Speaker 4: previous year. And the test was sign at the top
Speaker 4: saying you're going to be honest in you're reporting, versus
Speaker 4: sign at the bottom saying you've been honest in what
Speaker 4: you reported. The idea was like, signing first would draw
Speaker 4: your attention to honesty, and you'd you know, produce more
Speaker 4: you know, more accurate, more honest results in that case.
Speaker 3: And when.
Speaker 4: When this experiment was done and the data file was
Speaker 4: being looked at by some of the researchers, initially it
Speaker 4: seemed like there was no effect at all, or the
Speaker 4: effect was even the opposite of what they had expected.
Speaker 4: But then one of them said, oh, well, accidentally switched
Speaker 4: the columns, you know. So once the columns were switched back,
Speaker 4: then the effect, you know, turned out to be right
Speaker 4: basically exactly as Dan said, you know, you switched sort
Speaker 4: of the you know, the treatment and the placebo in
Speaker 4: this case, the sign first and sign and sign later columns. Well,
Speaker 4: it turned out in retrospect that the entire data set
Speaker 4: was fraudulent. But once they got the result that you know,
Speaker 4: that fit the theory, or fit the prediction, or fit
Speaker 4: the expectations, then apparently they stopped looking to see does
Speaker 4: the rest of the data make sense? Are there any
Speaker 4: obvious red flags in there?
Speaker 3: And so on.
Speaker 4: I think a perfect example of at least, you know,
Speaker 4: some authors of that paper being satisfied that they're you know,
Speaker 4: the theory had been confirmed and not looking more deeply enough.
Speaker 4: Of course, you know, researchers are taught to look at
Speaker 4: the distributions of their variables, look at all of this
Speaker 4: kinds of stuff and so on, before getting too excited
Speaker 4: about just you know, confirming their hypothesis. But sometimes that's
Speaker 4: hard even for experienced scientists to do so. In our
Speaker 4: own everyday life, we should be more aware of when
Speaker 4: our expectations are being sort of exquisitely satisfied. That could
Speaker 4: be someone deliberately designing something to you know, to take
Speaker 4: advantage of.
Speaker 5: Us, say in a much more mundane case, you know,
Speaker 5: before you repost something or share it on social media,
Speaker 5: just ask yourself, is it really true?
Speaker 3: Right?
Speaker 5: And what would I need to know to be sure
Speaker 5: that it was really true, And that's something you can do,
Speaker 5: whether or not you agree with it, and it just
Speaker 5: takes a second. But once you ask that question, you
Speaker 5: might realize, I have no idea how i'd know if
Speaker 5: that were actually true. You know, I'd have to do
Speaker 5: a lot of digging. And then you know, maybe just
Speaker 5: don't reshare things that you haven't been able to verify
Speaker 5: that might might actually help prevent the spread of information.
Speaker 4: I think most people really do want to only share
Speaker 4: true stuff. I don't think people deliberately want to spread
Speaker 4: false information a lot of time. I think they're just
Speaker 4: not often thinking like whether it might be false that
Speaker 4: they're being swept along by other cues.
Speaker 1: Besides that, I wanted to jump back to the science
Speaker 1: the practice of science for just a second, which is
Speaker 1: I was just talking some colleagues who got a big
Speaker 1: data set and they wanted to prove something in particular
Speaker 1: about it. They got this big thing of police records
Speaker 1: and they had a particular thing that they wanted to demonstrate,
Speaker 1: and they analyzed it and couldn't find evidence for their hypothesis,
Speaker 1: figured out another way to look at it statistically, and
Speaker 1: then another way, and they still could fine and finally
Speaker 1: came up with some way, some statistical trick, and they
Speaker 1: were very proud, they said, to have found finally this
Speaker 1: evidence of this bias that they were looking for in
Speaker 1: these police records. But it made me wonder about it,
Speaker 1: because they clearly went in to find this thing. And
Speaker 1: the question is did they do the right thing by
Speaker 1: continuing to search and search and search with different statistical methods,
Speaker 1: or is it purely that they were trying to make
Speaker 1: the duck quack in a particular way. How do you
Speaker 1: think about these issues?
Speaker 5: It's a really complicated problem because, of course you want
Speaker 5: to be able to explore your data right. The problem
Speaker 5: comes when you don't think about all of the alternative
Speaker 5: paths you could take to get to the outcome that
Speaker 5: you want to report, right. And Andrew Gellman refers to
Speaker 5: this as the garden of forking paths. And I think
Speaker 5: it doesn't imply any sort of malicious intent or intent
Speaker 5: to deceive at all. But we make lots of choices
Speaker 5: along the way that can influence the result, and sometimes
Speaker 5: we don't even think about what those choices were. So
Speaker 5: I think the problem comes not in exploring your data
Speaker 5: really fully. It comes in only reporting the thing that worked,
Speaker 5: the one example, the one analysis that was successful, And
Speaker 5: what you really want to know is, hey, is this
Speaker 5: hypothesis robust to a whole bunch of different ways of
Speaker 5: testing it? And it sounds like in that particular case
Speaker 5: it wasn't at all Right. All of the other ways
Speaker 5: you look at it, you don't find anything. You only
Speaker 5: find it if you look in this one particular way. Well,
Speaker 5: that would be an important thing to know, right, That
Speaker 5: would be important for the science in the field to
Speaker 5: know that this only works in this one study if
Speaker 5: you measure it this way, and if you fish around enough,
Speaker 5: you'll find something that could be consistent, which means that
Speaker 5: maybe we shouldn't trust that a whole lot until we
Speaker 5: can replicate with that particular way of analyzing the data
Speaker 5: and see if that holds up consistently. We should also
Speaker 5: check to make sure that it holds up for real
Speaker 5: reasons as opposed to just something odd about how you've
Speaker 5: constructed the measure. Right, it might be that it's completely
Speaker 5: reliable when you measure it that way, but it's sort
Speaker 5: of an artifact of the structure of data of that sort.
Speaker 5: So I think the most powerful way to do that
Speaker 5: to say, hey, I want to make a claim that
Speaker 5: we've discovered some relationship with some bias. Well, if you
Speaker 5: want to claim that it's a general truth about how
Speaker 5: the world works, you want to be able to show
Speaker 5: that it works under a range of different ways of
Speaker 5: measuring it and under a range of different conditions, not
Speaker 5: just the one special one that you identified. And I
Speaker 5: think this has been an issue in our field for
Speaker 5: a long time, is that you know, there's obviously a
Speaker 5: goal to try and support the theories that you're working under.
Speaker 5: That's a natural thing to be doing, and it's not
Speaker 5: necessarily a terrible.
Speaker 3: Thing to be doing.
Speaker 5: But we haven't been completely straightforward as a field in
Speaker 5: reporting all the things we've tried, and depending on the
Speaker 5: kind of approach you're taking, that can be really misleading.
Speaker 5: If you don't report everything the way it was done,
Speaker 5: it's cherry picking in a sense. You're taking out the
Speaker 5: results that you wanted and ignoring all the ones that
Speaker 5: didn't work, and you're left with the reader only having
Speaker 5: in the paper that you read about it, focusing on
Speaker 5: the information that you've told them. And it's just like
Speaker 5: a magician who's directed you to the thing they want
Speaker 5: you to see and hidden all of the secret methods.
Speaker 3: That get you there.
Speaker 4: Yeah, you, in a way, as the researcher who did that,
Speaker 4: have inadvertently become the con artist, although you had no
Speaker 4: intention to do it, and you're not actually trying to deceive,
Speaker 4: but you're accidentally sort of using some of those very
Speaker 4: same techniques that people could use, you know, to do
Speaker 4: worse things than publish a paper that didn't you know,
Speaker 4: that didn't actually have good evidence for its conclusions.
Speaker 1: Yeah, it strikes me that one of the things as
Speaker 1: I was reading this excellent book, a lot of this
Speaker 1: just has to do with taking the tools of good
Speaker 1: science to the way that we interpret the world around us.
Speaker 1: So the things about asking more questions and digging deeper
Speaker 1: and so on. But it's interesting that even scientists don't
Speaker 1: always do good science.
Speaker 5: Yeah, we're all capable of being fooled, right in. Scientists
Speaker 5: and maybe journalists are trained to dig more and ask
Speaker 5: more questions and to think critically about what they're hearing.
Speaker 5: But you know, we're human. We have the same sorts
Speaker 5: of habits and ways of thinking. We tend to like
Speaker 5: results that support what we predicted, and we're drawn to
Speaker 5: the same kinds of information, and if somebody's looking to
Speaker 5: sort of hide what they're doing, they can fool scientists.
Speaker 5: And there are plenty of fraudulent papers out there that
Speaker 5: got through peer review even though there were red flags.
Speaker 5: And just like that sort of heist movie that you
Speaker 5: watch from the outside and you see all of the
Speaker 5: red flags along the way that people are falling for,
Speaker 5: but you're not falling for them because you're watching them.
Speaker 5: In hindsight, they're all obvious, right, but in that moment,
Speaker 5: you don't necessarily see them as red flags until you know, oh, wait,
Speaker 5: that paper was fraudulent. I found out through other means.
Speaker 5: Now I can see all the red flags that are there.
Speaker 1: Yes, So as I was reading the book, the way
Speaker 1: I was thinking about it was, you know, the brain
Speaker 1: is of course locked in silence and darkness inside the skull,
Speaker 1: and it's just trying to make an internal model of
Speaker 1: the world out there, a mental model, and we're always
Speaker 1: we're always very limited in what our internal models can detect,
Speaker 1: can see, And so one of the most important things
Speaker 1: to expanding our model is to ask questions. And in
Speaker 1: a sense this is the same as paying attention to something.
Speaker 1: We ask a question that forces us to attend to
Speaker 1: some aspect and then that updates our model a little bit.
Speaker 1: So in chapter four, you guys had an example of
Speaker 1: a chess grand master that asks his students to always
Speaker 1: ask three questions when they're looking at the board?
Speaker 2: Can you tell us about that?
Speaker 4: Would I would love to tell you. I would love
Speaker 4: to tell you about that. So I actually took a
Speaker 4: during COVID, I took a summer chess camp on Zoom
Speaker 4: with this guy and it was me and like twelve
Speaker 4: people aged ten and under, which was a fun you know,
Speaker 4: which was a fun again because who goes to summer
Speaker 4: chess camps, right, It's like, you know, kids gauge ten
Speaker 4: and under. Very good players by the way. And you
Speaker 4: know one thing that he often that the coach would
Speaker 4: often do. His name is jako Ogard. He's one of
Speaker 4: the most famous chess coaches in the world, and it
Speaker 4: was privileged to be able to sort of be in.
Speaker 3: His camp for a few hours.
Speaker 4: He would constantly say, we need more, you know, you
Speaker 4: need to think of more moves. Right in a chess position,
Speaker 4: there's like thirty to forty moves you can typically play,
Speaker 4: you know, with all your pieces and people are often
Speaker 4: become too focused on one. So he would say, think
Speaker 4: of more candidate moves. Think of more moves you might
Speaker 4: want to play and analyze. And if you're having trouble
Speaker 4: thinking of them, he has specific questions that you can
Speaker 4: use to try to generate ideas, and one of them
Speaker 4: is what's your worst place piece? Maybe you should move
Speaker 4: it if that's the one that's in the worst position.
Speaker 4: Or what's the opponent's idea, Well, maybe you should come
Speaker 4: up with a move that stops their idea. The third
Speaker 4: one is what are the weaknesses? Maybe you should come
Speaker 4: up with a move that attacks something that's weak. I
Speaker 4: mean this is this will make sense to people who
Speaker 4: play chess, but these are they're in almost all fields.
Speaker 4: There are sort of general kinds of things you can
Speaker 4: look at and principles you can use to generate, you know,
Speaker 4: to generate more information and as you say, like you
Speaker 4: I like your way putting it to sort of improve
Speaker 4: your mental model of what's really going on, because in
Speaker 4: order to in order to play good chess moves, you
Speaker 4: have to have a good internal model of what's going
Speaker 4: on on the board someplace in your brain. You've got
Speaker 4: to have it and that's a way of sort of
Speaker 4: generating more ideas, more analysis that then updates that then
Speaker 4: updates the model.
Speaker 5: And most of the time the models that we have
Speaker 5: for how the world works are pretty.
Speaker 3: Good, right.
Speaker 5: I mean, we're not, you know, constantly getting conned. We're
Speaker 5: not you know, we don't have trouble getting around, we
Speaker 5: don't have trouble communicating with other people. Most of the time,
Speaker 5: our models of how the world is working are great.
Speaker 5: They work very effectively. And it's only in those cases
Speaker 5: where we need to dig a little more to update
Speaker 5: our model for the possibility we're being teated or deceived
Speaker 5: that we need to ask a lot more questions. Right,
Speaker 5: most of the time, we've built up these models from
Speaker 5: a ton of experience, and they generally do okay, Yeah.
Speaker 1: And we have expectations about what we're looking for given
Speaker 1: these models, such that most of the time we're filling
Speaker 1: in the blanks. And that is at the heart of
Speaker 1: all these hoaxes and scams that you talk about throughout
Speaker 1: the book, is we're filling in the blanks, and the
Speaker 1: things that aren't said, we assume we.
Speaker 2: Know what they mean.
Speaker 1: So I'm curious what you guys think about the Turing
Speaker 1: tests and for the listeners in case someone doesn't know.
Speaker 1: The Turing test was proposed by Alan Turning to figure
Speaker 1: out when a machine has become as smart as the human.
Speaker 1: The idea is that you are the evaluator and you're
Speaker 1: talking to a machine, and you're talking to a human,
Speaker 1: let's say, by text, and you don't know which is which,
Speaker 1: and the question is can you tell the difference between
Speaker 1: the human and the machine. And the interesting part is,
Speaker 1: because we bring so much to the table in any
Speaker 1: conversation and we fill in the blanks, what do you
Speaker 1: guys think Is that a good, meaningful test or is
Speaker 1: it flawed in that way?
Speaker 4: I think we have a lot of evidence that it's
Speaker 4: not so good from chat, JPT and large language models,
Speaker 4: which I don't think are actually intelligent in the way
Speaker 4: that we should. I mean, I realized there might be
Speaker 4: some dispute about this. Some people make some sort of
Speaker 4: extravagant claims about signs of general intelligence, but I don't
Speaker 4: think they're actually intelligent, or at least not in a
Speaker 4: useful way. And yet they are extremely convincing. I mean,
Speaker 4: I think they show that you can sort of dissociate,
Speaker 4: you know, producing what humans expect to see next, which
Speaker 4: is basically, you know, basically what large language models do
Speaker 4: because they've been trained to sort of, you know, output
Speaker 4: the most probable next token or word and so on.
Speaker 4: You can dissociate that capability from having sort of a
Speaker 4: true understanding of what's going on. For example, you could
Speaker 4: ask an LLLM to play chess with you, and it
Speaker 4: wouldn't do very well. It would produce a lot of
Speaker 4: stuff that sounds like chess and so on. But if
Speaker 4: you really knew the game of chess, you would know
Speaker 4: that this is sort of gibberish. If you don't know,
Speaker 4: it sounds perfectly good, right, You just you just sort
Speaker 4: of you fill it in with sort of the assumption
Speaker 4: that this guy sounds like he knows what he's talking about,
Speaker 4: you know, which is which is not not you know,
Speaker 4: not necessarily intelligence things.
Speaker 5: Like chat GPT they speak with absolute confidence and certainty, right,
Speaker 5: and that's regardless of whether or not they're generating true content.
Speaker 5: You know, they're the consonant bullshitter right in that they
Speaker 5: are equally confident when they're completely wrong and when they're
Speaker 5: completely right. Because there's no grounding to any sort of
Speaker 5: reality in the world. All they're doing is predicting what
Speaker 5: comes next.
Speaker 1: That's true, although I have to say one of the
Speaker 1: things about chat GPT that I've really come to appreciate
Speaker 1: is that almost any question that you ask it, it'll say, look,
Speaker 1: some people think this, some people think that, and in conclusion,
Speaker 1: we need to balance these points of view. And at
Speaker 1: first I found that really annoying, but I came to
Speaker 1: understand and appreciate that it is trying, you know, because
Speaker 1: of the reinforcement learning and so on, it's trying to
Speaker 1: give different perspectives instead of sounding totally confident about just
Speaker 1: a single answer.
Speaker 4: You kind of wish politicians would talk to you that
Speaker 4: way sometimes exactly instead of the way they do it. Yeah,
Speaker 4: well it's it's it's reasonable. I just don't think chet
Speaker 4: GPT believes that both of those things are equally you know,
Speaker 4: are equally likely to be true and so on in
Speaker 4: any in any meaningful way. I think, you know, it's
Speaker 4: the part of the danger I think of of models
Speaker 4: like this is if you don't understand how they work,
Speaker 4: and if you just see this is a I like
Speaker 4: a lot of times you see these posts on Twitter
Speaker 4: that say and A n AI did this that's just
Speaker 4: designed to impress you, right and to mislead you by
Speaker 4: thinking that therefore the results must be created by genius
Speaker 4: and totally accurate. But if you actually understand something about
Speaker 4: how it works, then you will have the reaction you did.
Speaker 3: You'll say, well, this is this is kind.
Speaker 4: Of interesting that it's trying to give you know, sort
Speaker 4: of too equally like probable you know, schools of thought here,
Speaker 4: or you know common you know, views on the topic
Speaker 4: or something like that, but you understand something about how
Speaker 4: they work, so it doesn't you know, they their output
Speaker 4: sort of is more sensible to you than it might
Speaker 4: be to people who don't know.
Speaker 1: I want to come back to just something you mentioned
Speaker 1: about politicians. One of the things I was thinking about
Speaker 1: as I was reading the book was politicians often get
Speaker 1: points deducted if they change their mind on something.
Speaker 2: They're called flip floppers.
Speaker 1: And it's such a shame because we know that if
Speaker 1: people are using scientific reasoning, they might reasonably change their
Speaker 1: mind about some issue.
Speaker 2: At some point.
Speaker 1: The question is, how would you guys see a way
Speaker 1: to change something about the way we educate the public
Speaker 1: so that politicians who change their mind on a topic
Speaker 1: are not considered flip floppers and deducted points.
Speaker 4: Now, I guess I would say that this refers back
Speaker 4: to our taste for consistency. So there's something appealing in
Speaker 4: consistency of a wide variety and consistency and a person's
Speaker 4: behavior is also appealing to us. And many times that's good.
Speaker 3: Right.
Speaker 4: You want someone who keeps their word. You want someone
Speaker 4: who says what they are going to do and then
Speaker 4: does what they said they would do. You want someone
Speaker 4: who's always on time. Like those are generally positive things.
Speaker 4: But when you're talking about complex subjects like what should
Speaker 4: climate policy be? You know, what should tax policy be?
Speaker 4: Any of these kinds of things and so on, facts
Speaker 4: change over time, you know, and that alone, never mind
Speaker 4: you know, using scientific reasoning, but just the changing of
Speaker 4: facts might, you know, might change people's views. I think
Speaker 4: teaching people about the trap of consistency, you know, may
Speaker 4: start to help. I'm not sure there's like an easy
Speaker 4: nudge that is going to make people suddenly prefer the
Speaker 4: flip flopper, because, after all, sometimes the flip flopper is
Speaker 4: just being expedient, right. It's not as simple as saying, like, well,
Speaker 4: we should anytime someone flip flops. We should just assume
Speaker 4: that they are have done some complex ratios nation and
Speaker 4: integration who do information, and now they've changed their mind.
Speaker 4: It could be they're just saying something to a different
Speaker 4: audience because that's what is expedient for them at the moment.
Speaker 4: It's a tough problem to sort out, but certainly I
Speaker 4: don't think we should have the bias against changing one's
Speaker 4: mind that it is really seems to be built into
Speaker 4: the political system, because I do think it contributes to
Speaker 4: some polarization also, Right, in order to be a consistent conservative,
Speaker 4: you always have to do this, in order to be
Speaker 4: a strong you know, it's definitely it definitely has its cost.
Speaker 5: Well, and you know, scientists also aren't necessarily the greatest
Speaker 5: at updating their beliefs in light of evidence.
Speaker 2: Right.
Speaker 5: There are plenty of people who get evidence that should
Speaker 5: contradict their claims, but they continue arguing for the old
Speaker 5: position without fully updating their beliefs accordingly, Right, every time
Speaker 5: somebody encounters have failure to replicate their own work, right,
Speaker 5: what's their reaction, Well, I'm going to try and find
Speaker 5: all of the things that were done differently in that
Speaker 5: replication attempt that could maybe excuse why they didn't find
Speaker 5: what I found, And what they really should do is say, Okay,
Speaker 5: you know that may or may not be right. I
Speaker 5: might disagree with it, but it should make me a
Speaker 5: little less likely to believe in my original result. And
Speaker 5: maybe there are alternatives and I need to go test those,
Speaker 5: and if those alternatives don't work out, then I should
Speaker 5: be changing my beliefs. But you don't see that all
Speaker 5: that often. You don't see the person going back to
Speaker 5: the study and saying, Okay, I'm going to prove that
Speaker 5: it was this moderation that explained why they get They
Speaker 5: didn't get it, but I did. More often than not,
Speaker 5: you have a dismissal Right, Well, that's really not all
Speaker 5: that different than what a politician does when they refuse
Speaker 5: to change their views in light of new data.
Speaker 3: Yeah, you know.
Speaker 1: I think this is back to this issue of complexity
Speaker 1: in science, which is that it essentially works by the
Speaker 1: advocacy system, which is that you're supposed to defend an
Speaker 1: idea all the way down until you can't defend it
Speaker 1: anymore and then you give up on it. But so
Speaker 1: when someone says, oh, I didn't replicate your thing, you
Speaker 1: might be the only one who says, hey, I'm willing
Speaker 1: to defend this and really fight for the idea until
Speaker 1: I reached some point. The question is what is the
Speaker 1: proper point? And as we said, scientists are humans too,
Speaker 1: and so they really care about their reputation and their
Speaker 1: previous publications.
Speaker 5: And that's fine, So it's what's what's the point? But
Speaker 5: also what evidence do you use to defend it?
Speaker 3: Yeah? Right?
Speaker 5: So if if the evidence you used to defend it is, hey,
Speaker 5: we can still get our result and we can show
Speaker 5: why you didn't get it, that's great, right, Then you
Speaker 5: then you've bolstered your position. You've let everybody to update
Speaker 5: with their beliefs. Are If your defense is they're incompetent,
Speaker 5: that's not a great defense. It might be true, but
Speaker 5: you'd have to show why and then show that if
Speaker 5: you do it in the incompetent way, you don't get
Speaker 5: the result.
Speaker 4: Well, according to the adage that science progresses one funeral
Speaker 4: at a time, that point is the point of death. Right,
Speaker 4: And so there is a generational aspect to some of
Speaker 4: these to some of these things. I think there's there, seriously,
Speaker 4: is right, there's sort of generations. Generations often are associated
Speaker 4: with particular schools of thought or views that sort of
Speaker 4: pass and.
Speaker 3: And and evolved.
Speaker 4: It would be great if we could get there before then, right,
Speaker 4: So we should get there before that point. But it's
Speaker 4: I don't think I'm not so bleak. I'm not so
Speaker 4: blik on this maybe as some people, because I think
Speaker 4: a lot of times what happens is people just sort
Speaker 4: of drop out of the debate. Right. They may not
Speaker 4: have changed their mind, but they don't become an important
Speaker 4: person in that in that area anymore, or at least
Speaker 4: the next generation who's doing all the interesting work doesn't
Speaker 4: take those people that seriously anymore. They're still alive where
Speaker 4: you know, their funeral hasn't happened yet, but they maybe
Speaker 4: moved on to another topic. They're doing something else in
Speaker 4: science maybe, but they're not like exerting like an iron
Speaker 4: you know, an iron fist, you know, rule over some area,
Speaker 4: like when a science works that way, that's pathological.
Speaker 1: Right.
Speaker 4: But you know when when someone's views like you know,
Speaker 4: must be then then we're you know, you're not talking
Speaker 4: about science anymore, right. I think a lot of people
Speaker 4: just sort of move on instead of before it's too late.
Speaker 1: Yeah, And this is the great part about science is
Speaker 1: that it is the only endeavor that's constantly knocking down
Speaker 1: its own wall.
Speaker 2: So with enough time, the truth will out.
Speaker 1: Things are changing really rapidly ever since you, you know,
Speaker 1: finished writing the book and sent it off, And so
Speaker 1: one of the things that I want to ask about
Speaker 1: are things like what's happening with AI and deep fakes.
Speaker 1: What are the new kinds of hoaxes that you see
Speaker 1: coming down the line.
Speaker 5: Well, I'll raise one that's not new, And I think
Speaker 5: it's important to point out that none of the hoaxes
Speaker 5: that have happened over the thousands of years are really
Speaker 5: fundamentally all that different in the way that they take
Speaker 5: advantage of our tendencies, right, And that's the thing that
Speaker 5: we noticed across all of these different domains, from chess
Speaker 5: to sports, to art to science, that they all take
Speaker 5: advantage of the same sorts of tendencies. And new scams
Speaker 5: are going to do that too, they just might do
Speaker 5: it more effectively. And even the Nigerian Prince email scam
Speaker 5: was originally a Nigerian prince mail scam back when people
Speaker 5: sent letters.
Speaker 3: It's more effective in.
Speaker 5: That they don't have to spend as much time and
Speaker 5: effort finding potential victims. Right, some of these scams with
Speaker 5: the advent of AI are going to become more effective.
Speaker 3: Right.
Speaker 5: So one that's common right now is it's either the
Speaker 5: you know your kid's been arrested scam or has been
Speaker 5: in an accident or is being held hostage. It's a
Speaker 5: horrible thing, preying on people's fears. They'll call up a
Speaker 5: parent or a grandparent and say that the kid needs
Speaker 5: to be bailed out immediately, right, and often they'll call up,
Speaker 5: you know, a relative like a cousin or something. Now,
Speaker 5: that scam's pretty effective because people want to quickly solve
Speaker 5: this problem, right, They want to quickly fix what is wrong,
Speaker 5: and often we don't have the preventive measures in place
Speaker 5: to stop that. But imagine how much more powerful that
Speaker 5: is if you're using AI voice synthesis to make the call,
Speaker 5: right and it actually sounds like it's coming from that person.
Speaker 5: That's going to ramp that one up, same principles, it's
Speaker 5: just more potent.
Speaker 3: I think a.
Speaker 4: Whole area which is rife with scams, which is a
Speaker 4: new area, but you know, sort of re scamming based
Speaker 4: on old principles as cryptocurrency, So there are thousands and
Speaker 4: thousands and thousands of cryptocurrencies and coins being issued and
Speaker 4: so on, and you know, as far as I can tell,
Speaker 4: most of that is mostly fraud. But yet it relies
Speaker 4: on all the same principles. You've got sort of like
Speaker 4: famili your celebrities advertising these things. You've got time pressure,
Speaker 4: there's a limited offering. You know, you've got to make
Speaker 4: a decision. Now, You've got these sort of like fake consistency,
Speaker 4: Like people will claim that like our crypto fund is,
Speaker 4: you know, never had a down month in all of
Speaker 4: its three months of existence or something like that, you know,
Speaker 4: but consistently going up, and all the same stuff just
Speaker 4: being applied to a whole new being applied to a
Speaker 4: whole new thing. And I don't really see that, you know,
Speaker 4: getting any better. I as far as AI in deep
Speaker 4: fakes and so on, I do have some optimism that
Speaker 4: it's going to increase the value of truly trusted sources
Speaker 4: who bother to check that stuff.
Speaker 3: Right, So I.
Speaker 4: Noticed during there were not long ago there but this
Speaker 4: is sort of pseudo attempted coup revolution weird thing that
Speaker 4: happened in Russia. You know, a sort of paramilitary group
Speaker 4: kind of turned on the military and started marching to
Speaker 4: Moscow and so on. And I was fascinated by this
Speaker 4: and paying attention to Twitter, and there were all kinds
Speaker 4: of reports on Twitter, people claiming to be like eyewitnesses
Speaker 4: to things and so on, and very little of that
Speaker 4: made it to the mainstream media or to legitimate sources.
Speaker 4: And I thought about it afterwards, I thought, why is that?
Speaker 4: You know, well, maybe some of it was true, but
Speaker 4: probably most of it just couldn't be verified. You know.
Speaker 4: It was like one guy said they saw something, they
Speaker 4: couldn't find someone else who saw the same thing, They
Speaker 4: couldn't find the underlying you know, whatever it was that
Speaker 4: was supposedly the source of the evidence. But nonetheless the
Speaker 4: story that emerged, although a little more vague and abstract
Speaker 4: with less detail, was probably much more likely to be
Speaker 4: true because it was sort of filtered through agents that
Speaker 4: bother to check and try to only pass on verifiable information.
Speaker 4: And they are now faced with the problem of how
Speaker 4: do you tell whether this video of Trump doing X
Speaker 4: is actually Trump doing it or some fake that someone created, right,
Speaker 4: But I don't know who else to put more trust in,
Speaker 4: you know, for sorting that out than journalists and or
Speaker 4: and there are some organizations that they work with who
Speaker 4: are experts at detecting these kinds of things and so on.
Speaker 4: So I think maybe it might paradoxically increase the value
Speaker 4: and the attention paid to more legitimate sources, which I
Speaker 4: think would probably be a good thing on balance.
Speaker 5: I mean, the pessimistic view is that these things get
Speaker 5: increased in scale, right, it makes it much easier to
Speaker 5: scam at large scale and make it sound plausible, right.
Speaker 5: But the optimistic take is exactly what Chris was saying,
Speaker 5: that once we realize that these things are possible at scale,
Speaker 5: maybe we start being more skeptical of most of the
Speaker 5: sort of rapid information that we get, and we withhold
Speaker 5: judgment just a little bit longer until we can have
Speaker 5: some verified sources. And the idea would be if we
Speaker 5: could actually have verified sources again, we haven't had that
Speaker 5: for a while. Now that anybody can start up a
Speaker 5: cable network and say whatever they want.
Speaker 1: This is something I've been wondering about recently, is all
Speaker 1: of these things that we're very concerned about, like deep fakes,
Speaker 1: will the younger generations be much less susceptible to them
Speaker 1: because they're well aware that if you see a video
Speaker 1: of something, it might be real, it might be fake,
Speaker 1: as opposed to know, those of us who are older
Speaker 1: are really concerned about it in a way that we
Speaker 1: might not need to be.
Speaker 3: I think that's a really good question.
Speaker 4: I think the jury still out on that, because I
Speaker 4: think in some ways younger people are a bit more
Speaker 4: naive about some things. They don't have certain experiences and
Speaker 4: so on. On the other hand, as you say, they
Speaker 4: may be more used to the idea that videos are
Speaker 4: not proof the way people who grew up in an
Speaker 4: era of less video and less awareness of video editing
Speaker 4: and so on might not be. I'm reminded of the
Speaker 4: sort of discussion that you heard, you know, fifteen to
Speaker 4: twenty years ago about the so called digital natives and
Speaker 4: how having grown up with technology, they were so smart
Speaker 4: in using it and so on.
Speaker 3: And then when I became a college.
Speaker 4: Professor, I found out that students didn't really know how
Speaker 4: to do a proper Google search, you know, and so on,
Speaker 4: even though they were supposedly natives, like it's not in America,
Speaker 4: not being able to, you know, to speak English correctly,
Speaker 4: So that gives me less optimism. But I think in general,
Speaker 4: across generations. I think there's going to be a rise
Speaker 4: in a rise in skepticism, may be somewhat of a
Speaker 4: decline of truth bias. Truth bias can't decline too far
Speaker 4: otherwise we just can't interact with anybody anymore. But maybe
Speaker 4: a sort of a decline or a specialization of truth
Speaker 4: bias where you have sort of a little bit more
Speaker 4: truth bias in some areas, like when you're talking to
Speaker 4: an actual human being standing in front of you, and
Speaker 4: less when you're watching a video on TikTok. Like that
Speaker 4: would be a nice balance to have, right and not
Speaker 4: to pick on TikTok, but there seems to be more
Speaker 4: nonsense there than most other places, just from what I've noticed.
Speaker 1: Okay, so zooming out, give us some practical advice for people,
Speaker 1: some tips they can take home. Well.
Speaker 5: I'd say one quick one is that whenever you're in
Speaker 5: a situation where the consequences could be big, be willing
Speaker 5: to ask more questions. And it can be socially awkward
Speaker 5: to do that, right, to kind of press for more information,
Speaker 5: but doing that's essential if the consequences of being deceived
Speaker 5: are big, and sometimes you can kind of get started
Speaker 5: on asking questions without actually, you know, being hostile and aggressive,
Speaker 5: like can you tell me more? Is a way of
Speaker 5: getting somebody to talk a little bit more, give you
Speaker 5: a little more information that might actually make it more
Speaker 5: comfortable to ask questions about that more information they give you. Right,
Speaker 5: So the sorts of skills that many of us develop
Speaker 5: an academia. Or you're giving a talk and you can
Speaker 5: stand up and ask the hostile question, or you could
Speaker 5: ask a question that reveals more information, and the goal
Speaker 5: is to try and reveal more information and remain a
Speaker 5: little uncertain until you have that information. One broader one
Speaker 5: is if somebody were trying to scam me in this situation,
Speaker 5: Let's say you're investing in something. If somebody were trying
Speaker 5: to scam me, how would I know?
Speaker 1: Right?
Speaker 5: So, if I'm thinking about investing in crypto and say,
Speaker 5: is that a scam? How would I know if that's
Speaker 5: a scam. If you can't answer that question, then you
Speaker 5: probably should walk away.
Speaker 3: So if you.
Speaker 5: Don't understand how blockchain works and how crypto coins work,
Speaker 5: you probably shouldn't be investing in crypto. Regardless of what
Speaker 5: a celebrity tells you. If it were a scam, how
Speaker 5: would you tell well, and be really hard to tell
Speaker 5: if you don't understand how it works intimately. I'll give
Speaker 5: two practical ideas also. I think one is don't make
Speaker 5: really important decisions all by yourself. We came across many
Speaker 5: example where people were about to make mistakes, big mistakes, like,
Speaker 5: for example, one of those guys was about to give
Speaker 5: to wire money to the fake French defense minister, and
Speaker 5: his friend walked into the room where he was having
Speaker 5: this call with him, and he immediately right away said
Speaker 5: this can't be real. This must be a scam. And
Speaker 5: why was the friend able to notice but the victim,
Speaker 5: the intended victim wasn't. Well, probably the friend had not
Speaker 5: been in on all the previous conversations, so there wasn't
Speaker 5: sort of that sunk costs idea, that idea of a relationship.
Speaker 3: And so on.
Speaker 4: And maybe it was just he had a different mindset
Speaker 4: that day, He had a different attitude, he was thinking
Speaker 4: different things, and he never got sucked into the whole thing.
Speaker 4: So ask a friend, get an outside view before you
Speaker 4: make a big decision. Should I really send all my
Speaker 4: life savings to this guy, you know, just because everybody
Speaker 4: says he's the greatest thing, or is there any other
Speaker 4: consideration I should be using when investing my money.
Speaker 3: So that's one.
Speaker 4: The second one is like, do your work on deadlines,
Speaker 4: but don't like give away your money on deadline. So
Speaker 4: if anybody ever says, like, you know, you've got to
Speaker 4: do this within a certain period of time, the police
Speaker 4: are coming to your house if you don't like pay
Speaker 4: this bill right away, or this offers exploding very quickly,
Speaker 4: you know, or there's only one of these things left
Speaker 4: or whatever, just be aware that, like that's a prime
Speaker 4: environment to not have time to ask questions, not have
Speaker 4: time to think about the information you're missing, not go
Speaker 4: through any of this, and realize that, like, you can
Speaker 4: still buy that thing the next day if you really
Speaker 4: want it, you can still invest your money next week
Speaker 4: after you have checked the guy out.
Speaker 3: You're not going to lose much. I would go with
Speaker 3: those two.
Speaker 1: So that was Dan Simon's and Christopher Shabri. Now what
Speaker 1: we learn from them is that a lot of protecting
Speaker 1: ourselves against deception is about taking the tools of science,
Speaker 1: which is nothing but the tools of thinking clearly, and
Speaker 1: applying those to our daily lives. So, for example, if
Speaker 1: somebody says something is true, whether from their position of
Speaker 1: authority or religious status, or with a trust me on
Speaker 1: this one vibe. The key is to trust, but verify.
Speaker 1: The important thing to get in the habit of is
Speaker 1: just asking the next question. And it's tough because life
Speaker 1: doesn't allow questioning everything. Our schedules just don't allow that,
Speaker 1: and we have to operate on trust for most of
Speaker 1: what we do. And sometimes we find ourselves in a
Speaker 1: situation where someone doesn't quite answer the question we've asked,
Speaker 1: and it feels impolite to keep pressing on it. And
Speaker 1: also what is life if we don't trust? But the
Speaker 1: fact is we can always get a little smarter, a
Speaker 1: little less gullible by knowing that reality can be different
Speaker 1: in different heads. And whether we're talking about Tanya my
Speaker 1: fellow graduate student, or the dealer of the shell Game,
Speaker 1: or Elizabeth Holmes and Farahos or whatever, it's incredibly useful
Speaker 1: to stretch beyond the parochial limits of our mental models
Speaker 1: of the world, because with with more knowledge comes a
Speaker 1: bit more immunity, and understanding the character of our brains
Speaker 1: allows us to move through the world a little bit
Speaker 1: more smoothly than we would without that knowledge. Go to
Speaker 1: Eagleman dot com slash podcast. For more information and to
Speaker 1: find further reading, send me an email at podcasts at
Speaker 1: eagleman dot com with questions or discussion, and I'll address
Speaker 1: those in a special episode. Until next time, I'm David Eagleman,
Speaker 1: and this is Inner Cosmos.