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Speaker 1: Welcome to Stuff to Blow Your Mind from how stup

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Speaker 1: works dot com. Hey, welcome to Stuff to Blow your Mind.

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Speaker 1: My name is Robert Lamb and I'm Joe McCormick and Robert.

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Speaker 1: I've got a trivia question for you. All right, hittany,

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Speaker 1: this is for all of you out there listening as well.

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Speaker 1: When Marie Curie died, was she older or younger than

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Speaker 1: twenty seven years old? Think about your answer, older or

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Speaker 1: younger than seven? Okay, well, I have to say she

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Speaker 1: was definitely older. But I have to admit that I

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Speaker 1: read an excellent glow in the dark book about her

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Speaker 1: a few years back, titled Radioactive Marie and Pierre Curie,

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Speaker 1: A Tale of Love and Fallout, which, by the way,

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Speaker 1: heading into Valentine's Day, it's an excellent Valentine's Day book

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Speaker 1: to give somebody. Wait, hold on, so this is going

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Speaker 1: to be about them, but it's glow in the dark

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Speaker 1: signaling that the book is radio active and will poison

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Speaker 1: you and your fingers will fall off. And well, when

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Speaker 1: you put it like that, it doesn't sound very romantic,

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Speaker 1: but it's it's a it's a very romantic book. But

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Speaker 1: I know I know from having read that that she

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Speaker 1: she would live significantly longer than than her twenties. Well, okay,

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Speaker 1: so most people probably do know that. But here here's

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Speaker 1: another chance. Just guess what age she died. How old

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Speaker 1: was Marie Curry when she died? And just think about it.

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Speaker 1: This becomes a little harder for me because I have

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Speaker 1: I can clearly picture a photograph of her. I'm gonna say,

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Speaker 1: very close. Marie Cury died in nineteen thirty four at

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Speaker 1: the age of sixty six, So yeah, very close. Now

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Speaker 1: you listening at home, how close were you? Did you overshoot?

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Speaker 1: I assume not many people undershot the age. If you did,

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Speaker 1: that's okay, no shame in it. I tried this on

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Speaker 1: somebody yesterday and she guessed forty four. I did the

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Speaker 1: same thing. I said, older, younger than seven? What's the age?

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Speaker 1: And when when I told her the answer that it

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Speaker 1: was actually sixty six, the person I was talking to said, oh, well,

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Speaker 1: I've seen pictures of her that looked older than that,

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Speaker 1: but I guess I assumed it was from all the radiation. Okay,

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Speaker 1: I see where you're going with this. Though, the the

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Speaker 1: question you asked by putting twenty seven in there, you're

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Speaker 1: you're you're sort of lowering their expectations. It could be, Yeah,

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Speaker 1: maybe something's going on there. I've got another one for you.

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Speaker 1: When Sean Connery took a role in the film Highlander

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Speaker 1: to the Quickening one of his finest choices, Yes, the

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Speaker 1: planet's eist. When he took that role, what was his

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Speaker 1: salary for the role? Was it more or less than

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Speaker 1: thirty one million dollars? Okay, this one's tough for me

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Speaker 1: because I love movie trivia, but I'm not very good

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Speaker 1: with the economic movie trivia, so I don't even have

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Speaker 1: a very good starting point. It seems to me, though,

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Speaker 1: that sounds like an awful lot of money. Um, especially

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Speaker 1: for Highlander two. Yeah, Like that's that's some that's some

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Speaker 1: big Uh. That's that's some Tom Cruise money right there,

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Speaker 1: I would guess. So you're saying lower maybe, But then

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Speaker 1: this was you have to put yourself in a pre

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Speaker 1: Highland or two era, So Highlander two can't imagine it,

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Speaker 1: unable to process, cannot compute. It's It's true. I don't

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Speaker 1: think I watched Highlander. I got excited to watch Highlander

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Speaker 1: one after I saw trailers for Highlander two. I believe

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Speaker 1: that's how that went down. But yeah, not not knowing

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Speaker 1: what we know now about the the the public reception,

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Speaker 1: uh to to Highlander two, one could easily say, yeah

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Speaker 1: it was everyone was just totally optimistic. It was a

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Speaker 1: follow up to Highlander, which was arguably, you know, one

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Speaker 1: of the greatest films of its generation. Okay, so guess

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Speaker 1: taking out what was his actual salary? All right, you're

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Speaker 1: asking about thirty five one I'm going to have that

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Speaker 1: and say fifteen million? Is that a lot for Sean Connery? Oh?

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Speaker 1: Your way over the mark. Now, I do have to

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Speaker 1: admit that my answer comes from a sketchy looking website,

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Speaker 1: which is the only place I could find an answer

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Speaker 1: called like the movie time. So maybe this is wrong,

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Speaker 1: but the answer I could find said he was paid

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Speaker 1: three point five million. Okay, well, yeah I was way

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Speaker 1: over the line. Then yeah, but worth every penny really

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Speaker 1: and then some exactly right. But notice how far off

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Speaker 1: the mark you were given those starting questions. I asked,

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Speaker 1: was she older, younger than twenty seven? Or was it

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Speaker 1: more or less than thirty one million? And I wonder

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Speaker 1: to what extent those questions changed the kind of answer

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Speaker 1: you gave to your ultimate guests on her age at

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Speaker 1: death or on the movie salary? What would you have

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Speaker 1: said if you hadn't received those questions to start with? Well,

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Speaker 1: in the case of the Highlander question, I was just

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Speaker 1: kind of trying to reverse engineer and answer. I think

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Speaker 1: I would have I still would have missed the mark,

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Speaker 1: but I think I would have probably said something like

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Speaker 1: five or six million, a lot closer, A lot closer,

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Speaker 1: certainly less of an exaggeration, but the but but but

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Speaker 1: you were pretty much on the money on Murray Curry, right,

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Speaker 1: So yeah, but but that was an area where I

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Speaker 1: I have read about her and I think I did

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Speaker 1: a podcast that talked about her a while back, so

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Speaker 1: I had some sort of I had some level of

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Speaker 1: expert information there, but I had nothing really to go

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Speaker 1: on for the Highland or two one. Okay. So this

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Speaker 1: effect that we've just been demonstrating is what we're going

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Speaker 1: to be talking about today. And this is a psychological effect.

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Speaker 1: It's been written about a lot in the field of

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Speaker 1: behavioral economics, but it's fundamentally a psychological phenomenon known as

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Speaker 1: the anchoring bias, and I would argue it's one of

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Speaker 1: the most powerful, most well known, and most easily exploited

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Speaker 1: vulnerabilities in our minds, and for that reason, I think

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Speaker 1: it's something that really everybody should know about, because it's

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Speaker 1: something that people will constantly be using to try to

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Speaker 1: get the per hand on you for the rest of

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Speaker 1: your life. Indeed, this is definitely a topic that will

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Speaker 1: change the way you think about everything from salary negotiations

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Speaker 1: to just haggling at the market totally. Yeah. Uh, and

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Speaker 1: not just economic matters too, I want to uh, though

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Speaker 1: it's mostly been tested in terms of estimating numbers, and

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Speaker 1: especially economic type numbers, prices, things where you're trying to

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Speaker 1: determine a reasonable figure for something. I would posit that

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Speaker 1: I think it's very likely this type of thinking also

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Speaker 1: biases all kinds of judgments we make, such as judgments

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Speaker 1: of people's reputations, judgments of the confidence we place in

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Speaker 1: the outcomes of events, all of which is going to

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Speaker 1: be enormously important for the rest of your life in

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Speaker 1: myriad ways. Yeah. Though certainly a lot of the more

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Speaker 1: like readily available examples are gonna involve economics. They're gonna

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Speaker 1: involve things like massive discounts. How can you do you

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Speaker 1: remember deep discount vds? Deep discount DVDs or I guess

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Speaker 1: it was deep discount DVD. I think it was a

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Speaker 1: website that had it's time in the sun there with

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Speaker 1: with deeply discounted DVDs. And it seems like everybody I

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Speaker 1: knew we were just like, oh my goodness, these deals

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Speaker 1: are too good. You're practically you're losing money if you

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Speaker 1: don't order these movies. Right. The more you buy, the

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Speaker 1: more you save. And it's easy to fall into that mentality.

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Speaker 1: It's like I didn't really want to pick up this

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Speaker 1: video game or this movie or this book, but when

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Speaker 1: you slice the price that much, I guess I'll bite. Yeah, man,

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Speaker 1: seems irrational, right. But back to the questions I asked earlier,

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Speaker 1: what what age did Marie Curry die? How much was

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Speaker 1: Sean Connery paid for Highlander two? I actually did a brief,

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Speaker 1: non scientific email survey. I say non scientific because these

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Speaker 1: were very small samples, not truly random. I just basically

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Speaker 1: randomly emailed coworkers UH in two different groups and asked

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Speaker 1: them to estimate answers to those questions. Now, I had

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Speaker 1: Group A where I just asked them how old did

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Speaker 1: you think Marie Curry was when she died? And how

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Speaker 1: much do you think Sean Connery got paid for highland

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Speaker 1: Er two? No anchors, right, no starting numbers higher or

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Speaker 1: lower than And in that group, the average answer that

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Speaker 1: people gave was that they thought that Marie Cury died

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Speaker 1: at fifty three, and they thought that Sean Connery got

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Speaker 1: paid three point two million. Three point that was there.

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Speaker 1: That was their answer, without any anchoring, right, without any anchoring.

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Speaker 1: So that's very close, very close to the three point

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Speaker 1: five if that website is correct. Who knows? Then Group B,

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Speaker 1: I did the same anchors I just gave you. So

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Speaker 1: I asked them, did she die older? Younger than twenty seven?

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Speaker 1: What age did she die? The average answer for that

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Speaker 1: group was forty eight point three good bit lower than yeah.

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Speaker 1: And then also I did the same thing, I said, uh,

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Speaker 1: higher or lower than thirty one million for Sean Connery.

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Speaker 1: Average guests in Group B was that Sean Connery got

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Speaker 1: paid nineteen point three million dollars uncle that I wasn't

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Speaker 1: alone to be for Islander to nineteen point three million.

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Speaker 1: And these are these are co workers, These are smart people,

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Speaker 1: you know, they should be good at making estimates of

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Speaker 1: these kind of things off the top of their head.

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Speaker 1: But in this non scientific way, I feel like we've

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Speaker 1: just demonstrated that just putting a number out there, even

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Speaker 1: if the number is totally unreasonable, and Mary cuy didn't

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Speaker 1: die at twenty seven, what Sean Connery did not get

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Speaker 1: thirty one million for this movie in that doesn't make

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Speaker 1: any sense. But even if you put these unreasonable numbers

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Speaker 1: out there, they seem to bias people's answers toward the

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Speaker 1: numbers you've thrown out. Well, it really takes me back

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Speaker 1: to like pop quizzes in grade school tests, right, and

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Speaker 1: the saying the famous adage the answer is in the question,

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Speaker 1: Because what do you do if you don't really know

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Speaker 1: the answer? Will you have? It's multiple choice? You look

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Speaker 1: at the available answers and answers and see which one

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Speaker 1: each is out to you the most, which one feels

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Speaker 1: true or stirs your memory? And then failing that, you

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Speaker 1: look to the question itself. Is there some sort of

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Speaker 1: information in the question? Uh? Essentially you're looking for like

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Speaker 1: a leak in the question. You're looking for a flaw

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Speaker 1: in the in the Riddler's strategy. Yeah, there's test taking skills,

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Speaker 1: which are essentially meta test taking skills. They are skills

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Speaker 1: that are not really about the subject of the test,

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Speaker 1: but skills at determining how to interrogate the style and

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Speaker 1: format of a test to exploit it for better scores

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Speaker 1: in the end. Right. But then I also think there's

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Speaker 1: there's also kind of a social connotation to this as well.

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Speaker 1: Like an example would be, you have a friend who

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Speaker 1: comes up and says, hey, man, have you heard this

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Speaker 1: latest album by I don't know name an active band

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Speaker 1: Kansas Kansas. Have you heard this new album by Kansas?

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Speaker 1: How awesome is that album? Man? So now I have

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Speaker 1: to I have to frame my answer around awesome? Is

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Speaker 1: it pretty awesome? Super awesome? It was okay, reasonably awesome.

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Speaker 1: When you say it was okay, what that means is

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Speaker 1: you hated it, But you have to adjust up to

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Speaker 1: the fact that they started with how awesome is it? Right?

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Speaker 1: And this is a case though, where it's it's not

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Speaker 1: a situation where you're gonna appear stupid or or uninformed

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Speaker 1: on a topic unless you know, except on the topic

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Speaker 1: of Kansas. Maybe it's not a situation where you have

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Speaker 1: any monetary steaks, but there is kind of like a

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Speaker 1: social steak employ there. If your friend is a huge

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Speaker 1: Kansas fan. You don't want to say, oh, I think

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Speaker 1: Kansas is awful. You want to adjust your answer so

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Speaker 1: that it's the appropriate balance of truth and uh in politeness. Yeah, So,

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Speaker 1: in the same way that my email survey was not

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Speaker 1: really scientific, what we're talking about. These examples are not

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Speaker 1: really scientific either. They're just anecdotes, and they've got all

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Speaker 1: these contaminating factors like you're saying Kansas, well, like social

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Speaker 1: social dynamics, like you were just explaining, so you respond,

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Speaker 1: it might not be truly influenced by just the presence

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Speaker 1: of the word awesome as much as it is by

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Speaker 1: the fact that you're trying to maintain a relationship with

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Speaker 1: the person who said this, you know what I mean.

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Speaker 1: So it's not divorced of this contaminating context. Now, the

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Speaker 1: anchoring effect that we're going to be talking about today

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Speaker 1: has been thoroughly demonstrated in fully scientific context, So it's

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Speaker 1: not always just this social kind of stuff going on. Uh.

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Speaker 1: You can test it ten ways to Sunday, and it

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Speaker 1: has been tested not just ten a million ways to Sunday,

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Speaker 1: and this thing works. This anchoring effect is a known

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Speaker 1: robust exploit of the human mind that works almost all

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Speaker 1: the time it is. It is scary how often it works. Yeah,

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Speaker 1: there are no shortage of papers about this. Uh, that's

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Speaker 1: for sure. Now, I guess we should try to define

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Speaker 1: it just a little bit more so to define the

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Speaker 1: anchoring effect. It is an example of what's known as

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Speaker 1: a cognitive heuristic. And if you're like me, I can remember.

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Speaker 1: I think back when I was in college, I went

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Speaker 1: a long time hearing the word heuristic and just sort

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Speaker 1: of nodding without really knowing what it meant. Anytime you

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Speaker 1: hear the word heuristic, you can just substitute the phrase

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Speaker 1: rule of thumb or mental shortcut. I still picture a

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Speaker 1: hair shirt, no matter what I mean. Cans like that

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Speaker 1: because you just kind of you ever have words like

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Speaker 1: that where it's completely illogical, but you can't help but

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Speaker 1: picture this thing in your head. I have no idea why,

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Speaker 1: like I tend to imagine a philosopher in a hair shirt.

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Speaker 1: Melissandra put on her rough spun heuristic pretty much. But

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Speaker 1: in reality, a heuristic is a rule of thumb or

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Speaker 1: a mental shortcut. It's essentially a fast and easy process

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Speaker 1: that your brain uses to come up with some kind

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Speaker 1: of output. You need a piece of information or a

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Speaker 1: judgment about something, and you don't really have time to

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Speaker 1: sit down and work out all the details, so instead

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Speaker 1: you use a heuristic. And heuristics can lead to relatively

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Speaker 1: good output. Sometimes you're good it at a fast and

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Speaker 1: loose judgment on the fly, or they can lead to

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Speaker 1: relatively bad output. And there are all kinds of heuristics

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Speaker 1: we use. One example of an extremely common and extremely

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Speaker 1: bad heuristic is judging some what you think of somebody

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Speaker 1: by how they look. Extremely common heuristic. It's a shortcut.

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Speaker 1: You don't want to do the work of like talking

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Speaker 1: to them for hours and figuring out, you know, what

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Speaker 1: you really think about them and their reliability as a

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Speaker 1: person and their values and all that. So instead you

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Speaker 1: can just look at them and make a crude judgment.

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Speaker 1: This is a great example because it's also a process

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Speaker 1: that is not necessarily taking place at the surface level

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Speaker 1: of of cognition. It's implicit as opposed to explicit. Yeah,

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Speaker 1: it very often is. And so this is like one

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Speaker 1: of these really bad heuristics that we're just plagued by. Uh,

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Speaker 1: you know, it's everybody should recognize it's a destructive way

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Speaker 1: of thinking that it's not really good for society. That

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Speaker 1: people do it, but people just keep doing it because

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Speaker 1: they're naturally vulnerable to it. It's like taking a short

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Speaker 1: cut through the woods. It makes sense unless there's a

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Speaker 1: monster there, or it's rain or you get lost. Um.

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Speaker 1: I mean really, that can be said about a lot

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Speaker 1: of shortcuts. So when we call them shortcuts, they can

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Speaker 1: help you help you out in the short term. But

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Speaker 1: if everybody does it, it breaks the system, or if

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Speaker 1: you do it too often, you're more likely to run

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Speaker 1: up against the pitfalls of taking that shortcut. Another bad heuristic,

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Speaker 1: of course, is the anchoring heuristic, the one we're talking

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Speaker 1: about today. Uh. It might not be bad in every

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Speaker 1: single case, because maybe in some off chance it will

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Speaker 1: bias you toward a correct answer. But most of the time,

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Speaker 1: the way the anchoring heuristic is going to be deployed

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Speaker 1: in your life is by people who are trying to

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Speaker 1: get you negotiated toward their position on something, and they

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Speaker 1: will use the anchoring bias in order to exploit your

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Speaker 1: mind and make you come closer to a position that

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Speaker 1: benefits them. Right. So again, this is haggling for something

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Speaker 1: at a marketplace. This is negotiations over a contract. After

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Speaker 1: what have you exactly right, So I think we should

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Speaker 1: take a quick break, and then when we come back

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Speaker 1: we will discuss the origins of the idea of anchoring

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Speaker 1: and some research in psychology and behavioral economics on how

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Speaker 1: it applies. Thank alright, we're back. I should mention that

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Speaker 1: one of our main resources in discussing the anchoring effect

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Speaker 1: is a two thousand eleven literature review from the Journal

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Speaker 1: of Socioeconomics by Adrian Fernham and Hua Cheu Boo, which

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Speaker 1: collects and synthesizes all of the major research on the

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Speaker 1: subject over the past forty years or so up until

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Speaker 1: about two thousand eleven. This paper is a great resource.

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Speaker 1: It puts it all in one place, and so that's

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Speaker 1: going to be sort of our guide for discussing it

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Speaker 1: as we go. One question is where does the idea

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Speaker 1: of anchoring come from. Obviously people have been using it

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Speaker 1: before it was understood and codified as a principle in

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Speaker 1: behavioral economics, right, But the anchoring and adjustment effect was

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Speaker 1: most influentially described and articulated by Tversky and Conomon in

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Speaker 1: nineteen seventy four, and according to them, it is quote

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Speaker 1: the disproportionate influence on decision makers to make judgments that

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Speaker 1: are biased toward and initially presented value. So what that means,

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Speaker 1: in effect, is that when we're trying to make a

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Speaker 1: reasonable guess or a judgment about something, any piece of

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Speaker 1: information you get before you make the judgment is likely

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Speaker 1: to bias your thinking in the direction of that piece

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Speaker 1: of information. So, if you're shown a car and asked

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Speaker 1: how much you would pay for it, you you might say, what,

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Speaker 1: I don't know, ten thousand dollars. That seems about right.

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Speaker 1: But let's say instead you are shown the same car

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Speaker 1: with the price sticker on it that says sixteen thousand dollars.

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Speaker 1: According to the anchoring and adjustment hypothesis here, you would

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Speaker 1: be more likely in this scenario to offer more for

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Speaker 1: the car, more than you would have if you just

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Speaker 1: saw the car and tried to think, how much would

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Speaker 1: that be worth to me? Because now, oh, now that

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Speaker 1: it has a sixteen thousand dollar price tag, I think

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Speaker 1: maybe it looks worth about twelve thousand. You're still coming

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Speaker 1: down from the offer, but the offer has biased up

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Speaker 1: your initial judgment of how much it's worth, or, in

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Speaker 1: other words, the anchor of the initial price has adjusted

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Speaker 1: your offer higher than you naturally be willing to pay

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Speaker 1: if that price hadn't been presented to you. It's kind

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Speaker 1: of like if you have a ticket for a concert

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Speaker 1: and then you realize you can't go, and so you

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Speaker 1: try to sell that ticket, just you know, online to

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Speaker 1: some friends. Maybe you'll often include how much you paid

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Speaker 1: for it and and what you're really saying there is

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Speaker 1: I paid thirty bucks for this ticket, so I'll take

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Speaker 1: whatever I can get. But either closer you get to

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Speaker 1: thirty the better. You're not a Yeah, you're not asking

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Speaker 1: how much is it worth for you to see Kansas.

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Speaker 1: You're saying, given that I paid five hundred dollars for

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Speaker 1: front row seats to Kansas, how close can you get

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Speaker 1: to that that number. I have no idea how much

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Speaker 1: Kansas tickets actually cost. I assume their mega and demand.

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Speaker 1: But by simply mentioning five hundred dollars, you made me

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Speaker 1: think about anything. Well, you know they're Kansas. I I

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Speaker 1: know of Kansas, so they're a big enough name. Uh,

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Speaker 1: it makes sense that someone would pay a lot of

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Speaker 1: money for a first row experience. You know, we're dust

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Speaker 1: in the wind, we only live once. You might as

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Speaker 1: well go see Kansas, even if it costs a pretty penny. Yeah,

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Speaker 1: it's crazy, Like you said, just how just through observation

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Speaker 1: you can tell how powerful this this, the anchoring phenomenon.

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Speaker 1: It actually is right, But we don't have to go

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Speaker 1: anecdotal because this has been proved up down, left, right,

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Speaker 1: sideways to Wichita and back. Uh. It is a thoroughly,

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Speaker 1: thoroughly demonstrated principle. Our minds just work this way, and

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Speaker 1: so there are some qualifications. The anchoring bias can be

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Speaker 1: affected by some variables, we think, and there is actually

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Speaker 1: debate over what explains the reason behind it, why it

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Speaker 1: happens in different scenarios. But what there's really no debating

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Speaker 1: is that it happens. This is This has proven a

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Speaker 1: million ways, and it is. It is considered a thoroughly

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Speaker 1: robust bias and a fundamental part of how the human

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Speaker 1: brain works. Yes, as you said, it said there are

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Speaker 1: no shortage of papers to back this up. I would

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Speaker 1: say that one of the problems is that these are

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Speaker 1: some of the stuffiest academic papers you could hope to read.

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Speaker 1: I mean, they're they're breaking apart a phenomenous best studied

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Speaker 1: through numbers and figures and estimates on value. So it's

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Speaker 1: not as sexy as you have somebody in a room

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Speaker 1: pulling a lever to shock somebody in the next room.

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Speaker 1: You know. I feel like maybe maybe what anchoring needs

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Speaker 1: is like one really good but kind of superficial study

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Speaker 1: that's just based on saying, how much do you think

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Speaker 1: Tom Cruise was paid for this film? Something that will

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Speaker 1: get that will generate headlines that will be uh relatable

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Speaker 1: in a slightly different way, and that could help explain

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Speaker 1: anchoring more to the general public. Yeah, it's like a

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Speaker 1: popular sensational demons stration, but it's been demonsted. I mean,

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Speaker 1: part of the problem is you don't need to demonstrate

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Speaker 1: it anymore. It's been demonstrated with these like hundreds of questions.

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Speaker 1: It's been demonstrated on right, what is the freezing point

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Speaker 1: of vodka? That's one that they ask people. Uh makes

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Speaker 1: a difference there. What is the height of Mount Everest? Uh?

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Speaker 1: What age was Amelia Earhart when she disappeared attempting to

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Speaker 1: pilot a plane around the world. So they're just all

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Speaker 1: these studies that ask questions like this and use anchoring

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Speaker 1: to bias the answers of participants. But it also works

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Speaker 1: in things other than just like giving a basic informational

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Speaker 1: guess about something. That's what we've been doing so far,

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Speaker 1: Like you know, can you guess a fact about history?

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Speaker 1: It also works in contexts like what percent chance would

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Speaker 1: you give of a thing happening? What's the percent chance

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Speaker 1: you would give of a certain athlete scoring a certain

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Speaker 1: number of points in an upcoming game. So it influences

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Speaker 1: our judgments of probabilities. Yes, we certainly see this in

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Speaker 1: political elections, for instance, Absolutely numbers thrown out what are

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Speaker 1: the chances of this particular candidate winning, and then you

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Speaker 1: end up adjusting your expectations of the future based on

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Speaker 1: those percentages. Yeah, and so those percentages could be based

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Speaker 1: on something in reality. I mean, like if you're looking

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Speaker 1: at good, well conducted poll data that's reflecting information about

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Speaker 1: reality that you might want to adjust according to that, right,

401
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Speaker 1: if it's good information. But somebody could also bias you

402
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Speaker 1: with bad information, uh, just by using the anchoring effect.

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Speaker 1: If they just put a ridiculous number that's not true

404
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Speaker 1: in front of your face, chances are that this will

405
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Speaker 1: actually influence the extent to which will influence your self

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Speaker 1: synthesized probability judgment. Yeah, Like there's I say, there's a

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Speaker 1: poll that comes out and says, of wizards think Voldemort,

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Speaker 1: it will be a great ruler of the Earth, you know,

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Speaker 1: and then you're like, well, who, I don't know, is

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Speaker 1: kind of high. It's probably more like sixty, right when

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Speaker 1: when really most wizards, maybe of wizards think Baltimore is great.

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Speaker 1: I don't know. I leave that to the the Potter fans. Yeah,

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Speaker 1: I don't know what the percent is, but yeah, you

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Speaker 1: could be anchored and biased that way. So it affects

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Speaker 1: these probability estimates. I know one thing they tested it

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Speaker 1: on was like likelihood estimates of nuclear war. You can

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Speaker 1: bias people's answers with anchors there. It has been shown

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Speaker 1: to influence legal judgments like sentencing and uh and liability

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Speaker 1: for punitive damages. It's been shown to influence this is

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Speaker 1: a huge one, valuations and prices, right, how much you'd

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Speaker 1: be willing to pay for something. That's a really common example. Uh,

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Speaker 1: it would be it's been used in in forecasting examples

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Speaker 1: like how much you would expect to spend on a restaurant.

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Speaker 1: And here's a really weird thing. The types of anchors

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Speaker 1: that influence people don't have to seem credible. People can

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Speaker 1: be influenced. These studies have shown by things that obviously

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00:24:07,400 --> 00:24:10,919
Speaker 1: shouldn't be influences. They don't have to like frame this, uh,

428
00:24:11,000 --> 00:24:14,040
Speaker 1: this anchoring number that they prime you with as coming

429
00:24:14,119 --> 00:24:17,480
Speaker 1: from some reasonable authority or something like that. They can

430
00:24:17,520 --> 00:24:20,760
Speaker 1: just prime you with a random number that doesn't matter

431
00:24:20,800 --> 00:24:23,639
Speaker 1: at all. Some studies have people spinning a wheel to

432
00:24:23,720 --> 00:24:27,680
Speaker 1: get a random number, and the random number still biases

433
00:24:27,720 --> 00:24:32,360
Speaker 1: your answer toward it. So just a random approval rating

434
00:24:32,400 --> 00:24:34,800
Speaker 1: for Boltimore, I could say, even though it's super high

435
00:24:35,840 --> 00:24:39,280
Speaker 1: approval rating among wizards of Baltimore. Apparently you could spin

436
00:24:39,320 --> 00:24:42,480
Speaker 1: a wheel in front of people so that it's entirely

437
00:24:42,520 --> 00:24:45,080
Speaker 1: clear to them that the number is random and you're

438
00:24:45,200 --> 00:24:48,480
Speaker 1: it's not coming from real data, and still showing that

439
00:24:48,560 --> 00:24:51,520
Speaker 1: higher number from the random spin of the wheel would

440
00:24:51,560 --> 00:24:55,200
Speaker 1: bias people's estimates towards the number. But we're getting ahead

441
00:24:55,200 --> 00:24:57,080
Speaker 1: of ourselves, because I think we should take a moment

442
00:24:57,400 --> 00:25:00,240
Speaker 1: to talk about the different theories about what explained means

443
00:25:00,320 --> 00:25:03,040
Speaker 1: the anchoring effect. Obviously, this thing's there. If you put

444
00:25:03,040 --> 00:25:05,320
Speaker 1: a number in front of somebody's face, it's going to

445
00:25:05,440 --> 00:25:09,240
Speaker 1: bias their estimate or their answer towards that number. But

446
00:25:09,520 --> 00:25:12,480
Speaker 1: why does this happen now we mentioned the idea was

447
00:25:13,280 --> 00:25:16,920
Speaker 1: very popularly explained by Knomon and Diverseki in nineteen seventy four,

448
00:25:17,240 --> 00:25:22,000
Speaker 1: and their original proposal of adjustment was was going up

449
00:25:22,080 --> 00:25:24,679
Speaker 1: or down from a given anchor. And so their idea

450
00:25:24,800 --> 00:25:28,520
Speaker 1: was you start with the anchor when you're trying to

451
00:25:28,560 --> 00:25:30,919
Speaker 1: reason out the answer to something. So I say, you know,

452
00:25:31,000 --> 00:25:36,080
Speaker 1: what was Sean Connery's salary in uh in Highland or two?

453
00:25:36,200 --> 00:25:38,960
Speaker 1: Was it thirty one million or or above or below?

454
00:25:39,640 --> 00:25:41,919
Speaker 1: The way people reason about that is they'd start with

455
00:25:41,960 --> 00:25:45,080
Speaker 1: thirty one million, and they'd say is that reasonable? And

456
00:25:45,119 --> 00:25:47,200
Speaker 1: then most people would say, no, it can't be that much.

457
00:25:47,520 --> 00:25:51,080
Speaker 1: So then they'd work their way down from thirty one

458
00:25:51,119 --> 00:25:55,760
Speaker 1: million to a place that starts to feel reasonable. And

459
00:25:55,800 --> 00:25:58,960
Speaker 1: so in that sense, you're sort of biasing yourself up

460
00:25:59,000 --> 00:26:02,359
Speaker 1: towards like the the utter top range of whatever you

461
00:26:02,440 --> 00:26:07,280
Speaker 1: might consider a reasonable range of answers. Does that make sense? Yes, yeah, definitely.

462
00:26:07,400 --> 00:26:09,960
Speaker 1: But this explanation does have problems. People have attacked it

463
00:26:10,000 --> 00:26:12,880
Speaker 1: in the literature because anchoring, for one thing, is often

464
00:26:12,920 --> 00:26:16,679
Speaker 1: shown to be unconscious. So if you're not doing this consciously,

465
00:26:16,720 --> 00:26:19,240
Speaker 1: it's kind of hard to explain how that whole process

466
00:26:19,280 --> 00:26:21,359
Speaker 1: could work itself out. Now that's not to say that

467
00:26:21,680 --> 00:26:24,280
Speaker 1: it's it's not ever conscious, because clearly, if someone's going

468
00:26:24,320 --> 00:26:27,439
Speaker 1: into negotiations of a price, you might go into it

469
00:26:27,520 --> 00:26:30,600
Speaker 1: saying I paid thirty dollars for this ticket. If I

470
00:26:30,640 --> 00:26:33,160
Speaker 1: could get forty, that would be great. So I'm gonna

471
00:26:33,200 --> 00:26:36,120
Speaker 1: start at forty knowing that they'll work me down closer

472
00:26:36,160 --> 00:26:39,160
Speaker 1: to what I actually expect to get. Yeah, you're totally right.

473
00:26:39,240 --> 00:26:42,760
Speaker 1: Sometimes it clearly is conscious, And in those conscious scenarios,

474
00:26:42,800 --> 00:26:45,600
Speaker 1: I think Conomon in Tversky's explanation might be right on

475
00:26:45,640 --> 00:26:50,040
Speaker 1: the money. But uh, it also in some cases is

476
00:26:50,040 --> 00:26:53,760
Speaker 1: clearly unconscious. And also it affects judgment whether or not

477
00:26:53,880 --> 00:26:56,720
Speaker 1: the anchor is anywhere close to the realm of a

478
00:26:56,760 --> 00:27:02,800
Speaker 1: reasonable range. So if I said, um, uh Sean Connery's

479
00:27:03,400 --> 00:27:06,720
Speaker 1: was was Sean Connery's salary in Highlander to the quickening,

480
00:27:07,520 --> 00:27:11,159
Speaker 1: uh eight million dollars? Or if I said, was it

481
00:27:11,280 --> 00:27:16,000
Speaker 1: ten billion dollars? Either way, that kind of thing has

482
00:27:16,040 --> 00:27:19,760
Speaker 1: been shown to influence to bias your answer toward it.

483
00:27:19,840 --> 00:27:23,280
Speaker 1: So whether it's within a somewhat reasonable range or not. Yeah,

484
00:27:23,359 --> 00:27:25,159
Speaker 1: and if you throw it throughout one of those Uh,

485
00:27:25,240 --> 00:27:28,360
Speaker 1: those figures, I'm thinking it's either exceedingly high or it's

486
00:27:28,640 --> 00:27:31,800
Speaker 1: it's pretty small for Sean Connery. Like if you, if

487
00:27:31,840 --> 00:27:35,200
Speaker 1: you'd if you'd asked me, did Sean Connery receive less

488
00:27:35,280 --> 00:27:37,920
Speaker 1: or more than a hundred dollars for his role in

489
00:27:38,600 --> 00:27:41,680
Speaker 1: in Highland or two? That would make me begin to think, well,

490
00:27:41,800 --> 00:27:44,280
Speaker 1: maybe he was paid of an exceedingly small amount of

491
00:27:44,320 --> 00:27:46,280
Speaker 1: money and it was there was some sort of special

492
00:27:46,320 --> 00:27:48,639
Speaker 1: studio deal about it, or he just did it for

493
00:27:48,640 --> 00:27:52,159
Speaker 1: the love of the franchise. Yeah, he just wanted to

494
00:27:52,160 --> 00:27:54,719
Speaker 1: support Highlander. He said, I'll to just take fifty thousand,

495
00:27:54,880 --> 00:27:58,080
Speaker 1: that's all I need. Uh Yeah, that would still bias

496
00:27:58,160 --> 00:28:01,360
Speaker 1: you way down from the true answer. Now, a different

497
00:28:01,480 --> 00:28:07,159
Speaker 1: hypothesis for explaining what causes the anchoring effect is something

498
00:28:07,200 --> 00:28:09,879
Speaker 1: that we're all very familiar with. It's often called the

499
00:28:09,920 --> 00:28:14,240
Speaker 1: selective accessibility hypothesis, but really this is just explaining the

500
00:28:14,320 --> 00:28:20,119
Speaker 1: anchoring effect through confirmatory hypothesis testing a k A. Confirmation bias.

501
00:28:20,200 --> 00:28:21,600
Speaker 1: Oh yes, this is a big one. This is like

502
00:28:21,640 --> 00:28:26,440
Speaker 1: the bugbear of scientific study or just critical thinking exactly.

503
00:28:26,520 --> 00:28:29,800
Speaker 1: So in this in this format, when you're trying to

504
00:28:29,840 --> 00:28:33,600
Speaker 1: find the answer to a question, you mainly seek reasons

505
00:28:33,640 --> 00:28:37,040
Speaker 1: to justify belief in the answer you already suspect. So

506
00:28:37,080 --> 00:28:39,600
Speaker 1: if a detective is trying to solve a murder and

507
00:28:39,640 --> 00:28:42,840
Speaker 1: he's got a gut feeling that Eugene did it, he's

508
00:28:42,880 --> 00:28:46,080
Speaker 1: going to unconsciously give greater weight to any piece of

509
00:28:46,120 --> 00:28:49,760
Speaker 1: evidence that makes Eugene look more guilty, and unconsciously ignore

510
00:28:49,960 --> 00:28:52,920
Speaker 1: or give less weight to evidence that points to somebody

511
00:28:52,960 --> 00:28:57,080
Speaker 1: else or exonerates Eugene. So instead of openly and inductively

512
00:28:57,200 --> 00:29:02,360
Speaker 1: just gathering evidence for all possibilities, he's subconsciously, without realizing it,

513
00:29:02,360 --> 00:29:05,960
Speaker 1: trying to build a case for the suspect he already

514
00:29:06,040 --> 00:29:10,120
Speaker 1: hypothesizes to be guilty. Uh. Confirmation bias. Another way of

515
00:29:10,160 --> 00:29:12,600
Speaker 1: explaining it is that a lot of times when we

516
00:29:12,680 --> 00:29:15,920
Speaker 1: think we're working like an investigator, we're really working like

517
00:29:15,960 --> 00:29:20,120
Speaker 1: a prosecutor. Right. Uh. An example that's come up recently

518
00:29:20,160 --> 00:29:25,080
Speaker 1: on the podcast is is that of scientific studies into

519
00:29:25,360 --> 00:29:29,560
Speaker 1: the effectiveness of prayer, right, because it's you can see

520
00:29:29,560 --> 00:29:31,400
Speaker 1: how it's easy for an individual to go into this

521
00:29:32,200 --> 00:29:36,720
Speaker 1: thinking that they are being completely objective, but if they

522
00:29:36,760 --> 00:29:39,760
Speaker 1: if part of their worldview, even if it's not, even

523
00:29:39,800 --> 00:29:43,120
Speaker 1: if they're not just like a hardcore believer. Now, if

524
00:29:43,160 --> 00:29:44,760
Speaker 1: it's a part of their past, if it's a part

525
00:29:44,800 --> 00:29:47,960
Speaker 1: of their history, Uh, then that could be a stumbling

526
00:29:47,960 --> 00:29:52,960
Speaker 1: block to like true objective exploration of prayer as having

527
00:29:53,000 --> 00:29:55,240
Speaker 1: some sort of an influence on the real world. Yeah,

528
00:29:55,240 --> 00:29:57,680
Speaker 1: but of course we we would. We should say that

529
00:29:57,760 --> 00:30:01,040
Speaker 1: this doesn't mean things like prayer studies are do because

530
00:30:01,080 --> 00:30:03,800
Speaker 1: you can certainly design I mean, this is what science

531
00:30:03,840 --> 00:30:06,360
Speaker 1: is for. This is why you design experiments. You try

532
00:30:06,360 --> 00:30:09,400
Speaker 1: to make them so that your your biases don't matter.

533
00:30:09,840 --> 00:30:13,800
Speaker 1: You structure an experiment to try to exclude the possibility

534
00:30:13,880 --> 00:30:16,840
Speaker 1: of your bias interfering with the results. But I think

535
00:30:16,880 --> 00:30:19,280
Speaker 1: that the other takeaway here is that there are there

536
00:30:19,280 --> 00:30:23,120
Speaker 1: are two types of bad prayer researchers. Essentially, there's the

537
00:30:23,440 --> 00:30:28,760
Speaker 1: researcher who is just objectively bad, that is saying, I

538
00:30:28,840 --> 00:30:32,160
Speaker 1: believe prayer is real and I'm going to I'm gonna

539
00:30:32,200 --> 00:30:35,560
Speaker 1: bend and break every rule to quote unquote prove it

540
00:30:35,600 --> 00:30:38,320
Speaker 1: in the study. And I think though that sort of

541
00:30:38,320 --> 00:30:41,800
Speaker 1: researcher tends to not exist. But then there's the second level,

542
00:30:41,800 --> 00:30:44,720
Speaker 1: and that's the individual who if you ask them about it,

543
00:30:44,760 --> 00:30:47,240
Speaker 1: if you were able to peer into their mind. They

544
00:30:47,240 --> 00:30:50,040
Speaker 1: believe they are doing the objective thing. They honestly think

545
00:30:50,040 --> 00:30:53,680
Speaker 1: they're doing a good job, probably, but they are still

546
00:30:53,800 --> 00:30:58,320
Speaker 1: leaning into their bias. Yeah, they're prosecuting the truth rather

547
00:30:58,400 --> 00:31:03,400
Speaker 1: than than investigating all open possibilities. Uh. Yeah. But then again,

548
00:31:03,400 --> 00:31:05,680
Speaker 1: like I said, I don't want to automatically tar anybody

549
00:31:05,680 --> 00:31:08,160
Speaker 1: who does a prayer study with that, but that clearly

550
00:31:08,880 --> 00:31:12,480
Speaker 1: is probably happening in some cases. Yeah. But the prosecution

551
00:31:12,520 --> 00:31:15,040
Speaker 1: example is great too because it brings up the idea

552
00:31:15,080 --> 00:31:20,360
Speaker 1: of leading questions, and the anchoring seems to indicate that

553
00:31:20,920 --> 00:31:24,680
Speaker 1: any question with a with a figure in it, with

554
00:31:24,680 --> 00:31:26,920
Speaker 1: with some sort of a number in it is kind

555
00:31:26,920 --> 00:31:29,800
Speaker 1: of a leading question if I'm giving you a starting

556
00:31:29,840 --> 00:31:33,320
Speaker 1: point for you to determine the value. Yeah, exactly. I

557
00:31:33,320 --> 00:31:36,240
Speaker 1: mean that that is, you could say that leading questions

558
00:31:36,680 --> 00:31:39,800
Speaker 1: are something similar to the anchoring effect. You're trying to

559
00:31:39,840 --> 00:31:42,080
Speaker 1: give people a place to work from in the content

560
00:31:42,120 --> 00:31:45,640
Speaker 1: of the question. Now there's a third explanation for how

561
00:31:45,720 --> 00:31:49,880
Speaker 1: the anchoring effect works. Apart from the anchoring an adjustment

562
00:31:49,920 --> 00:31:53,600
Speaker 1: theory of Konomon and Tversky, and apart from the confirmation

563
00:31:53,640 --> 00:31:56,920
Speaker 1: bias or selective accessibility model and The third one is

564
00:31:56,960 --> 00:32:01,240
Speaker 1: often known as the attitude change model, and this uh

565
00:32:01,800 --> 00:32:04,720
Speaker 1: to think about the simple version of this. Essentially, in

566
00:32:04,760 --> 00:32:09,160
Speaker 1: the attitude change model, the anchor is treated as something

567
00:32:09,240 --> 00:32:13,000
Speaker 1: that changes your attitude towards the nature of the question.

568
00:32:13,120 --> 00:32:15,520
Speaker 1: In other words, the anchor is treated as a kind

569
00:32:15,560 --> 00:32:19,680
Speaker 1: of hint. Now, a lot of people might have reacted

570
00:32:19,720 --> 00:32:21,480
Speaker 1: to the stuff I said at the beginning of the

571
00:32:21,480 --> 00:32:25,760
Speaker 1: episode that way, like, oh, if you said, um, you know,

572
00:32:25,880 --> 00:32:29,800
Speaker 1: did Marie Cury live till after? She probably lived more

573
00:32:29,840 --> 00:32:32,479
Speaker 1: than that. But I bet that is like a cue

574
00:32:32,600 --> 00:32:36,120
Speaker 1: or a hint that she died young. Does that make sense? No,

575
00:32:36,280 --> 00:32:38,000
Speaker 1: I think that makes perfect sense. I think that is.

576
00:32:38,160 --> 00:32:40,360
Speaker 1: That is the way I tend to think about trivia

577
00:32:40,480 --> 00:32:44,080
Speaker 1: questions if one's pulling out some trivia cards just you know,

578
00:32:44,080 --> 00:32:47,160
Speaker 1: with friends or family. Like one example, there's a wonderful

579
00:32:47,200 --> 00:32:49,080
Speaker 1: little card game called are You Smarter Than a Box

580
00:32:49,080 --> 00:32:53,520
Speaker 1: of Rocks? And it's each trivia question the answer is

581
00:32:53,560 --> 00:32:56,720
Speaker 1: going to be zero, one or two, and you shake

582
00:32:56,760 --> 00:32:59,480
Speaker 1: a box of rocks, and the answers will will be

583
00:32:59,520 --> 00:33:02,120
Speaker 1: based on the random way that the rocks of fall

584
00:33:02,160 --> 00:33:05,200
Speaker 1: together a zero one or two, so that you're playing

585
00:33:05,200 --> 00:33:07,640
Speaker 1: against a box of rocks. But you go into every

586
00:33:07,720 --> 00:33:11,040
Speaker 1: question knowing that the answer is going to be low.

587
00:33:11,120 --> 00:33:13,880
Speaker 1: It cannot be greater than two, right, So in that case,

588
00:33:14,160 --> 00:33:17,440
Speaker 1: you are being primed with an anchor each time you

589
00:33:17,520 --> 00:33:20,880
Speaker 1: play with something that is informationally relevant, like it actually

590
00:33:21,000 --> 00:33:24,360
Speaker 1: is that that is useful information that's going to buy

591
00:33:24,400 --> 00:33:28,040
Speaker 1: us your answer toward correct answers. But in the case

592
00:33:28,120 --> 00:33:30,960
Speaker 1: of anchoring, there is plenty of evidence that you can

593
00:33:31,120 --> 00:33:36,440
Speaker 1: bias people's answers towards incorrect answers. Obviously incorrect answers answers

594
00:33:36,480 --> 00:33:39,840
Speaker 1: they would never give unless they've been given this anchor

595
00:33:39,880 --> 00:33:42,520
Speaker 1: before making the judgment. You know. Another area I think

596
00:33:42,520 --> 00:33:45,160
Speaker 1: we were one running runs into this a lot, uh

597
00:33:45,440 --> 00:33:48,560
Speaker 1: is the area of star ratings for things. You know,

598
00:33:48,800 --> 00:33:56,200
Speaker 1: if you see a five star rating for a particular service, podcast, movie, book, game,

599
00:33:56,240 --> 00:33:59,000
Speaker 1: you name it, uh, that is going to serve as

600
00:33:59,000 --> 00:34:02,520
Speaker 1: an as an anchoring point for your evaluation of the

601
00:34:02,560 --> 00:34:05,920
Speaker 1: product or one star. Yeah, well I think that they're

602
00:34:06,000 --> 00:34:10,000
Speaker 1: they're clearly is for example, a critical hurting effect about

603
00:34:10,080 --> 00:34:12,880
Speaker 1: if you look at the way critics opinions pour in

604
00:34:12,960 --> 00:34:16,840
Speaker 1: for movies and video games and things like that, especially

605
00:34:16,880 --> 00:34:19,600
Speaker 1: any system maybe less so for things like books where

606
00:34:19,600 --> 00:34:23,520
Speaker 1: there's not as much of an organized numerical rating system

607
00:34:23,600 --> 00:34:27,280
Speaker 1: that people use. But yeah, for like movies, the Rotten

608
00:34:27,320 --> 00:34:31,400
Speaker 1: Tomatoes score or whatever. I do really get the feeling

609
00:34:31,560 --> 00:34:35,200
Speaker 1: that once you've seen that lots of other critics like something,

610
00:34:35,280 --> 00:34:37,920
Speaker 1: you're more likely to give it a fair shake. Like

611
00:34:38,000 --> 00:34:40,839
Speaker 1: you might just pay more attention when you're watching it

612
00:34:41,320 --> 00:34:44,359
Speaker 1: and think, Okay, this is something interesting going on here.

613
00:34:44,400 --> 00:34:46,719
Speaker 1: You might have watched the same movie otherwise and just

614
00:34:46,840 --> 00:34:49,359
Speaker 1: kind of been checking your phone and I was like, oh,

615
00:34:49,360 --> 00:34:51,560
Speaker 1: it was okay. Yeah, And it kind of opens your

616
00:34:51,560 --> 00:34:55,839
Speaker 1: mind to the possibility for wonder um in something which

617
00:34:56,000 --> 00:34:58,400
Speaker 1: it in something that is his low stakes this film

618
00:34:58,480 --> 00:35:00,000
Speaker 1: for most of us, you know, unless you're a perfec

619
00:35:00,040 --> 00:35:04,520
Speaker 1: national um in the in the industry for the most part,

620
00:35:04,560 --> 00:35:06,680
Speaker 1: Like that's a good thing. Why I'm all for finding

621
00:35:06,719 --> 00:35:10,040
Speaker 1: the wonder in a terrible film. Uh, But when you

622
00:35:10,080 --> 00:35:12,400
Speaker 1: apply that to other areas, to find the wonder in

623
00:35:12,440 --> 00:35:16,280
Speaker 1: a terrible automobile, to find the wonder in a terrible

624
00:35:16,680 --> 00:35:20,719
Speaker 1: political candidate like that, the stakes are higher. I'm a

625
00:35:20,920 --> 00:35:25,680
Speaker 1: I'm a real devotee of cult b cars. We need

626
00:35:25,719 --> 00:35:30,400
Speaker 1: like a mystery science theater of household appliances. Yeah, that

627
00:35:30,480 --> 00:35:34,240
Speaker 1: the silhouettes are all missing fingers in that in that example. Okay,

628
00:35:34,280 --> 00:35:36,719
Speaker 1: I guess we should move on to and we're still

629
00:35:36,719 --> 00:35:40,080
Speaker 1: working mainly from that two thousand eleven paper I mentioned earlier.

630
00:35:40,400 --> 00:35:42,319
Speaker 1: Uh to mention a few of the factors that have

631
00:35:42,400 --> 00:35:46,799
Speaker 1: been found to affect or influence the anchoring effect, one

632
00:35:46,800 --> 00:35:49,759
Speaker 1: of which is mood. I thought this was kind of

633
00:35:49,800 --> 00:35:52,960
Speaker 1: interesting because it actually runs counter to some of the

634
00:35:52,960 --> 00:35:56,839
Speaker 1: ways that mood affects other types of judgment. Here's how

635
00:35:56,880 --> 00:36:01,120
Speaker 1: it goes. Being sad has been found to generally make

636
00:36:01,200 --> 00:36:05,880
Speaker 1: you more susceptible to anchoring. This is odd because the

637
00:36:06,000 --> 00:36:10,200
Speaker 1: general understanding is that people reason better when they're in

638
00:36:10,239 --> 00:36:13,479
Speaker 1: a sad mood than when they're in a happy mood. Yeah,

639
00:36:13,480 --> 00:36:16,359
Speaker 1: it's kind of the idea you want your shoppers happy, right,

640
00:36:16,800 --> 00:36:20,120
Speaker 1: Like a happy shopper is gonna enter and leave with

641
00:36:20,120 --> 00:36:21,759
Speaker 1: a smile on their face. But this makes it sound

642
00:36:21,760 --> 00:36:25,040
Speaker 1: like the opposite that you want sad shoppers. Yeah. Despite

643
00:36:25,040 --> 00:36:28,359
Speaker 1: the fact that information is generally processed more efficiently when

644
00:36:28,440 --> 00:36:31,360
Speaker 1: judges are in a sad mood. Uh, This it's the

645
00:36:31,400 --> 00:36:35,000
Speaker 1: opposite for the anchoring effect. To quote from the paper

646
00:36:35,040 --> 00:36:38,120
Speaker 1: I mentioned the two eleven paper quote. However, an exception

647
00:36:38,160 --> 00:36:42,960
Speaker 1: to this rule is judgmental anchoring. Bowdenhausen and Englick and

648
00:36:43,040 --> 00:36:46,239
Speaker 1: Soda found that participants in a sad mood were more

649
00:36:46,360 --> 00:36:50,279
Speaker 1: susceptible to the heuristic bias of anchoring in comparison to

650
00:36:50,320 --> 00:36:53,120
Speaker 1: their counterparts in a neutral or happy mood. From the

651
00:36:53,200 --> 00:36:57,440
Speaker 1: attitude change perspective, sad mood causes people to engage in

652
00:36:57,520 --> 00:37:02,839
Speaker 1: more effortful processing, where people interpret information through elaboration on

653
00:37:02,880 --> 00:37:06,520
Speaker 1: their existing knowledge and determine the claim to be acceptable

654
00:37:06,640 --> 00:37:10,520
Speaker 1: or unacceptable. So maybe the idea here is that people

655
00:37:10,520 --> 00:37:14,280
Speaker 1: in a sad mood are more likely to spend more

656
00:37:14,320 --> 00:37:18,799
Speaker 1: time reading into the question on anchoring, doing that attitude

657
00:37:18,880 --> 00:37:21,799
Speaker 1: change thing, looking for a hint in the question, and

658
00:37:21,880 --> 00:37:25,440
Speaker 1: this hint can bias them way off the mark. Okay,

659
00:37:25,440 --> 00:37:28,520
Speaker 1: well what about the knowledge of the participants? This comes

660
00:37:28,560 --> 00:37:31,279
Speaker 1: back to the to your initial question. Like I, I

661
00:37:31,360 --> 00:37:33,680
Speaker 1: had read this book right, had researched this topic before,

662
00:37:33,880 --> 00:37:35,480
Speaker 1: so I felt like I had a leg up on

663
00:37:35,520 --> 00:37:38,320
Speaker 1: the question. Yeah, if you've just been reading about Marie

664
00:37:38,360 --> 00:37:41,600
Speaker 1: Curry's life, you probably knew the right answer, and that

665
00:37:41,719 --> 00:37:44,200
Speaker 1: anchor wasn't going to throw you right. So there are

666
00:37:44,280 --> 00:37:47,239
Speaker 1: some cases where obviously knowledge can play a difference, but

667
00:37:47,440 --> 00:37:51,479
Speaker 1: in general, knowledge of a subject area has not been

668
00:37:51,560 --> 00:37:54,840
Speaker 1: shown to be a strong way of undercutting the anchoring effect.

669
00:37:55,320 --> 00:37:59,360
Speaker 1: Even if you're knowledgeable in a subject area, you're still

670
00:37:59,400 --> 00:38:03,520
Speaker 1: susceptible all to anchoring. Examples that have been tested here

671
00:38:03,560 --> 00:38:06,960
Speaker 1: are that, for example, car mechanics and car dealers were

672
00:38:07,000 --> 00:38:11,440
Speaker 1: influenced by anchors on car prices. Estate agents adjust to

673
00:38:11,480 --> 00:38:15,239
Speaker 1: their estate value estimates towards anchors. Even if you know

674
00:38:15,320 --> 00:38:19,960
Speaker 1: what you're talking about, anchors will probably still affect you. Huh. Well,

675
00:38:20,000 --> 00:38:22,080
Speaker 1: I mean, on one level, this makes sense because there's

676
00:38:22,239 --> 00:38:24,719
Speaker 1: we of course have the adage a little knowledge is

677
00:38:24,760 --> 00:38:27,840
Speaker 1: a dangerous thing. Uh And and certainly one can be

678
00:38:27,960 --> 00:38:32,600
Speaker 1: knowledgeable in a field or or what not without being

679
00:38:32,800 --> 00:38:35,080
Speaker 1: an expert in that area. There are gonna be holes

680
00:38:35,080 --> 00:38:38,520
Speaker 1: in your knowledge. There's gonna be room for doubt and

681
00:38:38,640 --> 00:38:42,120
Speaker 1: uh and and that and where there's doubt, there seems

682
00:38:42,120 --> 00:38:45,080
Speaker 1: like there's a susceptibility to anchoring. I'm sure that's not

683
00:38:45,160 --> 00:38:47,120
Speaker 1: always the case, but I think sometimes you've got some

684
00:38:47,160 --> 00:38:49,759
Speaker 1: sort of analog of the Dunning Kruger effect. Going on

685
00:38:49,960 --> 00:38:52,279
Speaker 1: where people who have more knowledge are going to be

686
00:38:52,320 --> 00:38:54,640
Speaker 1: a little more cautious people have less knowledge. You're just

687
00:38:54,719 --> 00:38:57,040
Speaker 1: kind of like, yeah, whatever, I'll give this answer. Well,

688
00:38:57,040 --> 00:38:59,000
Speaker 1: I mean there's more room for ego to get involved

689
00:38:59,000 --> 00:39:02,160
Speaker 1: to like take the the Highland or two question, as

690
00:39:02,200 --> 00:39:05,920
Speaker 1: I said, trivia about the budget of a film or

691
00:39:05,920 --> 00:39:08,400
Speaker 1: the growth of the film. Like, that's not an interest

692
00:39:08,440 --> 00:39:11,240
Speaker 1: area for me, and I'm not I'm not really hesitant

693
00:39:11,280 --> 00:39:13,839
Speaker 1: to be way off the mark on it. But if

694
00:39:13,880 --> 00:39:17,920
Speaker 1: it were a question about like a particular actor in

695
00:39:17,960 --> 00:39:21,480
Speaker 1: the film, like who played the villain in Highlander too,

696
00:39:21,960 --> 00:39:24,719
Speaker 1: which was because of course Michael Ironside. But if if

697
00:39:24,760 --> 00:39:27,680
Speaker 1: that name wasn't instantly coming to my head, I would

698
00:39:27,760 --> 00:39:31,600
Speaker 1: be less uh less brave about just blurting something out,

699
00:39:31,719 --> 00:39:35,040
Speaker 1: you know, because this is something I should know. So

700
00:39:35,040 --> 00:39:37,640
Speaker 1: I'm gonna be more cautious. Right. Well, the idea of

701
00:39:37,680 --> 00:39:42,279
Speaker 1: ego does introduce something about motivation, right, You can have

702
00:39:42,320 --> 00:39:46,000
Speaker 1: differential motivations and how you should try to answer questions.

703
00:39:46,360 --> 00:39:50,239
Speaker 1: Maybe the problem is, um people just don't care enough

704
00:39:50,280 --> 00:39:53,480
Speaker 1: to really try to answers answer these questions. Right, So

705
00:39:53,520 --> 00:39:56,279
Speaker 1: what happens if you give people incentives to get the

706
00:39:56,320 --> 00:39:59,839
Speaker 1: answer right, The answer is not much. Incentives and pay

707
00:39:59,840 --> 00:40:02,680
Speaker 1: off for accuracy have not been shown to correct the

708
00:40:02,719 --> 00:40:05,799
Speaker 1: anchoring effect. People are still affected by anchors And of

709
00:40:05,840 --> 00:40:07,279
Speaker 1: course this comes back to the idea that it is

710
00:40:07,320 --> 00:40:10,840
Speaker 1: an implicit process. Yeah, exactly, here's one that should be

711
00:40:10,880 --> 00:40:12,960
Speaker 1: a should be a total deal breaker. Here's how you

712
00:40:13,000 --> 00:40:16,839
Speaker 1: defeat the anchoring effect. Right for warning people, you say,

713
00:40:17,320 --> 00:40:20,600
Speaker 1: there's this thing called the anchoring effect, and we're going

714
00:40:20,680 --> 00:40:23,320
Speaker 1: to give you a number, and that number is probably

715
00:40:23,360 --> 00:40:26,960
Speaker 1: going to contaminate, uh, the way in which you answer

716
00:40:27,040 --> 00:40:29,480
Speaker 1: the question, So that number is going to bias your

717
00:40:29,520 --> 00:40:33,640
Speaker 1: answer towards that number. Be aware of the anchoring effect. Unfortunately,

718
00:40:34,040 --> 00:40:37,600
Speaker 1: studies have shown this doesn't work. Even when you explain

719
00:40:37,719 --> 00:40:40,600
Speaker 1: the anchoring effect to people and warn them that it

720
00:40:40,680 --> 00:40:44,239
Speaker 1: may be biasing their thinking, they are still vulnerable to it.

721
00:40:44,560 --> 00:40:46,600
Speaker 1: I want to try one out. This is just off off,

722
00:40:47,400 --> 00:40:50,359
Speaker 1: just shooting from the hip. Here. How many dwarves are

723
00:40:50,480 --> 00:40:54,240
Speaker 1: in the Disney movie Snow White in the seven Dwarves?

724
00:40:54,280 --> 00:40:59,680
Speaker 1: More or less than thirty eight? Like, just running it

725
00:40:59,680 --> 00:41:02,400
Speaker 1: through my mind, I feel the contamination of that question,

726
00:41:02,640 --> 00:41:05,920
Speaker 1: even though the answer is obvious, even though there should

727
00:41:05,920 --> 00:41:09,960
Speaker 1: be no rational reason to gravitate towards thirty eight, it

728
00:41:09,960 --> 00:41:14,800
Speaker 1: it begins to introduce like weeds of of doubt. Yeah, yeah, totally.

729
00:41:15,320 --> 00:41:17,040
Speaker 1: I mean in the same way that I don't know

730
00:41:17,080 --> 00:41:21,960
Speaker 1: if you've ever had this experience of like reading a

731
00:41:21,960 --> 00:41:25,920
Speaker 1: an obvious like fake news article on the internet, like

732
00:41:26,000 --> 00:41:29,440
Speaker 1: somebody posts something it's like from a conspiracy theory website

733
00:41:29,560 --> 00:41:31,719
Speaker 1: or you know, one of those fake news websites, or

734
00:41:31,760 --> 00:41:34,600
Speaker 1: something that's just obviously made up, is not from a

735
00:41:34,640 --> 00:41:39,000
Speaker 1: reputable news source. Even though you know this is obviously untrue,

736
00:41:39,040 --> 00:41:41,759
Speaker 1: you can kind of feel it's sort of like, yeah,

737
00:41:41,920 --> 00:41:45,360
Speaker 1: creeping in this, like you don't have you don't you

738
00:41:45,520 --> 00:41:49,680
Speaker 1: honestly put any credence in it being true, But just

739
00:41:49,719 --> 00:41:52,919
Speaker 1: the fact that the words appear on the screen has

740
00:41:53,000 --> 00:41:56,680
Speaker 1: some kind of like magical conjuring effect on your mind

741
00:41:56,840 --> 00:42:00,400
Speaker 1: that makes you sort of start like entertaining doubts about reality.

742
00:42:00,840 --> 00:42:03,200
Speaker 1: Yeah yeah, no, I've I've felt the same thing. And

743
00:42:03,280 --> 00:42:06,920
Speaker 1: you see that too with just straight up tabloid coverage

744
00:42:07,000 --> 00:42:10,520
Speaker 1: and slanderous statements, like the mere fact that it is

745
00:42:10,840 --> 00:42:13,719
Speaker 1: pumped into a headline gives it a certain life that

746
00:42:13,800 --> 00:42:16,680
Speaker 1: it shouldn't have. Okay, but what about on the individual level.

747
00:42:16,680 --> 00:42:18,160
Speaker 1: Are are some of it is just going to be

748
00:42:18,200 --> 00:42:22,240
Speaker 1: more susceptible than others. Uh, it does appear by based

749
00:42:22,280 --> 00:42:26,160
Speaker 1: on some preliminary research that that is the case. But

750
00:42:26,320 --> 00:42:28,319
Speaker 1: this this is not as solid as some of the

751
00:42:28,320 --> 00:42:33,280
Speaker 1: other research. But preliminary research says that participants with high

752
00:42:33,360 --> 00:42:37,440
Speaker 1: conscientiousness and that generally means things like self control and

753
00:42:37,480 --> 00:42:43,040
Speaker 1: self discipline, and high agreeableness that's how long you how

754
00:42:43,040 --> 00:42:47,600
Speaker 1: well you get along with others, and low extroversion meaning

755
00:42:48,600 --> 00:42:52,560
Speaker 1: and people who are introverted. Those three things also coupled

756
00:42:52,600 --> 00:42:56,440
Speaker 1: with high openness to experience, which these are all getting

757
00:42:56,440 --> 00:43:00,160
Speaker 1: into the Big five personality traits, these things are are

758
00:43:00,200 --> 00:43:03,439
Speaker 1: more susceptible to the anchoring effect. But like I said,

759
00:43:03,840 --> 00:43:06,680
Speaker 1: the study cautions that these are these are not super

760
00:43:06,719 --> 00:43:09,480
Speaker 1: solid results. This is just sort of like something that

761
00:43:09,520 --> 00:43:12,759
Speaker 1: appears to possibly be true. Now, the question would be

762
00:43:12,800 --> 00:43:17,080
Speaker 1: why those traits, Why would those things lend lends susceptibility

763
00:43:17,080 --> 00:43:20,160
Speaker 1: to the anchoring effect. To quote from the two thousand

764
00:43:20,200 --> 00:43:24,760
Speaker 1: eleven study, quote, individuals with high conscientiousness engage in more

765
00:43:24,920 --> 00:43:29,200
Speaker 1: thorough thought processes before judgments are made. Those with high

766
00:43:29,280 --> 00:43:34,400
Speaker 1: agreeableness take the provided anchors seriously, and high openness to

767
00:43:34,440 --> 00:43:40,120
Speaker 1: experience influences individuals who are more sensitive to anchor cues. Also,

768
00:43:40,200 --> 00:43:42,879
Speaker 1: they say that low extra version is possibly explained through

769
00:43:42,880 --> 00:43:47,400
Speaker 1: a correlation with sad mood, which apparently increases susceptibility to

770
00:43:47,440 --> 00:43:50,960
Speaker 1: the anchoring effect. As we explained earlier. Huh. Now, now

771
00:43:51,000 --> 00:43:55,560
Speaker 1: the the openness, high openness to experience, that that rings

772
00:43:55,600 --> 00:43:57,879
Speaker 1: true from here as well. And I feel like I've

773
00:43:57,920 --> 00:44:02,319
Speaker 1: seen that represented in other studies looking at you know,

774
00:44:02,360 --> 00:44:07,319
Speaker 1: individuals with liberal or conservative viewpoints. Uh, someone might ask,

775
00:44:07,440 --> 00:44:10,000
Speaker 1: or are you open to new experiences? I see you're

776
00:44:10,040 --> 00:44:13,640
Speaker 1: into uh, you know, extreme sports and uh and and

777
00:44:13,680 --> 00:44:17,319
Speaker 1: other new novel things in your life. Sure, well, are

778
00:44:17,320 --> 00:44:19,680
Speaker 1: you open to the idea that Voldemort would make a

779
00:44:19,719 --> 00:44:22,960
Speaker 1: great president and Harry Potter was a terrorist? And maybe

780
00:44:23,000 --> 00:44:27,879
Speaker 1: you are? You know, you're open to alternative viewpoints, alternative worldviews, right,

781
00:44:28,400 --> 00:44:31,279
Speaker 1: And that kind of that kind of mind can be

782
00:44:31,320 --> 00:44:33,640
Speaker 1: a dangerous thing because if you have a closed off mind,

783
00:44:33,800 --> 00:44:36,520
Speaker 1: and it kind of runs both both ways, good information

784
00:44:36,600 --> 00:44:39,440
Speaker 1: is not getting in, but also maybe bad information is

785
00:44:39,520 --> 00:44:42,239
Speaker 1: less likely to get in. So so like I said

786
00:44:42,280 --> 00:44:45,520
Speaker 1: that that that aspect of the argument, definitely I think

787
00:44:45,600 --> 00:44:47,960
Speaker 1: rings true for me. Okay, here's another one. What about

788
00:44:48,000 --> 00:44:52,920
Speaker 1: analytical intelligence? Will people with just greater cognitive abilities be

789
00:44:52,920 --> 00:44:55,920
Speaker 1: better at avoiding the effects of anchoring? Uh? This is

790
00:44:55,960 --> 00:44:58,480
Speaker 1: one where research is divided on the topic, at least

791
00:44:58,480 --> 00:45:00,560
Speaker 1: to the time this meta review is under it can

792
00:45:00,640 --> 00:45:03,959
Speaker 1: there were conflicting results. Essentially, some studies seem to find

793
00:45:04,040 --> 00:45:07,880
Speaker 1: that those with greater cognitive abilities were more resistant to anchoring,

794
00:45:07,960 --> 00:45:11,560
Speaker 1: and another study you found Nope, not the case. Okay, Well,

795
00:45:11,640 --> 00:45:13,680
Speaker 1: I mean we've seen plenty of studies before that show

796
00:45:13,760 --> 00:45:17,080
Speaker 1: that very intelligent people can be deceived and can be

797
00:45:17,239 --> 00:45:22,520
Speaker 1: self deceiving. So it would make sense that you're, you know,

798
00:45:22,600 --> 00:45:25,919
Speaker 1: cognitive level would only have so much influence on your

799
00:45:25,960 --> 00:45:28,800
Speaker 1: susceptibility to anchoring. Yeah, I mean, it's one of the

800
00:45:28,840 --> 00:45:31,560
Speaker 1: things we talked about in our Science Communication Breakdown episode

801
00:45:31,600 --> 00:45:36,280
Speaker 1: is that being a smart person does not necessarily protect

802
00:45:36,440 --> 00:45:41,560
Speaker 1: you against radicalizing yourself with untrue beliefs on a partisan basis.

803
00:45:42,040 --> 00:45:45,560
Speaker 1: Maria Kanakova has an entire book, UH dealing with with

804
00:45:45,680 --> 00:45:47,960
Speaker 1: con artists, and one of her key points is it

805
00:45:48,120 --> 00:45:50,680
Speaker 1: very intelligent people can be duped by things like this. Yeah,

806
00:45:50,680 --> 00:45:53,640
Speaker 1: smart people are vulnerable to con artists she's got a

807
00:45:53,640 --> 00:45:55,879
Speaker 1: great story, and that it's not a great story. It's

808
00:45:55,880 --> 00:45:58,640
Speaker 1: a sad story, but it's about like, what is it?

809
00:45:58,719 --> 00:46:01,960
Speaker 1: A nuclear physicis cysts who gets taken in on this

810
00:46:02,040 --> 00:46:05,400
Speaker 1: bizarre drug running scheme. Yes, I believe. So I have

811
00:46:05,480 --> 00:46:07,880
Speaker 1: to revisit to make sure I got the details right.

812
00:46:07,920 --> 00:46:10,160
Speaker 1: But that's a good book. Book. It's worth reading, by

813
00:46:10,160 --> 00:46:11,919
Speaker 1: the way. All Right, we're gonna take a quick break

814
00:46:11,960 --> 00:46:13,600
Speaker 1: and when we come back, we'll give you a little

815
00:46:13,640 --> 00:46:19,000
Speaker 1: advice on how to avoid the anchoring effect. All right,

816
00:46:19,040 --> 00:46:22,480
Speaker 1: we're back. So the question you're obviously wondering about is

817
00:46:22,520 --> 00:46:25,120
Speaker 1: you We've gone through all these reasons that the anchoring

818
00:46:25,120 --> 00:46:29,040
Speaker 1: effect appears incredibly robust, despite the fact that people want

819
00:46:29,080 --> 00:46:30,920
Speaker 1: to be able to avoid it and not have it

820
00:46:31,000 --> 00:46:34,360
Speaker 1: influenced their thinking, it just seems to work every time.

821
00:46:35,040 --> 00:46:38,000
Speaker 1: Uh So, how do you get around it? Well, this

822
00:46:38,080 --> 00:46:40,640
Speaker 1: comes up in uh in the two thousand eleven paper

823
00:46:40,680 --> 00:46:46,520
Speaker 1: we've been discussing, and the results are not great. There

824
00:46:46,560 --> 00:46:50,160
Speaker 1: there is not a whole lot of hope to be offered. Um.

825
00:46:50,320 --> 00:46:53,600
Speaker 1: One of the one of the strategies that has been

826
00:46:53,960 --> 00:46:56,560
Speaker 1: put out there is something that might work is what's

827
00:46:56,600 --> 00:47:00,960
Speaker 1: known as the consider the opposite strategy. Now, this is

828
00:47:01,000 --> 00:47:05,200
Speaker 1: effective at some types of d biasing. De Biasing is

829
00:47:05,239 --> 00:47:07,960
Speaker 1: the process of, you know, trying to remove your personal bias,

830
00:47:08,760 --> 00:47:11,800
Speaker 1: and so consider the opposite strategies are Actually it seems

831
00:47:11,800 --> 00:47:15,600
Speaker 1: pretty simple, but it's worth learning how to do. When

832
00:47:15,640 --> 00:47:20,160
Speaker 1: you think something is true, just sit there and come

833
00:47:20,239 --> 00:47:23,120
Speaker 1: up with a list of reasons it might not be true.

834
00:47:23,480 --> 00:47:26,239
Speaker 1: I think this is reasonable. Yeah, I mean a sort

835
00:47:26,239 --> 00:47:30,440
Speaker 1: of a science fiction example would be Star Wars looking

836
00:47:30,480 --> 00:47:33,279
Speaker 1: at the the the Empire. Is the Empire good or

837
00:47:33,320 --> 00:47:36,200
Speaker 1: is the Empire bad? You're told that they're bad, but

838
00:47:36,280 --> 00:47:40,160
Speaker 1: sometimes it's helpful to entertain the opposite viewpoint. Maybe the

839
00:47:40,160 --> 00:47:42,400
Speaker 1: Empire was good. I don't know what the arguments for

840
00:47:42,440 --> 00:47:45,000
Speaker 1: that would be, but okay, I don't know if it

841
00:47:45,000 --> 00:47:47,040
Speaker 1: holds up anymore, but I feel like there was a

842
00:47:47,080 --> 00:47:52,160
Speaker 1: time when when the argument was more convincing, or at

843
00:47:52,239 --> 00:47:54,719
Speaker 1: least I couldn't see that the Empire is good, but

844
00:47:54,760 --> 00:47:58,080
Speaker 1: I could see that the rebellion is also evil. Yes,

845
00:47:58,200 --> 00:48:00,080
Speaker 1: I could say that the Empire and the rebellion and

846
00:48:00,080 --> 00:48:03,160
Speaker 1: are both evil. Yeah. I feel like they're leaning into

847
00:48:03,200 --> 00:48:06,279
Speaker 1: that more with the recent films, right, Maybe, I don't

848
00:48:06,320 --> 00:48:09,560
Speaker 1: know But anyway, it comes back to a popular bit

849
00:48:09,600 --> 00:48:12,680
Speaker 1: of advice that Timothy Leary gave everyone. Right. Yes, though

850
00:48:12,719 --> 00:48:14,440
Speaker 1: a lot of people say that, and I think a

851
00:48:14,440 --> 00:48:17,640
Speaker 1: lot of times they just think that means like, don't

852
00:48:17,640 --> 00:48:20,600
Speaker 1: believe what the man tells. That is a part of it.

853
00:48:20,680 --> 00:48:23,680
Speaker 1: But another very important part of thinking for yourself is

854
00:48:24,000 --> 00:48:29,359
Speaker 1: questioning your internal authority, questioning what seems reasonable to you

855
00:48:29,480 --> 00:48:31,960
Speaker 1: at this moment. And a good way to do that,

856
00:48:32,000 --> 00:48:36,440
Speaker 1: apparently is to try this consider the opposite strategy. Just

857
00:48:36,600 --> 00:48:39,239
Speaker 1: honestly do your best to come up with a list

858
00:48:39,280 --> 00:48:43,239
Speaker 1: of reasons why what you're thinking is probably wrong. And

859
00:48:43,320 --> 00:48:46,279
Speaker 1: then you consider that list and you think about are

860
00:48:46,360 --> 00:48:50,520
Speaker 1: these reasons reasonable? And so this this has been shown

861
00:48:50,760 --> 00:48:54,000
Speaker 1: to be effective at some things, some types of debiasing,

862
00:48:54,480 --> 00:48:57,680
Speaker 1: but apparently it is not shown to be very effective

863
00:48:57,680 --> 00:49:01,720
Speaker 1: with anchoring. Well that's not good at all. Nope. Another

864
00:49:01,760 --> 00:49:04,800
Speaker 1: thing I want to read a quote from the paper.

865
00:49:04,880 --> 00:49:08,040
Speaker 1: Quote in their popular book on behavioral economics, bell Ski

866
00:49:08,120 --> 00:49:11,880
Speaker 1: and Golovich warned people that they may be prone to

867
00:49:12,000 --> 00:49:15,560
Speaker 1: confirmation biases and anchoring if they make spending and investment

868
00:49:15,600 --> 00:49:20,200
Speaker 1: decisions without research. They are especially loyal to certain brands

869
00:49:20,280 --> 00:49:23,439
Speaker 1: or investments for the wrong reasons. They find it hard

870
00:49:23,480 --> 00:49:26,080
Speaker 1: to see investments for less than they paid for them,

871
00:49:26,280 --> 00:49:29,320
Speaker 1: and they rely on the seller's price rather than assessing

872
00:49:29,360 --> 00:49:33,040
Speaker 1: the value themselves. They advise people to avoid the pitfall

873
00:49:33,080 --> 00:49:37,560
Speaker 1: of anchoring by broadening their board of advisors, so listening

874
00:49:37,560 --> 00:49:42,680
Speaker 1: to more people, doing more thorough research before making economic decisions,

875
00:49:43,760 --> 00:49:46,360
Speaker 1: So not just relying on one anchor you're seeing in

876
00:49:46,400 --> 00:49:48,680
Speaker 1: the store, but trying to get as much information in

877
00:49:48,719 --> 00:49:54,480
Speaker 1: front of you as possible, looking at trends, being realistic

878
00:49:54,600 --> 00:49:57,520
Speaker 1: and taking the longer view, and showing a little more

879
00:49:57,600 --> 00:50:01,080
Speaker 1: humility when it comes to one's own judgment. And now

880
00:50:01,160 --> 00:50:03,239
Speaker 1: all of this seems like good advice to me, But

881
00:50:03,360 --> 00:50:07,279
Speaker 1: I don't know if this actually proves effective at overcoming

882
00:50:07,320 --> 00:50:10,920
Speaker 1: the anchoring bias, right, because in all of these cases,

883
00:50:11,040 --> 00:50:13,480
Speaker 1: if you just had this this checklist in your pocket,

884
00:50:13,840 --> 00:50:16,480
Speaker 1: you would still being You would still be employing it

885
00:50:16,600 --> 00:50:21,359
Speaker 1: explicitly trying to counter something that is occurring implicitly. Yeah,

886
00:50:21,400 --> 00:50:23,160
Speaker 1: Now there are a few other ideas I was just

887
00:50:23,239 --> 00:50:25,960
Speaker 1: thinking about that these are not tested, but I was

888
00:50:26,000 --> 00:50:28,120
Speaker 1: trying to think, well, what could you do given how

889
00:50:28,320 --> 00:50:34,759
Speaker 1: robust the anchoring effect is. Here's one whenever possible. What

890
00:50:34,840 --> 00:50:38,640
Speaker 1: can you do to avoid the anchor? Like in situations

891
00:50:38,680 --> 00:50:41,120
Speaker 1: where you're going to have to make a judgment and

892
00:50:41,160 --> 00:50:43,960
Speaker 1: you know that you may be exposed to an anchor

893
00:50:44,000 --> 00:50:48,040
Speaker 1: that works against you, just try to protect yourself from

894
00:50:48,040 --> 00:50:50,600
Speaker 1: being exposed to it. Do whatever you can to avoid

895
00:50:50,719 --> 00:50:54,840
Speaker 1: actually encountering that anchor. Huh. Then this sounds like a

896
00:50:54,920 --> 00:50:58,880
Speaker 1: potential role for a an Internet browser filter, like an

897
00:50:58,920 --> 00:51:02,240
Speaker 1: anchor filter, where it will take out any any leading

898
00:51:02,360 --> 00:51:05,359
Speaker 1: numbers and whatever you might be reading. Yeah, but then again,

899
00:51:05,400 --> 00:51:07,200
Speaker 1: it's hard to know how to do that right, Like,

900
00:51:07,239 --> 00:51:10,000
Speaker 1: you don't want to cut yourself off from incoming information

901
00:51:10,040 --> 00:51:12,520
Speaker 1: that may actually be useful to you. True, and you

902
00:51:12,560 --> 00:51:15,120
Speaker 1: don't just remove all numbers from your news feed. That

903
00:51:15,200 --> 00:51:18,200
Speaker 1: sounds a bit extreme. Yeah, here's another one that is

904
00:51:18,320 --> 00:51:24,080
Speaker 1: much more, much more directly related to price negotiations. Uh,

905
00:51:24,520 --> 00:51:28,480
Speaker 1: be preemptive, set your own anchor before you're a negotiating

906
00:51:28,480 --> 00:51:31,680
Speaker 1: opponent has a chance to set an anchor for you. So,

907
00:51:31,719 --> 00:51:33,880
Speaker 1: if you want to pay a lower price on something,

908
00:51:34,200 --> 00:51:36,319
Speaker 1: apparently a good way to do that is you be

909
00:51:36,440 --> 00:51:39,160
Speaker 1: the first person to say something and set your really

910
00:51:39,200 --> 00:51:42,839
Speaker 1: really really low estimate or high estimate. If you're looking right,

911
00:51:42,880 --> 00:51:45,480
Speaker 1: if you're paid, if you're trying to get paid, Yeah, exactly,

912
00:51:45,760 --> 00:51:48,040
Speaker 1: this sounds it sounds like the art of the deal

913
00:51:48,320 --> 00:51:51,080
Speaker 1: right here. I don't think exactly is the art of

914
00:51:51,120 --> 00:51:55,000
Speaker 1: the deal um, but yeah, you can use anchoring to

915
00:51:55,040 --> 00:51:56,759
Speaker 1: your advantage. Most of the time people are going to

916
00:51:56,800 --> 00:51:58,839
Speaker 1: be trying to use it against you, But there are

917
00:51:58,880 --> 00:52:02,080
Speaker 1: cases where we're normal people who are not in advertising

918
00:52:02,160 --> 00:52:05,440
Speaker 1: or sales or whatever can try to use this. For example,

919
00:52:05,880 --> 00:52:08,799
Speaker 1: studies have actually been conducted and found that when you

920
00:52:09,160 --> 00:52:10,960
Speaker 1: if you're trying to get a higher salary at work,

921
00:52:11,000 --> 00:52:14,520
Speaker 1: you're trying to negotiate your pay up. Uh, salary negotiations

922
00:52:14,560 --> 00:52:17,720
Speaker 1: that open with a very high request are more likely

923
00:52:17,760 --> 00:52:20,319
Speaker 1: to end up with a higher salary offer in the end,

924
00:52:20,680 --> 00:52:23,920
Speaker 1: even if the opening anchor you request is way too high.

925
00:52:24,920 --> 00:52:29,200
Speaker 1: So going to every negotiation saying thirty million dollars, just

926
00:52:29,239 --> 00:52:31,520
Speaker 1: go for the Sean Connery money right off the bat.

927
00:52:31,560 --> 00:52:33,720
Speaker 1: I don't know if thirty million dollars, I mean, maybe

928
00:52:33,719 --> 00:52:35,920
Speaker 1: it will. I don't know. Then again, I mean, I

929
00:52:35,960 --> 00:52:40,720
Speaker 1: feel like if you're negotiating with a with a business person,

930
00:52:40,800 --> 00:52:43,840
Speaker 1: they've probably been trained to some extent about some version

931
00:52:43,880 --> 00:52:47,240
Speaker 1: of the anchoring effects. But then again, as we've discussed earlier,

932
00:52:47,400 --> 00:52:50,080
Speaker 1: knowing that out, yeah, knowing about it doesn't make it

933
00:52:50,160 --> 00:52:52,000
Speaker 1: not work on you. Hey, if you want to check

934
00:52:52,000 --> 00:52:53,399
Speaker 1: out more Stuff to Bbow your Mind, head on over

935
00:52:53,440 --> 00:52:55,359
Speaker 1: to stuff to Blow your Mind dot com. That's we'll

936
00:52:55,400 --> 00:52:57,880
Speaker 1: find all the podcast episodes, blog post and links out

937
00:52:57,920 --> 00:53:00,560
Speaker 1: to our various social media accounts. Big Thing says always

938
00:53:00,600 --> 00:53:03,799
Speaker 1: to our audio producers Alex Williams and Tory Harrison, And

939
00:53:03,880 --> 00:53:05,480
Speaker 1: if you want to get in touch with us directly,

940
00:53:05,520 --> 00:53:08,480
Speaker 1: as always, you can email us at blow the Mind

941
00:53:08,560 --> 00:53:21,080
Speaker 1: at how stuff works dot com for more on this

942
00:53:21,280 --> 00:53:23,759
Speaker 1: and thousands of other topics. Does it how stuff works

943
00:53:23,800 --> 00:53:47,000
Speaker 1: dot com