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Speaker 1: Pushkin let's play around Warders Studios. It sounds very elvin.

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Speaker 2: I always think of David Bowie when I think of Wardog.

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Speaker 2: In one of his early songs talks about right light,

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Speaker 2: soho wardour Street.

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Speaker 1: Bowie himself has an elfin aspect.

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Speaker 2: He said he does is break through gig at Aylesbury,

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Speaker 2: my hometown, which basically is about the only thing remarking

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Speaker 2: about Aylesbury is.

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Speaker 1: Well and also home of Tim Harford.

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Speaker 3: As the plaque says.

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Speaker 2: They've got a statue for Bowie, not yet for me.

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Speaker 2: I don't know why.

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Speaker 3: Here we go, Okay, let's go, okay, I'm ready.

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Speaker 2: Hello, and welcome back to another episode of Cautionary Questions,

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Speaker 2: our first of twenty twenty four. I am, of course

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Speaker 2: Tim Harford. You are our loyal listeners. You've been sending

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Speaker 2: in your burning questions on money, technology, economics and problem solving,

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Speaker 2: and thank you so much to everyone who's done so,

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Speaker 2: and today is the day I do my best to

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Speaker 2: answer them. And thankfully I won't be alone in this endeavor.

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Speaker 2: Here to help me out both of the questions, and

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Speaker 2: I think with some of the answers is the brilliant,

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Speaker 2: brilliant Jacob Goldstein, the host of Pushkin podcast What's Your Problem,

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Speaker 2: author of the book Money, The True Story of a

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Speaker 2: Made Up Thing. Jacob was my inaugural Cautionary Questions co

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Speaker 2: host Jacob's wonderful to.

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Speaker 1: Have you back, tim Asonana.

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Speaker 2: Hi, So we should just get on with the questions

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Speaker 2: because we always have so many and so much to say.

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Speaker 2: So what have you got in real big bag of

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Speaker 2: listener questions for me?

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Speaker 1: Let's start with a question from Alex in Melbourne, Australia.

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Speaker 1: Alex writes, I work with artificial intelligence image generation software

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Speaker 1: almost daily now, and I'm quickly seeing how so much

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Speaker 1: of my workforce and processes can either be sped up

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Speaker 1: or entirely replaced by AI, and this makes me nervous.

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Speaker 1: And then he asks about universal basic income, right, this

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Speaker 1: idea of a government giving all its citizens' money, And

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Speaker 1: he says it seems for the first time that computers

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Speaker 1: and software will actually replace jobs in a deeply concerning way,

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Speaker 1: which is both exciting and terrifying. What are your thoughts

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Speaker 1: on UBI, universal basic income as a solution to an

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Speaker 1: AI crisis and the widespread philosophical and economic implications of this.

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Speaker 2: I love this question. I mean it's so big, and

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Speaker 2: I think the first thing to say is we don't

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Speaker 2: really have any idea. If what Alex is thinking about

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Speaker 2: comes true, and if most people just have no economic value.

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Speaker 2: They have value as human beings, have value as members

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Speaker 2: of society, but there's nothing that they could actually sell

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Speaker 2: their labor to do, then that's completely uncharted territory. We've

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Speaker 2: never been anywhere like that before, so everything we do

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Speaker 2: is kind of speculative.

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Speaker 1: We've feared it for a long time, right We've have

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Speaker 1: two hundred years of being afraid of technological unemployment. And

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Speaker 1: my prior on this is to be somewhat skeptical. Right, Like,

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Speaker 1: clearly there can be some large number of people who

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Speaker 1: lose their jobs, and we should be concerned about that,

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Speaker 1: and we should think about how to mitigate that, But

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Speaker 1: the idea of more or less everybody losing their jobs

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Speaker 1: I'm skeptical of for the simple reason that it hasn't

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Speaker 1: happened in two hundred years of incredible technological progress. And

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Speaker 1: right now, after decades of extreme technological progress, in the US,

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Speaker 1: unemployment is below four percent, the share of working age

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Speaker 1: people who are working is near all time highs, and

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Speaker 1: so somehow we keep coming up with new things to

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Speaker 1: do for money, no matter how many things computers can do.

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Speaker 1: And so my first thought is, I don't think we're

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Speaker 1: going to have everybody losing their jobs to AI. I

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Speaker 1: definitely could be wrong, but that's what I think.

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Speaker 2: No, I think that's a good working assumption. If you

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Speaker 2: think back a few centuries, basically, almost all the labor

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Speaker 2: that people did, they might wash their clothes occasionally, Well,

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Speaker 2: that's been outsourced to the washing machines that spend a

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Speaker 2: lot of time moving water around, just drinking water, cooking water,

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Speaker 2: throwing out human excrement. That's all now handled by automated systems.

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Speaker 1: Digging people did a lot of digging, right, pulling a plow.

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Speaker 2: Almost everything we used to do is now done by machines.

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Speaker 2: But somehow we still all have jobs. Let's at least

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Speaker 2: accept the premise that maybe this time it might be different.

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Speaker 2: Because the robots are doing everything. There's still material prosperity,

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Speaker 2: there's food out there, in all the services we could

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Speaker 2: possibly want. We just have to find some system and

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Speaker 2: whereby the humans who have no economic value get to

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Speaker 2: enjoy all this cool stuff that's being produced by the machines.

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Speaker 1: Yeah, and you know, technological prosperity has given us in

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Speaker 1: the developed world ubi for old people, right in the US,

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Speaker 1: as in every developed country as far as I know,

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Speaker 1: once you get to some age, if you are a

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Speaker 1: citizen and you have worked, yeah, the government gives you money, yeah,

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Speaker 1: every month until you die, right, And so you could

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Speaker 1: imagine a kind of creeping extension of that. Certainly, right

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Speaker 1: now they're not talking about lowering the retirement age in

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Speaker 1: the US. They're talking about raising it as in many countries.

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Speaker 2: Yeah, raising it in the UK as well, But it's

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Speaker 2: still a long way away from life expectancies. My recollection

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Speaker 2: is that when Bismarck introduced the first pension, which was

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Speaker 2: in Germany in the late nineteenth century, I think the

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Speaker 2: pension started to be paid at the age sixty seven

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Speaker 2: and the life expectancy was sixty three.

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Speaker 1: You get negative four years in expectation.

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Speaker 2: People who survive long enough to claim any pension at

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Speaker 2: all are already exceptional. So that would be like having

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Speaker 2: a pension today that started at age ninety.

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Speaker 4: Like some people will get it, but most bad way.

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Speaker 2: But actually a lot of people could easily collect thirty

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Speaker 2: years of pension. Certainly twenty years so they're living a

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Speaker 2: large proportion of the adult life receiving money from the state,

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Speaker 2: and they're also receiving money from their own savings, their

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Speaker 2: own investments, which might be a replacement for UBI. Maybe

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Speaker 2: we all just have shares in the robots instead with

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Speaker 2: shares in Google or whatever, and that's how we get paid.

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Speaker 1: Yes, whether that is mediated by the government or not

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Speaker 1: looks somewhat different. Right, Either the government is taxing the

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Speaker 1: owners of in Nvidia and Google stock and distributing the money,

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Speaker 1: or everybody owns Google and Nvidia stock. There is the

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Speaker 1: piece of this which is about the non financial parts

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Speaker 1: of work, right, I mean there's a political economy piece,

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Speaker 1: the sort of bridge how do we get from here

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Speaker 1: to there? If that happens, And that's complicated and maybe ugly.

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Speaker 2: If the robots can do all of the stuff the

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Speaker 2: computers can do it, there's no economic reason why humans

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Speaker 2: couldn't just receive whatever an allowance they're given ten robots,

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Speaker 2: or they're given ten thousand dollars a month to spend

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Speaker 2: or whatever they like. There's no economic reason why that

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Speaker 2: couldn't happen. But I think what you're getting at is

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Speaker 2: what does it do to us if we're in that situation.

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Speaker 1: Yeah, yeah, Like I don't want to not have a job.

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Speaker 1: I recognize that I am fortunate to have a job

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Speaker 1: that I enjoy, that I derive a part of my

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Speaker 1: identity from. I recognize that for many, in fact probably

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Speaker 1: most people, a job is not that it's some unpleasant

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Speaker 1: thing they do because they need the money, and if

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Speaker 1: they got more money, they would quit their job. Well,

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Speaker 1: you probably know the kind of empirical evidence on this

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Speaker 1: better than I do. Like, people have looked at lottery winners, right,

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Speaker 1: and my sense is it's not great for you to

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Speaker 1: win the lottery and quit your job. It doesn't actually

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Speaker 1: make you happier.

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Speaker 2: Actually, the evidence on lottery winners is a bit mixed,

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Speaker 2: and I think slightly. We hear all the disaster stories

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Speaker 2: of lottery winners.

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Speaker 1: Actually, it's great to win the lottery. That's amazing. Is

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Speaker 1: that true?

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Speaker 4: I think it's fine. Yes, it's just fine to win

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Speaker 4: the lottery.

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Speaker 2: Is not really a problem to win the lottery. In

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Speaker 2: my vague recollection, I wrote about this a couple of

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Speaker 2: years ago. But we may not all want a job,

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Speaker 2: but we all want something to do. We all want

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Speaker 2: to feel useful. We all want a sense of some

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Speaker 2: kind of purpose. We all want, I think, that experience

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Speaker 2: of mastery, that experience of knowing that you can do

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Speaker 2: something that not everybody can do. Those things don't have

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Speaker 2: to come from a job, but for a lot of

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Speaker 2: people they do come from a job.

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Speaker 1: Yeah. For me, in my narrow provincial experience of life,

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Speaker 1: it's frankly hard to imagine getting those things without a job. Yeah.

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Speaker 2: It comes back to my initial reaction to Alex's terrific question,

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Speaker 2: which is this is such unknown territory that we can

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Speaker 2: only really speculate. But I think you and I J.

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Speaker 2: Cub are in agreement that the fundamental issue here is

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Speaker 2: not economic. It's really to do with our souls. How

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Speaker 2: would we react if our desire for mastery, our desire

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Speaker 2: for meaning, our desire to feel useful, if that had

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Speaker 2: to be satisfied without having a job, And what would

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Speaker 2: we do? And could we cope? And I don't know, I.

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Speaker 1: Mean, Harford, if the robots come and take our jobs,

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Speaker 1: let's just you and me make a podcast for free.

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Speaker 1: I'm in. I'll commit right now in the robot utopia

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Speaker 1: apocalypse to making a weekly podcast with you for free.

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Speaker 4: Deal.

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Speaker 3: Sorry, we have no more time you might give in

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Speaker 3: your robot overload.

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Speaker 1: Give us another question, Hello, Race, I'm enough. I would

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Speaker 1: like to ask, with the onset of AI, what is

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Speaker 1: the next cautionary tale you anticipate talking about in the

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Speaker 1: years to come?

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Speaker 2: Hello Abanov, I have just finished the first draft of

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Speaker 2: a script about the coming of AI, and without giving

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Speaker 2: too much away, the cautionary note is about what happens

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Speaker 2: when automation gets so good that we lose our own skills,

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Speaker 2: we hand over controlled to the machine, and then how

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Speaker 2: we respond when the machine says, actually, this one's too hard?

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Speaker 2: Could you take over again? Suddenly we're back at the

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Speaker 2: wheel and we're out of practice.

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Speaker 1: Harford, It's not another plane crash, is it? Don't you

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Speaker 1: have a moratry I'm undering plane crashes at this point

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Speaker 1: because it sounds like that, I'm.

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Speaker 2: Going to say nothing more fair enough, Let's go for

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Speaker 2: a break.

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Speaker 4: Right, Yeah, we're back.

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Speaker 2: I'm talking to Jacob Goldstein, the host of What's Your Problem,

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Speaker 2: and we are doing a Cautionary Questions Q and A episode.

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Speaker 2: Jacob has the questions. Jacob's also helping me with the answers,

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Speaker 2: and I'm really sepless to requirements here, but I'm doing

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Speaker 2: my best, Jacob, What have you got for me?

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Speaker 1: Tim? Our next question comes from Adam, who writes, Hi,

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Speaker 1: I have a question about investing in cryptocurrency.

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Speaker 4: Oh dear, oh dear, come on give it to them. Okay.

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Speaker 1: It's an interesting one. Traditionally, when buying shares in a company,

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Speaker 1: you would be supporting that company to grow, create a

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Speaker 1: new product, or enter a new retail space. This would

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Speaker 1: hopefully create jobs and new products. When people invest money

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Speaker 1: into cryptocurrency or any currency for that matter, isn't that

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Speaker 1: money just sitting around not doing anything until you withdraw it.

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Speaker 1: Would investing in crypto be bad for the economy compared

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Speaker 1: with investing in businesses?

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Speaker 4: This is a deep question. I really like it.

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Speaker 2: As the author of the wonderful book Money, The True

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Speaker 2: Story of a Maid art Thing, I am sure you.

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Speaker 4: Have thoughts, Jacob. Let me have a first crack and

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Speaker 4: you can tell me everything I've missed.

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Speaker 2: Yeah, so let's see you buy bitcoin or whatever, and

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Speaker 2: then the money's just sitting there because bitcoin is not

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Speaker 2: building anything. You're not buying investment in, say a road

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Speaker 2: building company, or you're not buying an investment in Google,

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Speaker 2: which is developing new technology. There's one of two possible

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Speaker 2: things that happens. So most likely is whoever you bought

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Speaker 2: the bitcoin from now has your money. Now it's their money,

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Speaker 2: and then they're going to do something with that money.

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Speaker 2: So maybe they then buy shares in Google, or they

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Speaker 2: then set up a business, or they will then go

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Speaker 2: on to do something with that money, or they'll lend

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Speaker 2: it to somebody else and that person will do something

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Speaker 2: with it. Eventually the money will find its way into

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Speaker 2: some productive investment. The fact that you bought bitcoin off

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Speaker 2: somebody does not mean that they won't then do something

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Speaker 2: useful with the money. But then let's say for some

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Speaker 2: reason they just go, you know what, I'm just going

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Speaker 2: to sit on this money. It's just dollars. I'm going

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Speaker 2: to stick them under the mattress. I guess a bitcoin

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Speaker 2: guy might do that. That would reduce inflationary pressures on

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Speaker 2: the economy, which is something we want anyway. And if

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Speaker 2: inflationary pressures are reduced too much, the Federal Reserve could

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Speaker 2: then go, you know what, guys, we should probably print

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Speaker 2: some money or otherwise stimulate the economy. Actually, the worst

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Speaker 2: thing that could possibly happen is that the money gets

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Speaker 2: invested in buying more computers that could be put to

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Speaker 2: better use, but in fact just end up doing bitcoin mining.

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Speaker 2: I guess what I'm saying is, as long as they

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Speaker 2: don't spend it on bitcoin mining computers, it's totally fine.

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Speaker 2: Well what am I missing?

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Speaker 1: I went around the same track as you went around,

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Speaker 1: and you know, different cryptocurrencies have different methods of regulation, right,

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Speaker 1: So bitcoin by design is this very energy intensive computing

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Speaker 1: process in order to mine bitcoin, and that is, in

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Speaker 1: my opinion, a bad outcome socially. Is I agree, because

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Speaker 1: you could imagine a world where maintaining the bitcoin network

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Speaker 1: was socially desirable. In the world as it has evolved,

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Speaker 1: I don't find it particularly socially desirable. But at this point,

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Speaker 1: I don't think most of the money going into bitcoin

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Speaker 1: is going to miners, right, there's a large stock of

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Speaker 1: bitcoin that exists in the world. I Mean. An interesting

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Speaker 1: thing about money is it almost never sleeps. It keeps going,

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Speaker 1: and so you can always ask what happens next. You know,

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Speaker 1: with the stock market, sometimes people talk about money on

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Speaker 1: the sidelines when they're like, well, the stock market can

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Speaker 1: go up somewhere because there's a lot of cash on

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Speaker 1: the sidelines. And there's this famous billionaire investor, cliff Asnas.

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Speaker 1: He's an interesting guy. He got a PhD from the

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Speaker 1: University of Chicago, studied with Gene Fama, famous economist, and

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Speaker 1: cliff Asnas gets driven up the wall when people say

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Speaker 1: cash on the sidelines for the same reason you just said, right,

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Speaker 1: Like every time somebody buys a share of stock, someone

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Speaker 1: else is selling it, right, So like the money is

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Speaker 1: changing hands, the stock is changing hands, but like there

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Speaker 1: are no sidelines with money.

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Speaker 2: Yeah, and even if someone puts just cash in a

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Speaker 2: checking account, well, then now the bank has money, and

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Speaker 2: you can bet the bank is going to want to

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Speaker 2: do something with that money, something productive. And even if

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Speaker 2: they doesn't put it in the bank, even if, as

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Speaker 2: I say, it's under a mattress, well, even then the

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Speaker 2: Federal Reserve could always put money.

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Speaker 1: Yeah, the stock of money is not fixed, right. I

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Speaker 1: think there's a there's an assumption in the question I

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Speaker 1: think goes back to the pre modern era, the era

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Speaker 1: before the nineteen thirties when money was based on gold

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Speaker 1: or silver and it was finite. Right, there was a

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Speaker 1: fixed amount of gold and silver in the world in

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Speaker 1: those days. Indeed, if you sat on money, you were

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Speaker 1: effectively reducing the supply of money in the world. But

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Speaker 1: to your point, the supply of money in the world

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Speaker 1: is as big as the Central Bank wants it to be,

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Speaker 1: and so it's not a meaningful constraint on economic growth.

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Speaker 1: So the ultimate answer to the question is, if you

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Speaker 1: are just sitting on money, it's not really going to

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Speaker 1: have an effect on the broader economy.

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Speaker 2: Well, I think we've come to agreement. Hopefully that makes

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Speaker 2: sense to Adam. Next question.

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Speaker 1: Our next question comes from doctor Yvonne Couch, who is

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Speaker 1: Associate Professor of Neuroimmunology, Alzheimer's Research UK Fellow, Associate Research

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Speaker 1: Fellow at Saint Hilda's College, stipendary lecturer at Somerville College,

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Speaker 1: University of Oxford.

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Speaker 2: Yeah, University of Oxford. I know, well, one of my

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Speaker 2: sisters went to Hilldess College and one of my sisters

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Speaker 2: went to Somerville College, so this feels very much in

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Speaker 2: the family. So what does doctor Couch got just say?

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Speaker 1: She writes, my question was vaguely economics based, although probably

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Speaker 1: not very is the way we currently fund science feasible

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Speaker 1: going forward? Do we just have too many scientists and

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Speaker 1: not enough resources?

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Speaker 2: All the best, Yvonne, Oh love it. Let me throw

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Speaker 2: out a few thoughts. Then I think that probably there

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Speaker 2: is a problem with the way we fund science. But

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Speaker 2: there's no one way that we fund science. You have

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Speaker 2: university research, you have various grants, various sources of funding,

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Speaker 2: philanthropy and so on. You also have private sector research,

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Speaker 2: which is often incentivized by the patent system. And then

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Speaker 2: you have big block grants that are handed out by

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Speaker 2: agencies such as the National Institutes for Health. So there

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Speaker 2: are lots of different ways that science gets funded. A

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Speaker 2: couple of things that worry me is that, first of all,

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Speaker 2: there's a big incentive to make really incremental improvements rather than.

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Speaker 4: To take risks.

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Speaker 2: There's a great economics paper studying scientists who are funded

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Speaker 2: by the National Institutes for Health, which is a wonderful institution,

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Speaker 2: very important, and scientists who on paper seem to be

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Speaker 2: the same that they're on the same career tracks, got

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Speaker 2: very similar publication records, and they are instead funded by

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Speaker 2: a private foundation called the Howard Hughes Medical Institute. Listeners

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Speaker 2: who want to hear more about Howard Hughes and the

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Speaker 2: Howard Hughes biography can go to the back catalog of

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Speaker 2: caution details. So the Howard Hughes Medical Institute basically takes risks.

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Speaker 2: It wants people to do something new. It's happy with

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Speaker 2: a high risk of failure as long as there's some

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Speaker 2: chance of a real breakthrough success. And this particular paper

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Speaker 2: studying the results that come from these two funding systems

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Speaker 2: basically finds that the grant funders get what they pay for.

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Speaker 2: So the National Institutes for Health get a high success rate,

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Speaker 2: but it's often quite incremental progress. And the Howard Hughes

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Speaker 2: Medical Institute has lots and lots of failures, but when

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Speaker 2: it succeeds, it really succeeds. And they these are hugely

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Speaker 2: important papers, and I just feel that we're not deliberate

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Speaker 2: enough about saying, well, how much of our funding ecosystem

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Speaker 2: should be aiming at kind of venture capital style for

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Speaker 2: really big wins, and how much should be incremental. I

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Speaker 2: think those are not the sorts of questions that get asked,

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Speaker 2: So that's one of the things that worries me.

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Speaker 4: But what's your take, Jacob.

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Speaker 1: One of the things that I have read is that

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Speaker 1: over time, the average age of grant recipients from the

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Speaker 1: NIH has gone up and up and up, so kind

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Speaker 1: of you just think of this whole universe as getting

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Speaker 1: older and more risk averse and more kind of bureaucratic.

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Speaker 1: There is an interesting set of counter pressures I think

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Speaker 1: rising up partly out of Silicon Valley. I don't know

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Speaker 1: if you've come across the work of Patrick Hollison and

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Speaker 1: his brother. They're from Ireland. They found it Stripe. They're

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Speaker 1: very rich. Stripe is a big company that does basically

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Speaker 1: payment stuff online. They have a really interesting set of

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Speaker 1: kind of philanthropic endeavors around the idea of progress. You know,

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Speaker 1: they're trying to create a kind of field of progress studies.

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Speaker 1: That is, it's very meta. What are the conditions that

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Speaker 1: best foster technological and scientific progress?

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Speaker 2: But this question from doctor Couch is very massive. Yes,

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Speaker 2: it's very much progress stud is, isn't it.

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Speaker 1: Yes. There's this institute in the Bay Area called the

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Speaker 1: Arc Institute that hires leading scientists. The basic idea is

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Speaker 1: give talented people freedom and money, right and encourage them

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Speaker 1: to take big swings. And they are interested, you know,

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Speaker 1: not just in outcomes but in new tools. Right again,

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Speaker 1: continuing on the nerdy thing, like if you take a

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Speaker 1: tool like Crisper. Crisper is an intermediate tool that allows

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Speaker 1: people to cut up a genome. Basically, we just had

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Speaker 1: the first transplant from an animal into a human a

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Speaker 1: few weeks ago because of Crisper. We have the first

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Speaker 1: treatments for hicle cell disease because of Crisper's. So I

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Speaker 1: do think there is a wave of people trying to

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Speaker 1: rethink scientific funding. There is a bottom line aspect to

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Speaker 1: this question from doctor Couch that I don't feel like

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Speaker 1: I know enough to answer, and I'm curious if you do.

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Speaker 1: I mean, the question, is the way we currently fund

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Speaker 1: science feasible going forward? There is a yes no version

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Speaker 1: of the answer. Do you think you know? Is it?

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Speaker 2: I don't know the answer, but I don't know either.

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Speaker 2: Another thing that concerns me on this, which is potentially

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Speaker 2: really existential, is the question of how we fund new antibiotics.

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Speaker 1: Huh.

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Speaker 2: I think this points to a real weakness in the

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Speaker 2: ecosystem of research funding. So if you think about the

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Speaker 2: basic way we develop drugs, the fundamental incentive is the patent.

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Speaker 2: So a drug company spends a lot of money, tries

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Speaker 2: to develop drugs. Some of them work, some of them fail,

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Speaker 2: a lot of them fail. But in the end you

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Speaker 2: have a drug, and the deal is give you this

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Speaker 2: artificial monopoly called a patent, and it will only last

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Speaker 2: for a certain amount of time, which is kind of

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Speaker 2: a problem because it takes so long to develop the drugs.

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Speaker 2: Maybe the patent's nearly expired. You can charge an incredible

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Speaker 2: amount for these drugs for a while, and then your

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Speaker 2: patterents will run out, and then loads of its people

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Speaker 2: will make the same drug and it'll come down in value.

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Speaker 2: For example, Viagra, you know you could sell this for

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Speaker 2: a huge amount of money, and now Viagra is off

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Speaker 2: patent and anyone can make a generic Viagra. So that's

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Speaker 2: the incentive that we're given to private companies that you

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Speaker 2: will have this temporary monopoly. So now think about antibiotics.

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Speaker 2: The problem with antibiotics. We have lots of antibiotics that

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Speaker 2: work really well, except the bacteria have figured them out.

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Speaker 2: And why have they figured them out Because we've used them.

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Speaker 2: The bacteria developed resistance. So what we really need is

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Speaker 2: new antibiotics that we don't use. And now think about

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Speaker 2: how the patent system deals with that. So you basically say,

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Speaker 2: if you develop a new antibiotic, we'll give you a

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Speaker 2: temporary minio, but we really need you to just not

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Speaker 2: sell any don't sell any of this things except.

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Speaker 1: In cases of dire emergency. It's like a break glass

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Speaker 1: in case of emergency. I mean, I feel like a bounty,

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Speaker 1: Like every EuCon nerd storyteller loves a good bounty.

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Speaker 3: Right.

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Speaker 1: Wasn't this done with malaria vaccine, which is actually coming

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Speaker 1: along quite well? You have some universe of people say

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Speaker 1: we will pay a billion dollars to anyone who comes

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Speaker 1: up with a new antibiotic that meets this set of

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Speaker 1: criteria that treats this set of bugs that are resistant

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Speaker 1: to these existing antibiotics, and we'll give you the money,

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Speaker 1: and you give it to us, and we'll put it

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Speaker 1: on the break glass in case of emergency shelf.

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Speaker 2: You've used the word bounty, but what the term that

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Speaker 2: is normally used is an advanced market commitment. These were

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Speaker 2: proposed most famously by Michael Kramer, who's a Nobel Prize

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Speaker 2: winner in economics. And basically the way this price, this

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Speaker 2: bounty tends to get paid is as a kind of

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Speaker 2: extra payment on top of each dose you sell. Huh,

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Speaker 2: So we'll give you extra for every kid that gets

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Speaker 2: vaccinated so.

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Speaker 1: We're back to the same problem in that universe.

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Speaker 2: Back to the same problem. But the reason we go, well,

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Speaker 2: why does it have to be like that? It doesn't

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Speaker 2: have to be like that. But the reason that they

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Speaker 2: tend to be structured like that is because you need

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Speaker 2: to demonstrate some kind of market demand. Somebody needs to

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Speaker 2: be willing to buy your product. If they are, we'll

435
00:23:26,050 --> 00:23:28,610
Speaker 2: give you an extra payment for every product you sell.

436
00:23:29,050 --> 00:23:31,610
Speaker 2: But that wouldn't work for antibiotics. There's so many different

437
00:23:31,610 --> 00:23:33,890
Speaker 2: ways in which science funding could be said to be broken.

438
00:23:34,330 --> 00:23:38,130
Speaker 2: But I think doctor Couch is right to raise the issue.

439
00:23:38,250 --> 00:23:40,090
Speaker 2: We need to do a lot more of this kind

440
00:23:40,130 --> 00:23:42,610
Speaker 2: of meta thinking about progress studies.

441
00:23:42,890 --> 00:23:45,370
Speaker 1: Tim, that was a lot of answer. Let's take a break.

442
00:23:45,650 --> 00:23:56,290
Speaker 1: Cautionary Tales will be back in a moment. Edward back.

443
00:23:56,530 --> 00:23:59,490
Speaker 1: I'm Jacob Goldstein here with Tim Harford on Cautionary Tales.

444
00:23:59,570 --> 00:24:02,410
Speaker 2: Hello, Jacob, more questions. What have you got for me?

445
00:24:02,850 --> 00:24:06,210
Speaker 1: All right, Tim, I got another one for you. Comes

446
00:24:06,210 --> 00:24:12,850
Speaker 1: from Graham in Florida. Gram Rights. I'm fascinated by errors

447
00:24:13,010 --> 00:24:18,330
Speaker 1: made by falsely identifying correlation as causation. What examples of

448
00:24:18,370 --> 00:24:19,730
Speaker 1: this error stand out to you?

449
00:24:20,770 --> 00:24:23,090
Speaker 2: I love the question, but I'm going to slightly sidestep

450
00:24:23,130 --> 00:24:25,690
Speaker 2: it because I'm worried by this question, but I think

451
00:24:25,730 --> 00:24:32,250
Speaker 2: that it's generally more complicated than simply, oh, some fool

452
00:24:32,690 --> 00:24:34,810
Speaker 2: sourer correlation and thought it was causation.

453
00:24:35,250 --> 00:24:38,610
Speaker 1: You don't want to talk about sun spots and crap yields, Well,

454
00:24:38,690 --> 00:24:42,050
Speaker 1: let's talk about storks and babies briefly, Okay. A classic,

455
00:24:42,090 --> 00:24:43,210
Speaker 1: A classic of the genre.

456
00:24:43,370 --> 00:24:48,090
Speaker 2: Yeah, the most successful book ever published about statistics, How

457
00:24:48,130 --> 00:24:51,330
Speaker 2: to Lie with Statistics by Darryl Huff, includes this example

458
00:24:51,330 --> 00:24:54,450
Speaker 2: of stalks and babies and shows that there's a correlation

459
00:24:54,650 --> 00:24:57,290
Speaker 2: between the number of storks and the number of babies.

460
00:24:57,330 --> 00:24:59,930
Speaker 2: And there are various ways to demonstrate this correlation. One

461
00:24:59,930 --> 00:25:02,530
Speaker 2: way to do it is you just look at national populations.

462
00:25:02,770 --> 00:25:05,050
Speaker 2: You go, hey, countries with lots of storks also have

463
00:25:05,090 --> 00:25:07,370
Speaker 2: lots of babies, and there's a very very strong correlation.

464
00:25:07,850 --> 00:25:10,370
Speaker 2: But of course the reason is big places like the

465
00:25:10,490 --> 00:25:12,890
Speaker 2: United States have a lot of room for storks and

466
00:25:12,930 --> 00:25:15,570
Speaker 2: a lot of room for babies, and small places like

467
00:25:15,610 --> 00:25:17,970
Speaker 2: in a Luxembourg or the Vatican City don't have a

468
00:25:18,010 --> 00:25:19,210
Speaker 2: lot of room for babies and don't have a lot

469
00:25:19,210 --> 00:25:20,970
Speaker 2: of room for storks. So then you go, oh, that's

470
00:25:20,970 --> 00:25:24,370
Speaker 2: a great example of this mistake. The sting in the

471
00:25:24,410 --> 00:25:27,370
Speaker 2: tale of that story, as I describe in my book

472
00:25:27,450 --> 00:25:30,290
Speaker 2: The Data Detective, is that Darryl Huff, the guy who

473
00:25:30,530 --> 00:25:34,170
Speaker 2: created that story, then went on to tell the same

474
00:25:34,210 --> 00:25:40,370
Speaker 2: story in congressional testimony, saying that there was no compelling

475
00:25:40,410 --> 00:25:43,930
Speaker 2: evidence that smoking cigarettes would give you lung cancer, and

476
00:25:43,970 --> 00:25:45,770
Speaker 2: it was just like stalks and babies. And he was

477
00:25:45,770 --> 00:25:47,570
Speaker 2: actually hired by the tobacco lobby.

478
00:25:47,730 --> 00:25:48,890
Speaker 1: It was just correlation.

479
00:25:48,930 --> 00:25:52,930
Speaker 2: It was just correlational. They seemed about the same. Sure, yeah,

480
00:25:52,930 --> 00:25:54,930
Speaker 2: there are people who smoke, and that people who get cancer,

481
00:25:54,970 --> 00:25:58,330
Speaker 2: but there's no cause of evidence. One theory was cigarettes

482
00:25:58,330 --> 00:26:01,290
Speaker 2: are so soothing, and if you have some early symptoms

483
00:26:01,290 --> 00:26:03,890
Speaker 2: of lung cancer, maybe you soothed that with the soothing

484
00:26:04,170 --> 00:26:06,850
Speaker 2: vapors of cigarettes. I mean, it's completely ridiculous, but this

485
00:26:06,930 --> 00:26:09,530
Speaker 2: sort of rhetoric was deployed. And so one of the

486
00:26:09,570 --> 00:26:12,370
Speaker 2: things I'm very concerned about in my work on statistics

487
00:26:12,570 --> 00:26:16,090
Speaker 2: is that it's great to be skeptical about statistics and

488
00:26:16,130 --> 00:26:19,050
Speaker 2: to point out lots of examples of statistics being misused.

489
00:26:19,050 --> 00:26:21,690
Speaker 2: But if that's all you do, you just get to

490
00:26:22,290 --> 00:26:26,970
Speaker 2: a canihilistic situation where you're just constantly rejecting statistical evidence

491
00:26:26,970 --> 00:26:29,650
Speaker 2: because it's just another of those dawn lines and statistics

492
00:26:29,730 --> 00:26:32,450
Speaker 2: when you look at the real world, I think this

493
00:26:32,570 --> 00:26:36,330
Speaker 2: gets to be incredibly interesting. So a real hot topic

494
00:26:36,370 --> 00:26:41,330
Speaker 2: at the moment is our smartphones destroying a generation. Basically,

495
00:26:41,490 --> 00:26:43,610
Speaker 2: are our kids having their mental health wrecked.

496
00:26:43,810 --> 00:26:46,530
Speaker 1: There's a new book that basically makes that up, Yeah,

497
00:26:46,530 --> 00:26:50,090
Speaker 1: by prominent ecademic Yea by Jonathan Hyde, and there have

498
00:26:50,090 --> 00:26:51,890
Speaker 1: been others by Gene Twegi, and lots of people have

499
00:26:51,930 --> 00:26:57,610
Speaker 1: said this, and the evidence for it is mostly correlational,

500
00:26:57,850 --> 00:26:58,450
Speaker 1: not completely.

501
00:26:58,450 --> 00:27:02,010
Speaker 2: There are some experiments, but they're not that convincing. None

502
00:27:02,050 --> 00:27:03,770
Speaker 2: of them are perfect, but there are lots of different

503
00:27:03,810 --> 00:27:08,490
Speaker 2: ways of measuring it. And the really interesting evidence basically says, look,

504
00:27:09,170 --> 00:27:11,690
Speaker 2: there appears to be a mental health crisis. The kids

505
00:27:11,730 --> 00:27:16,050
Speaker 2: seem to get really distressed, particularly the girls, when children

506
00:27:16,210 --> 00:27:19,850
Speaker 2: have access to social media on their phones, and that

507
00:27:19,890 --> 00:27:23,330
Speaker 2: happens around sometime between twenty ten and twenty fourteen, and

508
00:27:23,530 --> 00:27:26,050
Speaker 2: at the same time, suddenly you've got this outbreak of

509
00:27:26,450 --> 00:27:30,450
Speaker 2: suicidal ideation, self harm, poor mental health, and so on.

510
00:27:30,810 --> 00:27:34,850
Speaker 2: And that's correlational evidence. I don't completely believe it, But

511
00:27:34,890 --> 00:27:36,890
Speaker 2: I don't completely not believe it either. I think that's

512
00:27:36,930 --> 00:27:39,890
Speaker 2: what makes it interesting and makes it important to engage with.

513
00:27:40,210 --> 00:27:42,450
Speaker 2: You have to start going, well, what's the alternative explanation?

514
00:27:42,570 --> 00:27:45,410
Speaker 2: Is there something else that has happened sometime about sort

515
00:27:45,450 --> 00:27:49,010
Speaker 2: of ten, twelve, fourteen years ago that might explain this

516
00:27:49,130 --> 00:27:51,250
Speaker 2: mental health distress? So the timing of the Great Financial

517
00:27:51,290 --> 00:27:54,690
Speaker 2: Crisis probably not quite right Donald Trump. Maybe Donald Trump

518
00:27:54,730 --> 00:27:58,570
Speaker 2: is upsetting the kids. Timing doesn't work, so partly does

519
00:27:58,610 --> 00:28:02,570
Speaker 2: the pan of the correlation make enough sense to explain

520
00:28:02,610 --> 00:28:05,490
Speaker 2: this causal story, which, funny enough, is basically exactly what

521
00:28:05,530 --> 00:28:09,610
Speaker 2: the scientists finding a connection between cigarettes and lunghuans were doing.

522
00:28:09,770 --> 00:28:12,130
Speaker 2: They only had correlational evidence for a long time. They

523
00:28:12,170 --> 00:28:15,050
Speaker 2: weren't running randomized trials saying you know, half of you

524
00:28:15,130 --> 00:28:16,850
Speaker 2: smoke and half of you don't smoke. I mean, they

525
00:28:16,850 --> 00:28:19,450
Speaker 2: couldn't do that. They had to look at correlational evidence,

526
00:28:19,490 --> 00:28:21,290
Speaker 2: and sometimes that's what we've got.

527
00:28:21,770 --> 00:28:25,370
Speaker 1: This question got me thinking about the rise in social

528
00:28:25,450 --> 00:28:29,650
Speaker 1: science of what they call natural experiments, right, trying to

529
00:28:29,730 --> 00:28:34,050
Speaker 1: find instances in the real world where you have something

530
00:28:34,370 --> 00:28:36,650
Speaker 1: that obviously is not as good as a randomized trial,

531
00:28:36,690 --> 00:28:38,890
Speaker 1: because you just can't get that with large numbers of

532
00:28:38,890 --> 00:28:41,970
Speaker 1: people in the world. But that gives you some element

533
00:28:42,250 --> 00:28:46,530
Speaker 1: of randomization or pseudo randomization, something that allows you to

534
00:28:46,690 --> 00:28:50,330
Speaker 1: try and make the leap from correlation to causation. And

535
00:28:50,410 --> 00:28:52,290
Speaker 1: you know, this, as far as I know, goes back

536
00:28:52,290 --> 00:28:55,250
Speaker 1: to the Vietnam War, right, where in the US there

537
00:28:55,370 --> 00:28:58,810
Speaker 1: was a draft lottery, and there were social scientists after

538
00:28:59,250 --> 00:29:01,930
Speaker 1: the war who looked and said, oh, look, here are

539
00:29:01,930 --> 00:29:05,250
Speaker 1: people who are at an aggregate level very similar on

540
00:29:05,330 --> 00:29:09,890
Speaker 1: many dimensions, socioeconomic dimensions. We can look at people who

541
00:29:09,970 --> 00:29:13,610
Speaker 1: randomly got drafted versus those who randomly didn't and who

542
00:29:13,690 --> 00:29:16,530
Speaker 1: appear quite similar in the aggregate, and see how their

543
00:29:16,530 --> 00:29:17,370
Speaker 1: outcomes differ.

544
00:29:17,530 --> 00:29:20,610
Speaker 2: And that's a really good example of a natural experiment

545
00:29:20,610 --> 00:29:22,690
Speaker 2: because it's actually very close to a really.

546
00:29:22,610 --> 00:29:25,330
Speaker 1: Yeah, when you get a lottery in the real world, now,

547
00:29:25,450 --> 00:29:27,090
Speaker 1: social scientists flock to it.

548
00:29:27,210 --> 00:29:27,370
Speaker 3: Right.

549
00:29:27,410 --> 00:29:32,370
Speaker 1: Similarly, there was one in Oregon some years ago with Medicaid,

550
00:29:32,370 --> 00:29:35,930
Speaker 1: which is the healthcare program for low income people in

551
00:29:35,970 --> 00:29:39,330
Speaker 1: the US, and Oregon got some new Medicaid funds and

552
00:29:39,370 --> 00:29:43,530
Speaker 1: they randomly allocated them to a group of people over time, Right,

553
00:29:43,610 --> 00:29:46,010
Speaker 1: So that social scientists could say, oh, look, here are

554
00:29:46,050 --> 00:29:48,610
Speaker 1: people who are basically identical. Some of them got this

555
00:29:48,810 --> 00:29:50,570
Speaker 1: health insurance and some of them didn't, and in that

556
00:29:50,690 --> 00:29:53,890
Speaker 1: case the findings were quite interesting. Medicaid didn't appear to

557
00:29:53,930 --> 00:29:56,930
Speaker 1: be as helpful as I would have thought, as the

558
00:29:56,970 --> 00:30:00,490
Speaker 1: researchers themselves would have thought. According to the one I interviewed,

559
00:30:00,730 --> 00:30:04,010
Speaker 1: lowered mental health problems, people worried less about money, but

560
00:30:04,130 --> 00:30:07,890
Speaker 1: like their basic health outcomes didn't improve, which nobody would

561
00:30:07,890 --> 00:30:10,810
Speaker 1: have guessed right. And the the evidence is quite robust

562
00:30:11,290 --> 00:30:14,010
Speaker 1: when you don't have those lotteries. As you say, the

563
00:30:14,050 --> 00:30:16,730
Speaker 1: world is just hard to understand. I mean, even in

564
00:30:16,770 --> 00:30:20,810
Speaker 1: some instances where you do have randomized trials, people are complicated.

565
00:30:20,810 --> 00:30:24,730
Speaker 1: The body is complicated, society is complicated, and so you know,

566
00:30:24,890 --> 00:30:28,490
Speaker 1: correlation in a certain way I think is underrated. Like, yes,

567
00:30:28,570 --> 00:30:31,450
Speaker 1: obviously it doesn't equal causation, but it's a place to

568
00:30:31,530 --> 00:30:34,330
Speaker 1: start looking, right, It's a place to start asking questions.

569
00:30:34,370 --> 00:30:37,130
Speaker 2: I think that's absolutely right. And we need more randomized

570
00:30:37,130 --> 00:30:40,370
Speaker 2: experiments that there are more opportunities to run them than

571
00:30:40,410 --> 00:30:42,250
Speaker 2: people seem to think. For example, one of the things

572
00:30:42,290 --> 00:30:44,530
Speaker 2: that John Hyde in his book is arguing for is

573
00:30:44,610 --> 00:30:47,850
Speaker 2: you know, shouldn't have smartphones in schools. Schools should be

574
00:30:48,050 --> 00:30:51,690
Speaker 2: phone free zones. I could completely imagine a state saying

575
00:30:51,930 --> 00:30:54,650
Speaker 2: we're going to introduce a rule whereby in all of

576
00:30:54,690 --> 00:30:58,970
Speaker 2: the schools in the state, no smartphones strictly forbidden. You

577
00:30:59,010 --> 00:31:00,450
Speaker 2: have to put them in a locker when you show

578
00:31:00,530 --> 00:31:02,250
Speaker 2: up and then unlock them at the end of the day.

579
00:31:02,610 --> 00:31:04,490
Speaker 2: You could introduce that rule and just go, well, we know,

580
00:31:04,570 --> 00:31:08,890
Speaker 2: introduce it for a semester at random in fifty percent

581
00:31:08,930 --> 00:31:11,290
Speaker 2: of the schools, and then we'll introduce it in the

582
00:31:11,290 --> 00:31:14,050
Speaker 2: other fifty percent of the schools in the next semester,

583
00:31:14,130 --> 00:31:16,130
Speaker 2: and we'll just randomize that because we want to know

584
00:31:16,130 --> 00:31:19,090
Speaker 2: whether there's any point in this experiment or not. That's

585
00:31:19,170 --> 00:31:22,090
Speaker 2: not very difficult to do. It would create so much

586
00:31:22,170 --> 00:31:26,810
Speaker 2: information about children's performance in the classroom, their mental health

587
00:31:27,130 --> 00:31:29,050
Speaker 2: that could inform policy across the world.

588
00:31:29,370 --> 00:31:32,210
Speaker 1: That would be the dream. You know, there is now

589
00:31:32,250 --> 00:31:36,890
Speaker 1: a robust set of methods essentially where social scientists could

590
00:31:36,890 --> 00:31:39,730
Speaker 1: look at that state and compare it to neighboring states

591
00:31:39,810 --> 00:31:42,690
Speaker 1: and see the difference in differences, as they say, see

592
00:31:42,690 --> 00:31:45,970
Speaker 1: the change over time. And that's an instance where it

593
00:31:45,970 --> 00:31:49,250
Speaker 1: wouldn't be as elegant as randomizing within a state. But

594
00:31:49,290 --> 00:31:51,010
Speaker 1: I feel like you could start to get pretty good

595
00:31:51,090 --> 00:31:54,970
Speaker 1: data even if you did one state compared to other states.

596
00:31:55,290 --> 00:31:58,130
Speaker 2: Yeah, if you've got no experiments, we should run them.

597
00:31:58,210 --> 00:32:00,370
Speaker 2: If we don't run them, there are still ways of

598
00:32:00,450 --> 00:32:01,450
Speaker 2: making correlation.

599
00:32:01,730 --> 00:32:01,970
Speaker 4: Talk.

600
00:32:03,410 --> 00:32:04,370
Speaker 2: Do we have another question?

601
00:32:05,450 --> 00:32:07,850
Speaker 1: Okay, Tim, we are one more. We had to have

602
00:32:08,010 --> 00:32:12,530
Speaker 1: one game related question for great Yes, you're welcome. This

603
00:32:12,530 --> 00:32:17,250
Speaker 1: one comes from Yost, who writes, Hi, Tim, diving into

604
00:32:17,290 --> 00:32:21,050
Speaker 1: the board game shaped flank you've now exposed for discussion.

605
00:32:21,330 --> 00:32:25,090
Speaker 1: That is a very gamersh way in what board games

606
00:32:25,210 --> 00:32:28,730
Speaker 1: or board game mechanisms that you enjoy are particularly insightful

607
00:32:28,850 --> 00:32:32,770
Speaker 1: on some aspect of real world economics offer a unique

608
00:32:32,810 --> 00:32:37,010
Speaker 1: angle to look at a maybe niche problem. And what

609
00:32:37,290 --> 00:32:40,730
Speaker 1: non D and D games are you particularly enjoying right now?

610
00:32:41,450 --> 00:32:45,450
Speaker 1: Love the show, however many episodes you produce that is

611
00:32:45,490 --> 00:32:47,690
Speaker 1: a listener We all want thank you, yos.

612
00:32:47,770 --> 00:32:50,410
Speaker 2: Yeah, so kind your There are a couple of mechanisms

613
00:32:50,450 --> 00:32:53,570
Speaker 2: that I do see used quite often that shed important

614
00:32:53,610 --> 00:32:56,450
Speaker 2: light to economics. So one is trading a lot of

615
00:32:56,490 --> 00:33:00,930
Speaker 2: games involved trading. Now Monopoly, that classic, of sadly not

616
00:33:01,050 --> 00:33:03,570
Speaker 2: a very good game. Oh, there's a caution we tell

617
00:33:03,610 --> 00:33:05,570
Speaker 2: about the history of Monopoly if people want to listen.

618
00:33:06,410 --> 00:33:09,970
Speaker 2: Monopoly in theory involves trading, but in practice, not a

619
00:33:09,970 --> 00:33:13,930
Speaker 2: lot of trading happens. It turns out that you need

620
00:33:13,970 --> 00:33:17,330
Speaker 2: to give people a reason to trade. The great modern

621
00:33:17,330 --> 00:33:19,530
Speaker 2: game Settlers, now about thirty years old.

622
00:33:19,610 --> 00:33:22,050
Speaker 1: By the way, Settlers, I assume that Settlers of Catan

623
00:33:22,250 --> 00:33:25,170
Speaker 1: as an outsider, not on a first name basis.

624
00:33:24,810 --> 00:33:25,250
Speaker 3: With the game.

625
00:33:25,410 --> 00:33:28,170
Speaker 2: Settlers of Katan is the game. It's super It's the

626
00:33:28,170 --> 00:33:31,250
Speaker 2: game that Monopoly wishes it was. It's like if Monopoly

627
00:33:31,330 --> 00:33:35,410
Speaker 2: had been designed with a modern eye. To be super exciting.

628
00:33:35,890 --> 00:33:40,690
Speaker 2: The people need different resources, and the supply of resources fluctuates,

629
00:33:40,770 --> 00:33:43,290
Speaker 2: so sometimes you've got loads of wood, sometimes there's no wood,

630
00:33:43,490 --> 00:33:45,690
Speaker 2: so there's an active incentive to trade all the time.

631
00:33:45,890 --> 00:33:47,210
Speaker 2: Oh and by the way, if you don't trade every

632
00:33:47,250 --> 00:33:49,050
Speaker 2: now and then the robber Barron comes and take stuff.

633
00:33:49,170 --> 00:33:52,050
Speaker 2: So it's an interesting insight that trading doesn't just happen

634
00:33:52,530 --> 00:33:55,290
Speaker 2: because it's allowed. There needs to be some difference in

635
00:33:55,370 --> 00:33:57,210
Speaker 2: value and perhaps some incentive.

636
00:33:56,730 --> 00:33:57,450
Speaker 4: To get on with it.

637
00:33:58,330 --> 00:34:02,450
Speaker 2: So my personal favorite game is Agricola, a wonderful game

638
00:34:02,490 --> 00:34:05,410
Speaker 2: about developing a farm, which doesn't sound promising, but it's

639
00:34:05,450 --> 00:34:08,010
Speaker 2: really really good. But one of the clever things about

640
00:34:08,010 --> 00:34:11,610
Speaker 2: agricola is it uses an auction in an interesting way,

641
00:34:12,090 --> 00:34:14,170
Speaker 2: but it's sometimes called a descending.

642
00:34:13,770 --> 00:34:15,610
Speaker 4: Clock auction or a Dutch auction.

643
00:34:16,210 --> 00:34:20,130
Speaker 2: The prize gets more and more tempting, so in a

644
00:34:20,170 --> 00:34:22,610
Speaker 2: gricola there's just more and more good stuff on the board.

645
00:34:22,930 --> 00:34:25,890
Speaker 2: In a traditional descending clock auction, basically the price is

646
00:34:25,930 --> 00:34:28,090
Speaker 2: going down and down and down and down, and everyone

647
00:34:28,130 --> 00:34:29,850
Speaker 2: is just sitting there and then it's a question of

648
00:34:29,930 --> 00:34:32,490
Speaker 2: who grabs it first. The longer you leave it, the

649
00:34:32,530 --> 00:34:35,610
Speaker 2: better it gets. But then only one person can have

650
00:34:35,690 --> 00:34:38,850
Speaker 2: it if very very elegant. They sell flowers at Alsmere

651
00:34:39,090 --> 00:34:42,810
Speaker 2: in the Netherlands and it's just incredibly quick, much much

652
00:34:42,850 --> 00:34:45,130
Speaker 2: quicker than selling say art. You know you're selling a

653
00:34:45,210 --> 00:34:47,530
Speaker 2: van goal hah No. The price rises and rises and rises.

654
00:34:47,610 --> 00:34:50,090
Speaker 1: So auction design is a whole thing, right, Yeah. When

655
00:34:50,090 --> 00:34:53,810
Speaker 1: would you choose a traditional prices going up eBay art

656
00:34:53,850 --> 00:34:56,570
Speaker 1: style auction in the real world, and when would you

657
00:34:56,650 --> 00:34:59,370
Speaker 1: choose a Dutch prices going down auction? What do you

658
00:34:59,450 --> 00:35:01,570
Speaker 1: get in a relative sense out of each one of those?

659
00:35:01,610 --> 00:35:05,330
Speaker 2: There's a couple of differences. One is that in certain

660
00:35:05,450 --> 00:35:09,490
Speaker 2: types of ascending auction you get to see people dropping

661
00:35:09,530 --> 00:35:11,490
Speaker 2: out as the price rises. It depends on the way

662
00:35:11,530 --> 00:35:14,210
Speaker 2: the auction is designed, but you could imagine an ascending

663
00:35:14,250 --> 00:35:16,490
Speaker 2: auction where you just see people going no, I'm out,

664
00:35:16,570 --> 00:35:17,610
Speaker 2: I'm out, I'm out.

665
00:35:17,930 --> 00:35:20,850
Speaker 1: Sort of the classic kind of art auction in a movie.

666
00:35:21,050 --> 00:35:23,650
Speaker 2: Yeah, and so you're learning information as the price rises.

667
00:35:23,650 --> 00:35:25,370
Speaker 2: How many people are still interested? Are there still ten

668
00:35:25,370 --> 00:35:28,850
Speaker 2: people interested? Is it just two people? So that generates information.

669
00:35:28,970 --> 00:35:32,770
Speaker 2: By generating information, it might raise the final price.

670
00:35:32,890 --> 00:35:33,450
Speaker 4: Overall.

671
00:35:34,010 --> 00:35:37,370
Speaker 2: The advantage of the descending auction is it's just so quick.

672
00:35:37,610 --> 00:35:41,170
Speaker 2: You can run an auction in ten seconds. It's one

673
00:35:41,250 --> 00:35:43,650
Speaker 2: hundred thousand tulips. Here are the tulips. We're going to

674
00:35:43,690 --> 00:35:49,370
Speaker 2: sell them for whatever, ten thousand euros nine eight hundred thousand,

675
00:35:49,370 --> 00:35:52,730
Speaker 2: and it's literally a clock. The clock just runs around

676
00:35:52,970 --> 00:35:56,570
Speaker 2: showing what the price is, and then someone presses the button. Sold, okay,

677
00:35:57,490 --> 00:35:59,570
Speaker 2: now bringing the roses. So it's very fast.

678
00:35:59,730 --> 00:36:01,610
Speaker 1: You would want to have a good idea of the

679
00:36:01,690 --> 00:36:03,610
Speaker 1: market clearing price if you were going to run that

680
00:36:03,770 --> 00:36:05,570
Speaker 1: kind of auction, so that you didn't sell it for

681
00:36:05,650 --> 00:36:08,410
Speaker 1: too cheap. Right, it's fast, but you run the risk

682
00:36:08,450 --> 00:36:11,050
Speaker 1: of selling it for cheap. But if it's just commodity toolips,

683
00:36:11,090 --> 00:36:13,330
Speaker 1: then you basically know the market clearing price anyways.

684
00:36:13,450 --> 00:36:15,770
Speaker 2: Yeah, it's almost like a stock market for tulips. That

685
00:36:15,890 --> 00:36:17,810
Speaker 2: I think is why it works so well. Whereas if

686
00:36:17,850 --> 00:36:20,090
Speaker 2: it's a unique work of art, you need to give

687
00:36:20,170 --> 00:36:23,490
Speaker 2: maximum information, maximum comfort to people, maybe a little bit

688
00:36:23,490 --> 00:36:25,770
Speaker 2: of theater as well. We're not in a hurry to

689
00:36:25,850 --> 00:36:28,170
Speaker 2: run this auction. The cost of the auction itself is

690
00:36:28,250 --> 00:36:31,610
Speaker 2: trivial compared to the value of what's being sold.

691
00:36:32,210 --> 00:36:34,530
Speaker 1: Now we go over to agricola and have a descending action.

692
00:36:34,730 --> 00:36:37,970
Speaker 1: It feels exciting and it also feels like you're trying

693
00:36:38,050 --> 00:36:40,530
Speaker 1: to guess what is in your opponent's head, right, because

694
00:36:40,530 --> 00:36:43,930
Speaker 1: they're not revealing it as they would in ascending price auction.

695
00:36:44,090 --> 00:36:45,930
Speaker 1: You have to think, oh, what are they willing to

696
00:36:46,010 --> 00:36:46,450
Speaker 1: pay for it?

697
00:36:46,570 --> 00:36:49,450
Speaker 2: Absolutely, and it works slightly differently, but the functionally what's

698
00:36:49,530 --> 00:36:52,970
Speaker 2: going on is that every turn you take you get

699
00:36:53,010 --> 00:36:55,490
Speaker 2: to grab some resource on the board, and then once

700
00:36:55,570 --> 00:36:57,610
Speaker 2: you've grabbed the resource on the board, no one else

701
00:36:57,730 --> 00:37:00,130
Speaker 2: is allowed to grab it until the next round. And

702
00:37:00,330 --> 00:37:02,330
Speaker 2: there are several moves you can make in each turn,

703
00:37:02,490 --> 00:37:03,930
Speaker 2: so you might be able to get two or three

704
00:37:04,010 --> 00:37:06,970
Speaker 2: things each turn, And so you go backwards and forwards.

705
00:37:07,010 --> 00:37:08,730
Speaker 2: It's your move and it's my move. And each time

706
00:37:08,770 --> 00:37:11,050
Speaker 2: we want to grab something, and you might go, well

707
00:37:11,050 --> 00:37:12,330
Speaker 2: do I want to grab it now? What do I

708
00:37:12,370 --> 00:37:14,930
Speaker 2: want to wait until next turn where it might be better?

709
00:37:14,970 --> 00:37:16,490
Speaker 2: And you're trying to figure out, like, what is the

710
00:37:16,650 --> 00:37:20,490
Speaker 2: thing that the other person is desperate to have? Can

711
00:37:20,570 --> 00:37:24,050
Speaker 2: I just let this pile get bigger because they can't

712
00:37:24,090 --> 00:37:25,530
Speaker 2: afford to take it because they've got to use their

713
00:37:25,570 --> 00:37:28,330
Speaker 2: move on something else. It's a very very clever game.

714
00:37:28,530 --> 00:37:29,250
Speaker 4: I do enjoy it.

715
00:37:29,970 --> 00:37:32,850
Speaker 2: Yours also ask what non D and D games I

716
00:37:33,090 --> 00:37:37,570
Speaker 2: am enjoying. I am enjoying a modified version of Blades

717
00:37:37,610 --> 00:37:41,050
Speaker 2: in the Dark, which's a classic role playing game, very

718
00:37:41,130 --> 00:37:43,930
Speaker 2: fast moving. You get to do Ocean's eleven or other

719
00:37:44,090 --> 00:37:48,170
Speaker 2: kind of heists, and it's very modifiable. We're speaking on

720
00:37:48,250 --> 00:37:51,050
Speaker 2: a Monday. Just yesterday on Sunday, had a whole bunch

721
00:37:51,130 --> 00:37:53,690
Speaker 2: of old friends around to my house and we just

722
00:37:53,730 --> 00:37:57,370
Speaker 2: played this game all day and we had an absolutely

723
00:37:57,410 --> 00:37:57,930
Speaker 2: terrific time.

724
00:37:58,290 --> 00:38:01,090
Speaker 1: I feel like you're living the dream. Harford makes me happy.

725
00:38:00,970 --> 00:38:04,090
Speaker 2: Living my best life. Jacob, this has been such fun.

726
00:38:04,250 --> 00:38:05,330
Speaker 2: Thank you so much for doing this.

727
00:38:05,730 --> 00:38:08,290
Speaker 1: Ah, it was a delight. I'll come back anytime.

728
00:38:09,290 --> 00:38:10,370
Speaker 4: Love to have you back.

729
00:38:11,130 --> 00:38:13,330
Speaker 2: Thank you everybody who sent in a question. Sorry we

730
00:38:13,410 --> 00:38:15,330
Speaker 2: weren't able to answer all the questions, but we are

731
00:38:15,370 --> 00:38:18,050
Speaker 2: going to be back with another Cautionary Questions episode later

732
00:38:18,130 --> 00:38:21,530
Speaker 2: this year. Please do keep your queries coming, send them

733
00:38:21,570 --> 00:38:25,010
Speaker 2: into tales at pushkin dot fm. That's t a l

734
00:38:25,130 --> 00:38:29,170
Speaker 2: e s at Pushkin dot fm and I'll be back

735
00:38:29,410 --> 00:38:33,090
Speaker 2: with another Cautionary Tale in two weeks time. Jacob Goldstein

736
00:38:33,330 --> 00:38:36,010
Speaker 2: has a wonderful podcast called What's Your Problem?

737
00:38:36,170 --> 00:38:37,450
Speaker 4: Thank you Jacob, Thanks Je.

738
00:38:45,650 --> 00:38:49,130
Speaker 2: Cautionary Tales is written by me, Tim Harford with Andrew Wright.

739
00:38:49,610 --> 00:38:52,890
Speaker 2: It's produced by Alice Fines with support from Marilyn Rust.

740
00:38:53,410 --> 00:38:55,930
Speaker 2: The sound design and original music is the work of

741
00:38:56,050 --> 00:39:00,890
Speaker 2: Pascal Wise. Sarah Nix edited the scripts. It features the

742
00:39:00,970 --> 00:39:05,730
Speaker 2: voice talents of Ben Crow, Melanie Guttridge, Stella Harford, Jamma Saunders,

743
00:39:05,850 --> 00:39:09,570
Speaker 2: and Rufus Wright. The show also wouldn't have been possible

744
00:39:09,610 --> 00:39:13,530
Speaker 2: without the work of Jacob Weisberg, Ryan Dilly, Gretta Cohne,

745
00:39:14,010 --> 00:39:19,330
Speaker 2: Vital Mollard, John Schnaz. Eric's Handler, Carrie Brody and Christina Sullivan.

746
00:39:20,130 --> 00:39:24,730
Speaker 2: Cautionary Tales is a production of Pushkin Industries. It's recorded

747
00:39:24,770 --> 00:39:28,490
Speaker 2: at Wardoor Studios in London by Tom Berry. If you

748
00:39:28,730 --> 00:39:32,890
Speaker 2: like the show, please remember to share, rate and review,

749
00:39:33,490 --> 00:39:35,610
Speaker 2: tell your friends and if you want to hear the

750
00:39:35,650 --> 00:39:39,090
Speaker 2: show ad free, sign up for Pushkin Plus on the

751
00:39:39,210 --> 00:39:43,210
Speaker 2: show page in Apple Podcasts or at pushkin dot Fm,

752
00:39:43,610 --> 00:39:44,610
Speaker 2: slash plus