# When the Robots Take Over... - Cautionary Questions

An episode of [Cautionary Tales with Tim Harford](https://invisiblenetwork.ai/podcast/1484511465), 24 May 2024, 40 min.

Audio: https://podtrac.com/pts/redirect.mp3/pdrl.fm/18db03/tracking.swap.fm/track/SxlTEPDY7xDg35RXkASs/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/c0ae8c6e-22f0-4e9b-ac1c-ae390037ac53/a4efe84f-d748-4730-98f5-b1770137cb8e/audio.mp3?utm_source=Podcast&in_playlist=7f5a4714-6b10-4ccf-a424-ae390037ac70

Tim Harford is joined by Jacob Goldstein to answer your questions. Does winning the lottery make you unhappy? Is Bitcoin bad for the economy? When does correlation imply causation? And what will Tim and Jacob do when the robot overlords come for their jobs? We love hearing from you, so please keep your questions coming: tales@pushkin.fm. See omnystudio.com/listener for privacy information.

## Transcript

[0:15] Speaker 1: Pushkin let's play around Warders Studios. It sounds very elvin.
[0:25] Speaker 2: I always think of David Bowie when I think of Wardog.
[0:28] Speaker 2: In one of his early songs talks about right light,
[0:30] Speaker 2: soho wardour Street.
[0:32] Speaker 1: Bowie himself has an elfin aspect.
[0:36] Speaker 2: He said he does is break through gig at Aylesbury,
[0:39] Speaker 2: my hometown, which basically is about the only thing remarking
[0:43] Speaker 2: about Aylesbury is.
[0:44] Speaker 1: Well and also home of Tim Harford.
[0:46] Speaker 3: As the plaque says.
[0:48] Speaker 2: They've got a statue for Bowie, not yet for me.
[0:50] Speaker 2: I don't know why.
[0:52] Speaker 3: Here we go, Okay, let's go, okay, I'm ready.
[1:18] Speaker 2: Hello, and welcome back to another episode of Cautionary Questions,
[1:22] Speaker 2: our first of twenty twenty four. I am, of course
[1:24] Speaker 2: Tim Harford. You are our loyal listeners. You've been sending
[1:28] Speaker 2: in your burning questions on money, technology, economics and problem solving,
[1:33] Speaker 2: and thank you so much to everyone who's done so,
[1:37] Speaker 2: and today is the day I do my best to
[1:39] Speaker 2: answer them. And thankfully I won't be alone in this endeavor.
[1:43] Speaker 2: Here to help me out both of the questions, and
[1:45] Speaker 2: I think with some of the answers is the brilliant,
[1:49] Speaker 2: brilliant Jacob Goldstein, the host of Pushkin podcast What's Your Problem,
[1:54] Speaker 2: author of the book Money, The True Story of a
[1:57] Speaker 2: Made Up Thing. Jacob was my inaugural Cautionary Questions co
[2:01] Speaker 2: host Jacob's wonderful to.
[2:02] Speaker 1: Have you back, tim Asonana.
[2:04] Speaker 2: Hi, So we should just get on with the questions
[2:07] Speaker 2: because we always have so many and so much to say.
[2:09] Speaker 2: So what have you got in real big bag of
[2:11] Speaker 2: listener questions for me?
[2:13] Speaker 1: Let's start with a question from Alex in Melbourne, Australia.
[2:17] Speaker 1: Alex writes, I work with artificial intelligence image generation software
[2:22] Speaker 1: almost daily now, and I'm quickly seeing how so much
[2:26] Speaker 1: of my workforce and processes can either be sped up
[2:29] Speaker 1: or entirely replaced by AI, and this makes me nervous.
[2:33] Speaker 1: And then he asks about universal basic income, right, this
[2:37] Speaker 1: idea of a government giving all its citizens' money, And
[2:40] Speaker 1: he says it seems for the first time that computers
[2:42] Speaker 1: and software will actually replace jobs in a deeply concerning way,
[2:47] Speaker 1: which is both exciting and terrifying. What are your thoughts
[2:50] Speaker 1: on UBI, universal basic income as a solution to an
[2:53] Speaker 1: AI crisis and the widespread philosophical and economic implications of this.
[2:59] Speaker 2: I love this question. I mean it's so big, and
[3:04] Speaker 2: I think the first thing to say is we don't
[3:05] Speaker 2: really have any idea. If what Alex is thinking about
[3:10] Speaker 2: comes true, and if most people just have no economic value.
[3:14] Speaker 2: They have value as human beings, have value as members
[3:16] Speaker 2: of society, but there's nothing that they could actually sell
[3:18] Speaker 2: their labor to do, then that's completely uncharted territory. We've
[3:23] Speaker 2: never been anywhere like that before, so everything we do
[3:27] Speaker 2: is kind of speculative.
[3:28] Speaker 1: We've feared it for a long time, right We've have
[3:32] Speaker 1: two hundred years of being afraid of technological unemployment. And
[3:36] Speaker 1: my prior on this is to be somewhat skeptical. Right, Like,
[3:41] Speaker 1: clearly there can be some large number of people who
[3:45] Speaker 1: lose their jobs, and we should be concerned about that,
[3:47] Speaker 1: and we should think about how to mitigate that, But
[3:49] Speaker 1: the idea of more or less everybody losing their jobs
[3:53] Speaker 1: I'm skeptical of for the simple reason that it hasn't
[3:55] Speaker 1: happened in two hundred years of incredible technological progress. And
[3:58] Speaker 1: right now, after decades of extreme technological progress, in the US,
[4:03] Speaker 1: unemployment is below four percent, the share of working age
[4:07] Speaker 1: people who are working is near all time highs, and
[4:10] Speaker 1: so somehow we keep coming up with new things to
[4:14] Speaker 1: do for money, no matter how many things computers can do.
[4:17] Speaker 1: And so my first thought is, I don't think we're
[4:21] Speaker 1: going to have everybody losing their jobs to AI. I
[4:23] Speaker 1: definitely could be wrong, but that's what I think.
[4:25] Speaker 2: No, I think that's a good working assumption. If you
[4:29] Speaker 2: think back a few centuries, basically, almost all the labor
[4:32] Speaker 2: that people did, they might wash their clothes occasionally, Well,
[4:35] Speaker 2: that's been outsourced to the washing machines that spend a
[4:38] Speaker 2: lot of time moving water around, just drinking water, cooking water,
[4:42] Speaker 2: throwing out human excrement. That's all now handled by automated systems.
[4:47] Speaker 1: Digging people did a lot of digging, right, pulling a plow.
[4:50] Speaker 2: Almost everything we used to do is now done by machines.
[4:54] Speaker 2: But somehow we still all have jobs. Let's at least
[4:56] Speaker 2: accept the premise that maybe this time it might be different.
[5:00] Speaker 2: Because the robots are doing everything. There's still material prosperity,
[5:03] Speaker 2: there's food out there, in all the services we could
[5:06] Speaker 2: possibly want. We just have to find some system and
[5:09] Speaker 2: whereby the humans who have no economic value get to
[5:13] Speaker 2: enjoy all this cool stuff that's being produced by the machines.
[5:16] Speaker 1: Yeah, and you know, technological prosperity has given us in
[5:22] Speaker 1: the developed world ubi for old people, right in the US,
[5:26] Speaker 1: as in every developed country as far as I know,
[5:29] Speaker 1: once you get to some age, if you are a
[5:31] Speaker 1: citizen and you have worked, yeah, the government gives you money, yeah,
[5:35] Speaker 1: every month until you die, right, And so you could
[5:38] Speaker 1: imagine a kind of creeping extension of that. Certainly, right
[5:43] Speaker 1: now they're not talking about lowering the retirement age in
[5:45] Speaker 1: the US. They're talking about raising it as in many countries.
[5:48] Speaker 2: Yeah, raising it in the UK as well, But it's
[5:50] Speaker 2: still a long way away from life expectancies. My recollection
[5:53] Speaker 2: is that when Bismarck introduced the first pension, which was
[5:55] Speaker 2: in Germany in the late nineteenth century, I think the
[6:00] Speaker 2: pension started to be paid at the age sixty seven
[6:04] Speaker 2: and the life expectancy was sixty three.
[6:07] Speaker 1: You get negative four years in expectation.
[6:10] Speaker 2: People who survive long enough to claim any pension at
[6:12] Speaker 2: all are already exceptional. So that would be like having
[6:15] Speaker 2: a pension today that started at age ninety.
[6:18] Speaker 4: Like some people will get it, but most bad way.
[6:20] Speaker 2: But actually a lot of people could easily collect thirty
[6:23] Speaker 2: years of pension. Certainly twenty years so they're living a
[6:27] Speaker 2: large proportion of the adult life receiving money from the state,
[6:30] Speaker 2: and they're also receiving money from their own savings, their
[6:33] Speaker 2: own investments, which might be a replacement for UBI. Maybe
[6:37] Speaker 2: we all just have shares in the robots instead with
[6:39] Speaker 2: shares in Google or whatever, and that's how we get paid.
[6:42] Speaker 1: Yes, whether that is mediated by the government or not
[6:44] Speaker 1: looks somewhat different. Right, Either the government is taxing the
[6:48] Speaker 1: owners of in Nvidia and Google stock and distributing the money,
[6:52] Speaker 1: or everybody owns Google and Nvidia stock. There is the
[6:56] Speaker 1: piece of this which is about the non financial parts
[7:01] Speaker 1: of work, right, I mean there's a political economy piece,
[7:03] Speaker 1: the sort of bridge how do we get from here
[7:05] Speaker 1: to there? If that happens, And that's complicated and maybe ugly.
[7:08] Speaker 2: If the robots can do all of the stuff the
[7:11] Speaker 2: computers can do it, there's no economic reason why humans
[7:15] Speaker 2: couldn't just receive whatever an allowance they're given ten robots,
[7:19] Speaker 2: or they're given ten thousand dollars a month to spend
[7:22] Speaker 2: or whatever they like. There's no economic reason why that
[7:24] Speaker 2: couldn't happen. But I think what you're getting at is
[7:26] Speaker 2: what does it do to us if we're in that situation.
[7:29] Speaker 1: Yeah, yeah, Like I don't want to not have a job.
[7:32] Speaker 1: I recognize that I am fortunate to have a job
[7:35] Speaker 1: that I enjoy, that I derive a part of my
[7:38] Speaker 1: identity from. I recognize that for many, in fact probably
[7:42] Speaker 1: most people, a job is not that it's some unpleasant
[7:44] Speaker 1: thing they do because they need the money, and if
[7:47] Speaker 1: they got more money, they would quit their job. Well,
[7:50] Speaker 1: you probably know the kind of empirical evidence on this
[7:52] Speaker 1: better than I do. Like, people have looked at lottery winners, right,
[7:55] Speaker 1: and my sense is it's not great for you to
[7:58] Speaker 1: win the lottery and quit your job. It doesn't actually
[8:00] Speaker 1: make you happier.
[8:01] Speaker 2: Actually, the evidence on lottery winners is a bit mixed,
[8:03] Speaker 2: and I think slightly. We hear all the disaster stories
[8:06] Speaker 2: of lottery winners.
[8:07] Speaker 1: Actually, it's great to win the lottery. That's amazing. Is
[8:09] Speaker 1: that true?
[8:10] Speaker 4: I think it's fine. Yes, it's just fine to win
[8:12] Speaker 4: the lottery.
[8:13] Speaker 2: Is not really a problem to win the lottery. In
[8:15] Speaker 2: my vague recollection, I wrote about this a couple of
[8:17] Speaker 2: years ago. But we may not all want a job,
[8:21] Speaker 2: but we all want something to do. We all want
[8:25] Speaker 2: to feel useful. We all want a sense of some
[8:28] Speaker 2: kind of purpose. We all want, I think, that experience
[8:31] Speaker 2: of mastery, that experience of knowing that you can do
[8:34] Speaker 2: something that not everybody can do. Those things don't have
[8:37] Speaker 2: to come from a job, but for a lot of
[8:38] Speaker 2: people they do come from a job.
[8:40] Speaker 1: Yeah. For me, in my narrow provincial experience of life,
[8:44] Speaker 1: it's frankly hard to imagine getting those things without a job. Yeah.
[8:49] Speaker 2: It comes back to my initial reaction to Alex's terrific question,
[8:52] Speaker 2: which is this is such unknown territory that we can
[8:55] Speaker 2: only really speculate. But I think you and I J.
[8:58] Speaker 2: Cub are in agreement that the fundamental issue here is
[9:00] Speaker 2: not economic. It's really to do with our souls. How
[9:03] Speaker 2: would we react if our desire for mastery, our desire
[9:07] Speaker 2: for meaning, our desire to feel useful, if that had
[9:10] Speaker 2: to be satisfied without having a job, And what would
[9:13] Speaker 2: we do? And could we cope? And I don't know, I.
[9:16] Speaker 1: Mean, Harford, if the robots come and take our jobs,
[9:19] Speaker 1: let's just you and me make a podcast for free.
[9:21] Speaker 1: I'm in. I'll commit right now in the robot utopia
[9:25] Speaker 1: apocalypse to making a weekly podcast with you for free.
[9:29] Speaker 4: Deal.
[9:29] Speaker 3: Sorry, we have no more time you might give in
[9:33] Speaker 3: your robot overload.
[9:35] Speaker 1: Give us another question, Hello, Race, I'm enough. I would
[9:40] Speaker 1: like to ask, with the onset of AI, what is
[9:43] Speaker 1: the next cautionary tale you anticipate talking about in the
[9:47] Speaker 1: years to come?
[9:48] Speaker 2: Hello Abanov, I have just finished the first draft of
[9:51] Speaker 2: a script about the coming of AI, and without giving
[9:56] Speaker 2: too much away, the cautionary note is about what happens
[10:00] Speaker 2: when automation gets so good that we lose our own skills,
[10:05] Speaker 2: we hand over controlled to the machine, and then how
[10:09] Speaker 2: we respond when the machine says, actually, this one's too hard?
[10:13] Speaker 2: Could you take over again? Suddenly we're back at the
[10:15] Speaker 2: wheel and we're out of practice.
[10:18] Speaker 1: Harford, It's not another plane crash, is it? Don't you
[10:20] Speaker 1: have a moratry I'm undering plane crashes at this point
[10:22] Speaker 1: because it sounds like that, I'm.
[10:24] Speaker 2: Going to say nothing more fair enough, Let's go for
[10:26] Speaker 2: a break.
[10:28] Speaker 4: Right, Yeah, we're back.
[10:37] Speaker 2: I'm talking to Jacob Goldstein, the host of What's Your Problem,
[10:41] Speaker 2: and we are doing a Cautionary Questions Q and A episode.
[10:46] Speaker 2: Jacob has the questions. Jacob's also helping me with the answers,
[10:49] Speaker 2: and I'm really sepless to requirements here, but I'm doing
[10:51] Speaker 2: my best, Jacob, What have you got for me?
[10:53] Speaker 1: Tim? Our next question comes from Adam, who writes, Hi,
[10:58] Speaker 1: I have a question about investing in cryptocurrency.
[11:01] Speaker 4: Oh dear, oh dear, come on give it to them. Okay.
[11:05] Speaker 1: It's an interesting one. Traditionally, when buying shares in a company,
[11:09] Speaker 1: you would be supporting that company to grow, create a
[11:12] Speaker 1: new product, or enter a new retail space. This would
[11:15] Speaker 1: hopefully create jobs and new products. When people invest money
[11:19] Speaker 1: into cryptocurrency or any currency for that matter, isn't that
[11:23] Speaker 1: money just sitting around not doing anything until you withdraw it.
[11:27] Speaker 1: Would investing in crypto be bad for the economy compared
[11:30] Speaker 1: with investing in businesses?
[11:32] Speaker 4: This is a deep question. I really like it.
[11:37] Speaker 2: As the author of the wonderful book Money, The True
[11:40] Speaker 2: Story of a Maid art Thing, I am sure you.
[11:42] Speaker 4: Have thoughts, Jacob. Let me have a first crack and
[11:45] Speaker 4: you can tell me everything I've missed.
[11:46] Speaker 2: Yeah, so let's see you buy bitcoin or whatever, and
[11:49] Speaker 2: then the money's just sitting there because bitcoin is not
[11:52] Speaker 2: building anything. You're not buying investment in, say a road
[11:55] Speaker 2: building company, or you're not buying an investment in Google,
[11:58] Speaker 2: which is developing new technology. There's one of two possible
[12:01] Speaker 2: things that happens. So most likely is whoever you bought
[12:05] Speaker 2: the bitcoin from now has your money. Now it's their money,
[12:10] Speaker 2: and then they're going to do something with that money.
[12:11] Speaker 2: So maybe they then buy shares in Google, or they
[12:14] Speaker 2: then set up a business, or they will then go
[12:17] Speaker 2: on to do something with that money, or they'll lend
[12:20] Speaker 2: it to somebody else and that person will do something
[12:22] Speaker 2: with it. Eventually the money will find its way into
[12:24] Speaker 2: some productive investment. The fact that you bought bitcoin off
[12:28] Speaker 2: somebody does not mean that they won't then do something
[12:31] Speaker 2: useful with the money. But then let's say for some
[12:33] Speaker 2: reason they just go, you know what, I'm just going
[12:35] Speaker 2: to sit on this money. It's just dollars. I'm going
[12:37] Speaker 2: to stick them under the mattress. I guess a bitcoin
[12:39] Speaker 2: guy might do that. That would reduce inflationary pressures on
[12:43] Speaker 2: the economy, which is something we want anyway. And if
[12:47] Speaker 2: inflationary pressures are reduced too much, the Federal Reserve could
[12:52] Speaker 2: then go, you know what, guys, we should probably print
[12:54] Speaker 2: some money or otherwise stimulate the economy. Actually, the worst
[12:58] Speaker 2: thing that could possibly happen is that the money gets
[13:00] Speaker 2: invested in buying more computers that could be put to
[13:04] Speaker 2: better use, but in fact just end up doing bitcoin mining.
[13:07] Speaker 2: I guess what I'm saying is, as long as they
[13:08] Speaker 2: don't spend it on bitcoin mining computers, it's totally fine.
[13:12] Speaker 2: Well what am I missing?
[13:13] Speaker 1: I went around the same track as you went around,
[13:15] Speaker 1: and you know, different cryptocurrencies have different methods of regulation, right,
[13:20] Speaker 1: So bitcoin by design is this very energy intensive computing
[13:25] Speaker 1: process in order to mine bitcoin, and that is, in
[13:28] Speaker 1: my opinion, a bad outcome socially. Is I agree, because
[13:32] Speaker 1: you could imagine a world where maintaining the bitcoin network
[13:35] Speaker 1: was socially desirable. In the world as it has evolved,
[13:37] Speaker 1: I don't find it particularly socially desirable. But at this point,
[13:40] Speaker 1: I don't think most of the money going into bitcoin
[13:43] Speaker 1: is going to miners, right, there's a large stock of
[13:46] Speaker 1: bitcoin that exists in the world. I Mean. An interesting
[13:49] Speaker 1: thing about money is it almost never sleeps. It keeps going,
[13:53] Speaker 1: and so you can always ask what happens next. You know,
[13:57] Speaker 1: with the stock market, sometimes people talk about money on
[14:00] Speaker 1: the sidelines when they're like, well, the stock market can
[14:02] Speaker 1: go up somewhere because there's a lot of cash on
[14:04] Speaker 1: the sidelines. And there's this famous billionaire investor, cliff Asnas.
[14:09] Speaker 1: He's an interesting guy. He got a PhD from the
[14:11] Speaker 1: University of Chicago, studied with Gene Fama, famous economist, and
[14:15] Speaker 1: cliff Asnas gets driven up the wall when people say
[14:18] Speaker 1: cash on the sidelines for the same reason you just said, right,
[14:21] Speaker 1: Like every time somebody buys a share of stock, someone
[14:25] Speaker 1: else is selling it, right, So like the money is
[14:28] Speaker 1: changing hands, the stock is changing hands, but like there
[14:31] Speaker 1: are no sidelines with money.
[14:33] Speaker 2: Yeah, and even if someone puts just cash in a
[14:37] Speaker 2: checking account, well, then now the bank has money, and
[14:40] Speaker 2: you can bet the bank is going to want to
[14:42] Speaker 2: do something with that money, something productive. And even if
[14:45] Speaker 2: they doesn't put it in the bank, even if, as
[14:46] Speaker 2: I say, it's under a mattress, well, even then the
[14:49] Speaker 2: Federal Reserve could always put money.
[14:51] Speaker 1: Yeah, the stock of money is not fixed, right. I
[14:54] Speaker 1: think there's a there's an assumption in the question I
[14:57] Speaker 1: think goes back to the pre modern era, the era
[15:01] Speaker 1: before the nineteen thirties when money was based on gold
[15:04] Speaker 1: or silver and it was finite. Right, there was a
[15:07] Speaker 1: fixed amount of gold and silver in the world in
[15:09] Speaker 1: those days. Indeed, if you sat on money, you were
[15:11] Speaker 1: effectively reducing the supply of money in the world. But
[15:16] Speaker 1: to your point, the supply of money in the world
[15:18] Speaker 1: is as big as the Central Bank wants it to be,
[15:21] Speaker 1: and so it's not a meaningful constraint on economic growth.
[15:24] Speaker 1: So the ultimate answer to the question is, if you
[15:27] Speaker 1: are just sitting on money, it's not really going to
[15:29] Speaker 1: have an effect on the broader economy.
[15:31] Speaker 2: Well, I think we've come to agreement. Hopefully that makes
[15:33] Speaker 2: sense to Adam. Next question.
[15:37] Speaker 1: Our next question comes from doctor Yvonne Couch, who is
[15:42] Speaker 1: Associate Professor of Neuroimmunology, Alzheimer's Research UK Fellow, Associate Research
[15:48] Speaker 1: Fellow at Saint Hilda's College, stipendary lecturer at Somerville College,
[15:53] Speaker 1: University of Oxford.
[15:55] Speaker 2: Yeah, University of Oxford. I know, well, one of my
[15:57] Speaker 2: sisters went to Hilldess College and one of my sisters
[15:59] Speaker 2: went to Somerville College, so this feels very much in
[16:02] Speaker 2: the family. So what does doctor Couch got just say?
[16:05] Speaker 1: She writes, my question was vaguely economics based, although probably
[16:10] Speaker 1: not very is the way we currently fund science feasible
[16:15] Speaker 1: going forward? Do we just have too many scientists and
[16:19] Speaker 1: not enough resources?
[16:20] Speaker 2: All the best, Yvonne, Oh love it. Let me throw
[16:25] Speaker 2: out a few thoughts. Then I think that probably there
[16:29] Speaker 2: is a problem with the way we fund science. But
[16:31] Speaker 2: there's no one way that we fund science. You have
[16:33] Speaker 2: university research, you have various grants, various sources of funding,
[16:36] Speaker 2: philanthropy and so on. You also have private sector research,
[16:41] Speaker 2: which is often incentivized by the patent system. And then
[16:45] Speaker 2: you have big block grants that are handed out by
[16:48] Speaker 2: agencies such as the National Institutes for Health. So there
[16:53] Speaker 2: are lots of different ways that science gets funded. A
[16:57] Speaker 2: couple of things that worry me is that, first of all,
[17:01] Speaker 2: there's a big incentive to make really incremental improvements rather than.
[17:07] Speaker 4: To take risks.
[17:09] Speaker 2: There's a great economics paper studying scientists who are funded
[17:14] Speaker 2: by the National Institutes for Health, which is a wonderful institution,
[17:17] Speaker 2: very important, and scientists who on paper seem to be
[17:21] Speaker 2: the same that they're on the same career tracks, got
[17:23] Speaker 2: very similar publication records, and they are instead funded by
[17:27] Speaker 2: a private foundation called the Howard Hughes Medical Institute. Listeners
[17:32] Speaker 2: who want to hear more about Howard Hughes and the
[17:34] Speaker 2: Howard Hughes biography can go to the back catalog of
[17:36] Speaker 2: caution details. So the Howard Hughes Medical Institute basically takes risks.
[17:41] Speaker 2: It wants people to do something new. It's happy with
[17:44] Speaker 2: a high risk of failure as long as there's some
[17:46] Speaker 2: chance of a real breakthrough success. And this particular paper
[17:52] Speaker 2: studying the results that come from these two funding systems
[17:56] Speaker 2: basically finds that the grant funders get what they pay for.
[18:00] Speaker 2: So the National Institutes for Health get a high success rate,
[18:03] Speaker 2: but it's often quite incremental progress. And the Howard Hughes
[18:06] Speaker 2: Medical Institute has lots and lots of failures, but when
[18:09] Speaker 2: it succeeds, it really succeeds. And they these are hugely
[18:12] Speaker 2: important papers, and I just feel that we're not deliberate
[18:16] Speaker 2: enough about saying, well, how much of our funding ecosystem
[18:21] Speaker 2: should be aiming at kind of venture capital style for
[18:25] Speaker 2: really big wins, and how much should be incremental. I
[18:28] Speaker 2: think those are not the sorts of questions that get asked,
[18:31] Speaker 2: So that's one of the things that worries me.
[18:32] Speaker 4: But what's your take, Jacob.
[18:34] Speaker 1: One of the things that I have read is that
[18:37] Speaker 1: over time, the average age of grant recipients from the
[18:40] Speaker 1: NIH has gone up and up and up, so kind
[18:43] Speaker 1: of you just think of this whole universe as getting
[18:46] Speaker 1: older and more risk averse and more kind of bureaucratic.
[18:50] Speaker 1: There is an interesting set of counter pressures I think
[18:53] Speaker 1: rising up partly out of Silicon Valley. I don't know
[18:57] Speaker 1: if you've come across the work of Patrick Hollison and
[19:01] Speaker 1: his brother. They're from Ireland. They found it Stripe. They're
[19:04] Speaker 1: very rich. Stripe is a big company that does basically
[19:07] Speaker 1: payment stuff online. They have a really interesting set of
[19:11] Speaker 1: kind of philanthropic endeavors around the idea of progress. You know,
[19:16] Speaker 1: they're trying to create a kind of field of progress studies.
[19:20] Speaker 1: That is, it's very meta. What are the conditions that
[19:24] Speaker 1: best foster technological and scientific progress?
[19:28] Speaker 2: But this question from doctor Couch is very massive. Yes,
[19:31] Speaker 2: it's very much progress stud is, isn't it.
[19:33] Speaker 1: Yes. There's this institute in the Bay Area called the
[19:37] Speaker 1: Arc Institute that hires leading scientists. The basic idea is
[19:42] Speaker 1: give talented people freedom and money, right and encourage them
[19:47] Speaker 1: to take big swings. And they are interested, you know,
[19:50] Speaker 1: not just in outcomes but in new tools. Right again,
[19:53] Speaker 1: continuing on the nerdy thing, like if you take a
[19:56] Speaker 1: tool like Crisper. Crisper is an intermediate tool that allows
[19:59] Speaker 1: people to cut up a genome. Basically, we just had
[20:01] Speaker 1: the first transplant from an animal into a human a
[20:05] Speaker 1: few weeks ago because of Crisper. We have the first
[20:07] Speaker 1: treatments for hicle cell disease because of Crisper's. So I
[20:11] Speaker 1: do think there is a wave of people trying to
[20:15] Speaker 1: rethink scientific funding. There is a bottom line aspect to
[20:20] Speaker 1: this question from doctor Couch that I don't feel like
[20:24] Speaker 1: I know enough to answer, and I'm curious if you do.
[20:25] Speaker 1: I mean, the question, is the way we currently fund
[20:28] Speaker 1: science feasible going forward? There is a yes no version
[20:32] Speaker 1: of the answer. Do you think you know? Is it?
[20:35] Speaker 2: I don't know the answer, but I don't know either.
[20:38] Speaker 2: Another thing that concerns me on this, which is potentially
[20:42] Speaker 2: really existential, is the question of how we fund new antibiotics.
[20:47] Speaker 1: Huh.
[20:48] Speaker 2: I think this points to a real weakness in the
[20:52] Speaker 2: ecosystem of research funding. So if you think about the
[20:54] Speaker 2: basic way we develop drugs, the fundamental incentive is the patent.
[21:00] Speaker 2: So a drug company spends a lot of money, tries
[21:02] Speaker 2: to develop drugs. Some of them work, some of them fail,
[21:04] Speaker 2: a lot of them fail. But in the end you
[21:07] Speaker 2: have a drug, and the deal is give you this
[21:09] Speaker 2: artificial monopoly called a patent, and it will only last
[21:13] Speaker 2: for a certain amount of time, which is kind of
[21:15] Speaker 2: a problem because it takes so long to develop the drugs.
[21:17] Speaker 2: Maybe the patent's nearly expired. You can charge an incredible
[21:21] Speaker 2: amount for these drugs for a while, and then your
[21:23] Speaker 2: patterents will run out, and then loads of its people
[21:26] Speaker 2: will make the same drug and it'll come down in value.
[21:28] Speaker 2: For example, Viagra, you know you could sell this for
[21:30] Speaker 2: a huge amount of money, and now Viagra is off
[21:32] Speaker 2: patent and anyone can make a generic Viagra. So that's
[21:36] Speaker 2: the incentive that we're given to private companies that you
[21:39] Speaker 2: will have this temporary monopoly. So now think about antibiotics.
[21:44] Speaker 2: The problem with antibiotics. We have lots of antibiotics that
[21:46] Speaker 2: work really well, except the bacteria have figured them out.
[21:50] Speaker 2: And why have they figured them out Because we've used them.
[21:53] Speaker 2: The bacteria developed resistance. So what we really need is
[21:56] Speaker 2: new antibiotics that we don't use. And now think about
[22:02] Speaker 2: how the patent system deals with that. So you basically say,
[22:05] Speaker 2: if you develop a new antibiotic, we'll give you a
[22:08] Speaker 2: temporary minio, but we really need you to just not
[22:11] Speaker 2: sell any don't sell any of this things except.
[22:14] Speaker 1: In cases of dire emergency. It's like a break glass
[22:17] Speaker 1: in case of emergency. I mean, I feel like a bounty,
[22:21] Speaker 1: Like every EuCon nerd storyteller loves a good bounty.
[22:24] Speaker 3: Right.
[22:24] Speaker 1: Wasn't this done with malaria vaccine, which is actually coming
[22:28] Speaker 1: along quite well? You have some universe of people say
[22:31] Speaker 1: we will pay a billion dollars to anyone who comes
[22:34] Speaker 1: up with a new antibiotic that meets this set of
[22:37] Speaker 1: criteria that treats this set of bugs that are resistant
[22:41] Speaker 1: to these existing antibiotics, and we'll give you the money,
[22:44] Speaker 1: and you give it to us, and we'll put it
[22:45] Speaker 1: on the break glass in case of emergency shelf.
[22:48] Speaker 2: You've used the word bounty, but what the term that
[22:50] Speaker 2: is normally used is an advanced market commitment. These were
[22:53] Speaker 2: proposed most famously by Michael Kramer, who's a Nobel Prize
[22:56] Speaker 2: winner in economics. And basically the way this price, this
[23:00] Speaker 2: bounty tends to get paid is as a kind of
[23:03] Speaker 2: extra payment on top of each dose you sell. Huh,
[23:06] Speaker 2: So we'll give you extra for every kid that gets
[23:08] Speaker 2: vaccinated so.
[23:10] Speaker 1: We're back to the same problem in that universe.
[23:12] Speaker 2: Back to the same problem. But the reason we go, well,
[23:14] Speaker 2: why does it have to be like that? It doesn't
[23:15] Speaker 2: have to be like that. But the reason that they
[23:17] Speaker 2: tend to be structured like that is because you need
[23:19] Speaker 2: to demonstrate some kind of market demand. Somebody needs to
[23:22] Speaker 2: be willing to buy your product. If they are, we'll
[23:26] Speaker 2: give you an extra payment for every product you sell.
[23:29] Speaker 2: But that wouldn't work for antibiotics. There's so many different
[23:31] Speaker 2: ways in which science funding could be said to be broken.
[23:34] Speaker 2: But I think doctor Couch is right to raise the issue.
[23:38] Speaker 2: We need to do a lot more of this kind
[23:40] Speaker 2: of meta thinking about progress studies.
[23:42] Speaker 1: Tim, that was a lot of answer. Let's take a break.
[23:45] Speaker 1: Cautionary Tales will be back in a moment. Edward back.
[23:56] Speaker 1: I'm Jacob Goldstein here with Tim Harford on Cautionary Tales.
[23:59] Speaker 2: Hello, Jacob, more questions. What have you got for me?
[24:02] Speaker 1: All right, Tim, I got another one for you. Comes
[24:06] Speaker 1: from Graham in Florida. Gram Rights. I'm fascinated by errors
[24:13] Speaker 1: made by falsely identifying correlation as causation. What examples of
[24:18] Speaker 1: this error stand out to you?
[24:20] Speaker 2: I love the question, but I'm going to slightly sidestep
[24:23] Speaker 2: it because I'm worried by this question, but I think
[24:25] Speaker 2: that it's generally more complicated than simply, oh, some fool
[24:32] Speaker 2: sourer correlation and thought it was causation.
[24:35] Speaker 1: You don't want to talk about sun spots and crap yields, Well,
[24:38] Speaker 1: let's talk about storks and babies briefly, Okay. A classic,
[24:42] Speaker 1: A classic of the genre.
[24:43] Speaker 2: Yeah, the most successful book ever published about statistics, How
[24:48] Speaker 2: to Lie with Statistics by Darryl Huff, includes this example
[24:51] Speaker 2: of stalks and babies and shows that there's a correlation
[24:54] Speaker 2: between the number of storks and the number of babies.
[24:57] Speaker 2: And there are various ways to demonstrate this correlation. One
[24:59] Speaker 2: way to do it is you just look at national populations.
[25:02] Speaker 2: You go, hey, countries with lots of storks also have
[25:05] Speaker 2: lots of babies, and there's a very very strong correlation.
[25:07] Speaker 2: But of course the reason is big places like the
[25:10] Speaker 2: United States have a lot of room for storks and
[25:12] Speaker 2: a lot of room for babies, and small places like
[25:15] Speaker 2: in a Luxembourg or the Vatican City don't have a
[25:18] Speaker 2: lot of room for babies and don't have a lot
[25:19] Speaker 2: of room for storks. So then you go, oh, that's
[25:20] Speaker 2: a great example of this mistake. The sting in the
[25:24] Speaker 2: tale of that story, as I describe in my book
[25:27] Speaker 2: The Data Detective, is that Darryl Huff, the guy who
[25:30] Speaker 2: created that story, then went on to tell the same
[25:34] Speaker 2: story in congressional testimony, saying that there was no compelling
[25:40] Speaker 2: evidence that smoking cigarettes would give you lung cancer, and
[25:43] Speaker 2: it was just like stalks and babies. And he was
[25:45] Speaker 2: actually hired by the tobacco lobby.
[25:47] Speaker 1: It was just correlation.
[25:48] Speaker 2: It was just correlational. They seemed about the same. Sure, yeah,
[25:52] Speaker 2: there are people who smoke, and that people who get cancer,
[25:54] Speaker 2: but there's no cause of evidence. One theory was cigarettes
[25:58] Speaker 2: are so soothing, and if you have some early symptoms
[26:01] Speaker 2: of lung cancer, maybe you soothed that with the soothing
[26:04] Speaker 2: vapors of cigarettes. I mean, it's completely ridiculous, but this
[26:06] Speaker 2: sort of rhetoric was deployed. And so one of the
[26:09] Speaker 2: things I'm very concerned about in my work on statistics
[26:12] Speaker 2: is that it's great to be skeptical about statistics and
[26:16] Speaker 2: to point out lots of examples of statistics being misused.
[26:19] Speaker 2: But if that's all you do, you just get to
[26:22] Speaker 2: a canihilistic situation where you're just constantly rejecting statistical evidence
[26:26] Speaker 2: because it's just another of those dawn lines and statistics
[26:29] Speaker 2: when you look at the real world, I think this
[26:32] Speaker 2: gets to be incredibly interesting. So a real hot topic
[26:36] Speaker 2: at the moment is our smartphones destroying a generation. Basically,
[26:41] Speaker 2: are our kids having their mental health wrecked.
[26:43] Speaker 1: There's a new book that basically makes that up, Yeah,
[26:46] Speaker 1: by prominent ecademic Yea by Jonathan Hyde, and there have
[26:50] Speaker 1: been others by Gene Twegi, and lots of people have
[26:51] Speaker 1: said this, and the evidence for it is mostly correlational,
[26:57] Speaker 1: not completely.
[26:58] Speaker 2: There are some experiments, but they're not that convincing. None
[27:02] Speaker 2: of them are perfect, but there are lots of different
[27:03] Speaker 2: ways of measuring it. And the really interesting evidence basically says, look,
[27:09] Speaker 2: there appears to be a mental health crisis. The kids
[27:11] Speaker 2: seem to get really distressed, particularly the girls, when children
[27:16] Speaker 2: have access to social media on their phones, and that
[27:19] Speaker 2: happens around sometime between twenty ten and twenty fourteen, and
[27:23] Speaker 2: at the same time, suddenly you've got this outbreak of
[27:26] Speaker 2: suicidal ideation, self harm, poor mental health, and so on.
[27:30] Speaker 2: And that's correlational evidence. I don't completely believe it, But
[27:34] Speaker 2: I don't completely not believe it either. I think that's
[27:36] Speaker 2: what makes it interesting and makes it important to engage with.
[27:40] Speaker 2: You have to start going, well, what's the alternative explanation?
[27:42] Speaker 2: Is there something else that has happened sometime about sort
[27:45] Speaker 2: of ten, twelve, fourteen years ago that might explain this
[27:49] Speaker 2: mental health distress? So the timing of the Great Financial
[27:51] Speaker 2: Crisis probably not quite right Donald Trump. Maybe Donald Trump
[27:54] Speaker 2: is upsetting the kids. Timing doesn't work, so partly does
[27:58] Speaker 2: the pan of the correlation make enough sense to explain
[28:02] Speaker 2: this causal story, which, funny enough, is basically exactly what
[28:05] Speaker 2: the scientists finding a connection between cigarettes and lunghuans were doing.
[28:09] Speaker 2: They only had correlational evidence for a long time. They
[28:12] Speaker 2: weren't running randomized trials saying you know, half of you
[28:15] Speaker 2: smoke and half of you don't smoke. I mean, they
[28:16] Speaker 2: couldn't do that. They had to look at correlational evidence,
[28:19] Speaker 2: and sometimes that's what we've got.
[28:21] Speaker 1: This question got me thinking about the rise in social
[28:25] Speaker 1: science of what they call natural experiments, right, trying to
[28:29] Speaker 1: find instances in the real world where you have something
[28:34] Speaker 1: that obviously is not as good as a randomized trial,
[28:36] Speaker 1: because you just can't get that with large numbers of
[28:38] Speaker 1: people in the world. But that gives you some element
[28:42] Speaker 1: of randomization or pseudo randomization, something that allows you to
[28:46] Speaker 1: try and make the leap from correlation to causation. And
[28:50] Speaker 1: you know, this, as far as I know, goes back
[28:52] Speaker 1: to the Vietnam War, right, where in the US there
[28:55] Speaker 1: was a draft lottery, and there were social scientists after
[28:59] Speaker 1: the war who looked and said, oh, look, here are
[29:01] Speaker 1: people who are at an aggregate level very similar on
[29:05] Speaker 1: many dimensions, socioeconomic dimensions. We can look at people who
[29:09] Speaker 1: randomly got drafted versus those who randomly didn't and who
[29:13] Speaker 1: appear quite similar in the aggregate, and see how their
[29:16] Speaker 1: outcomes differ.
[29:17] Speaker 2: And that's a really good example of a natural experiment
[29:20] Speaker 2: because it's actually very close to a really.
[29:22] Speaker 1: Yeah, when you get a lottery in the real world, now,
[29:25] Speaker 1: social scientists flock to it.
[29:27] Speaker 3: Right.
[29:27] Speaker 1: Similarly, there was one in Oregon some years ago with Medicaid,
[29:32] Speaker 1: which is the healthcare program for low income people in
[29:35] Speaker 1: the US, and Oregon got some new Medicaid funds and
[29:39] Speaker 1: they randomly allocated them to a group of people over time, Right,
[29:43] Speaker 1: So that social scientists could say, oh, look, here are
[29:46] Speaker 1: people who are basically identical. Some of them got this
[29:48] Speaker 1: health insurance and some of them didn't, and in that
[29:50] Speaker 1: case the findings were quite interesting. Medicaid didn't appear to
[29:53] Speaker 1: be as helpful as I would have thought, as the
[29:56] Speaker 1: researchers themselves would have thought. According to the one I interviewed,
[30:00] Speaker 1: lowered mental health problems, people worried less about money, but
[30:04] Speaker 1: like their basic health outcomes didn't improve, which nobody would
[30:07] Speaker 1: have guessed right. And the the evidence is quite robust
[30:11] Speaker 1: when you don't have those lotteries. As you say, the
[30:14] Speaker 1: world is just hard to understand. I mean, even in
[30:16] Speaker 1: some instances where you do have randomized trials, people are complicated.
[30:20] Speaker 1: The body is complicated, society is complicated, and so you know,
[30:24] Speaker 1: correlation in a certain way I think is underrated. Like, yes,
[30:28] Speaker 1: obviously it doesn't equal causation, but it's a place to
[30:31] Speaker 1: start looking, right, It's a place to start asking questions.
[30:34] Speaker 2: I think that's absolutely right. And we need more randomized
[30:37] Speaker 2: experiments that there are more opportunities to run them than
[30:40] Speaker 2: people seem to think. For example, one of the things
[30:42] Speaker 2: that John Hyde in his book is arguing for is
[30:44] Speaker 2: you know, shouldn't have smartphones in schools. Schools should be
[30:48] Speaker 2: phone free zones. I could completely imagine a state saying
[30:51] Speaker 2: we're going to introduce a rule whereby in all of
[30:54] Speaker 2: the schools in the state, no smartphones strictly forbidden. You
[30:59] Speaker 2: have to put them in a locker when you show
[31:00] Speaker 2: up and then unlock them at the end of the day.
[31:02] Speaker 2: You could introduce that rule and just go, well, we know,
[31:04] Speaker 2: introduce it for a semester at random in fifty percent
[31:08] Speaker 2: of the schools, and then we'll introduce it in the
[31:11] Speaker 2: other fifty percent of the schools in the next semester,
[31:14] Speaker 2: and we'll just randomize that because we want to know
[31:16] Speaker 2: whether there's any point in this experiment or not. That's
[31:19] Speaker 2: not very difficult to do. It would create so much
[31:22] Speaker 2: information about children's performance in the classroom, their mental health
[31:27] Speaker 2: that could inform policy across the world.
[31:29] Speaker 1: That would be the dream. You know, there is now
[31:32] Speaker 1: a robust set of methods essentially where social scientists could
[31:36] Speaker 1: look at that state and compare it to neighboring states
[31:39] Speaker 1: and see the difference in differences, as they say, see
[31:42] Speaker 1: the change over time. And that's an instance where it
[31:45] Speaker 1: wouldn't be as elegant as randomizing within a state. But
[31:49] Speaker 1: I feel like you could start to get pretty good
[31:51] Speaker 1: data even if you did one state compared to other states.
[31:55] Speaker 2: Yeah, if you've got no experiments, we should run them.
[31:58] Speaker 2: If we don't run them, there are still ways of
[32:00] Speaker 2: making correlation.
[32:01] Speaker 4: Talk.
[32:03] Speaker 2: Do we have another question?
[32:05] Speaker 1: Okay, Tim, we are one more. We had to have
[32:08] Speaker 1: one game related question for great Yes, you're welcome. This
[32:12] Speaker 1: one comes from Yost, who writes, Hi, Tim, diving into
[32:17] Speaker 1: the board game shaped flank you've now exposed for discussion.
[32:21] Speaker 1: That is a very gamersh way in what board games
[32:25] Speaker 1: or board game mechanisms that you enjoy are particularly insightful
[32:28] Speaker 1: on some aspect of real world economics offer a unique
[32:32] Speaker 1: angle to look at a maybe niche problem. And what
[32:37] Speaker 1: non D and D games are you particularly enjoying right now?
[32:41] Speaker 1: Love the show, however many episodes you produce that is
[32:45] Speaker 1: a listener We all want thank you, yos.
[32:47] Speaker 2: Yeah, so kind your There are a couple of mechanisms
[32:50] Speaker 2: that I do see used quite often that shed important
[32:53] Speaker 2: light to economics. So one is trading a lot of
[32:56] Speaker 2: games involved trading. Now Monopoly, that classic, of sadly not
[33:01] Speaker 2: a very good game. Oh, there's a caution we tell
[33:03] Speaker 2: about the history of Monopoly if people want to listen.
[33:06] Speaker 2: Monopoly in theory involves trading, but in practice, not a
[33:09] Speaker 2: lot of trading happens. It turns out that you need
[33:13] Speaker 2: to give people a reason to trade. The great modern
[33:17] Speaker 2: game Settlers, now about thirty years old.
[33:19] Speaker 1: By the way, Settlers, I assume that Settlers of Catan
[33:22] Speaker 1: as an outsider, not on a first name basis.
[33:24] Speaker 3: With the game.
[33:25] Speaker 2: Settlers of Katan is the game. It's super It's the
[33:28] Speaker 2: game that Monopoly wishes it was. It's like if Monopoly
[33:31] Speaker 2: had been designed with a modern eye. To be super exciting.
[33:35] Speaker 2: The people need different resources, and the supply of resources fluctuates,
[33:40] Speaker 2: so sometimes you've got loads of wood, sometimes there's no wood,
[33:43] Speaker 2: so there's an active incentive to trade all the time.
[33:45] Speaker 2: Oh and by the way, if you don't trade every
[33:47] Speaker 2: now and then the robber Barron comes and take stuff.
[33:49] Speaker 2: So it's an interesting insight that trading doesn't just happen
[33:52] Speaker 2: because it's allowed. There needs to be some difference in
[33:55] Speaker 2: value and perhaps some incentive.
[33:56] Speaker 4: To get on with it.
[33:58] Speaker 2: So my personal favorite game is Agricola, a wonderful game
[34:02] Speaker 2: about developing a farm, which doesn't sound promising, but it's
[34:05] Speaker 2: really really good. But one of the clever things about
[34:08] Speaker 2: agricola is it uses an auction in an interesting way,
[34:12] Speaker 2: but it's sometimes called a descending.
[34:13] Speaker 4: Clock auction or a Dutch auction.
[34:16] Speaker 2: The prize gets more and more tempting, so in a
[34:20] Speaker 2: gricola there's just more and more good stuff on the board.
[34:22] Speaker 2: In a traditional descending clock auction, basically the price is
[34:25] Speaker 2: going down and down and down and down, and everyone
[34:28] Speaker 2: is just sitting there and then it's a question of
[34:29] Speaker 2: who grabs it first. The longer you leave it, the
[34:32] Speaker 2: better it gets. But then only one person can have
[34:35] Speaker 2: it if very very elegant. They sell flowers at Alsmere
[34:39] Speaker 2: in the Netherlands and it's just incredibly quick, much much
[34:42] Speaker 2: quicker than selling say art. You know you're selling a
[34:45] Speaker 2: van goal hah No. The price rises and rises and rises.
[34:47] Speaker 1: So auction design is a whole thing, right, Yeah. When
[34:50] Speaker 1: would you choose a traditional prices going up eBay art
[34:53] Speaker 1: style auction in the real world, and when would you
[34:56] Speaker 1: choose a Dutch prices going down auction? What do you
[34:59] Speaker 1: get in a relative sense out of each one of those?
[35:01] Speaker 2: There's a couple of differences. One is that in certain
[35:05] Speaker 2: types of ascending auction you get to see people dropping
[35:09] Speaker 2: out as the price rises. It depends on the way
[35:11] Speaker 2: the auction is designed, but you could imagine an ascending
[35:14] Speaker 2: auction where you just see people going no, I'm out,
[35:16] Speaker 2: I'm out, I'm out.
[35:17] Speaker 1: Sort of the classic kind of art auction in a movie.
[35:21] Speaker 2: Yeah, and so you're learning information as the price rises.
[35:23] Speaker 2: How many people are still interested? Are there still ten
[35:25] Speaker 2: people interested? Is it just two people? So that generates information.
[35:28] Speaker 2: By generating information, it might raise the final price.
[35:32] Speaker 4: Overall.
[35:34] Speaker 2: The advantage of the descending auction is it's just so quick.
[35:37] Speaker 2: You can run an auction in ten seconds. It's one
[35:41] Speaker 2: hundred thousand tulips. Here are the tulips. We're going to
[35:43] Speaker 2: sell them for whatever, ten thousand euros nine eight hundred thousand,
[35:49] Speaker 2: and it's literally a clock. The clock just runs around
[35:52] Speaker 2: showing what the price is, and then someone presses the button. Sold, okay,
[35:57] Speaker 2: now bringing the roses. So it's very fast.
[35:59] Speaker 1: You would want to have a good idea of the
[36:01] Speaker 1: market clearing price if you were going to run that
[36:03] Speaker 1: kind of auction, so that you didn't sell it for
[36:05] Speaker 1: too cheap. Right, it's fast, but you run the risk
[36:08] Speaker 1: of selling it for cheap. But if it's just commodity toolips,
[36:11] Speaker 1: then you basically know the market clearing price anyways.
[36:13] Speaker 2: Yeah, it's almost like a stock market for tulips. That
[36:15] Speaker 2: I think is why it works so well. Whereas if
[36:17] Speaker 2: it's a unique work of art, you need to give
[36:20] Speaker 2: maximum information, maximum comfort to people, maybe a little bit
[36:23] Speaker 2: of theater as well. We're not in a hurry to
[36:25] Speaker 2: run this auction. The cost of the auction itself is
[36:28] Speaker 2: trivial compared to the value of what's being sold.
[36:32] Speaker 1: Now we go over to agricola and have a descending action.
[36:34] Speaker 1: It feels exciting and it also feels like you're trying
[36:38] Speaker 1: to guess what is in your opponent's head, right, because
[36:40] Speaker 1: they're not revealing it as they would in ascending price auction.
[36:44] Speaker 1: You have to think, oh, what are they willing to
[36:46] Speaker 1: pay for it?
[36:46] Speaker 2: Absolutely, and it works slightly differently, but the functionally what's
[36:49] Speaker 2: going on is that every turn you take you get
[36:53] Speaker 2: to grab some resource on the board, and then once
[36:55] Speaker 2: you've grabbed the resource on the board, no one else
[36:57] Speaker 2: is allowed to grab it until the next round. And
[37:00] Speaker 2: there are several moves you can make in each turn,
[37:02] Speaker 2: so you might be able to get two or three
[37:04] Speaker 2: things each turn, And so you go backwards and forwards.
[37:07] Speaker 2: It's your move and it's my move. And each time
[37:08] Speaker 2: we want to grab something, and you might go, well
[37:11] Speaker 2: do I want to grab it now? What do I
[37:12] Speaker 2: want to wait until next turn where it might be better?
[37:14] Speaker 2: And you're trying to figure out, like, what is the
[37:16] Speaker 2: thing that the other person is desperate to have? Can
[37:20] Speaker 2: I just let this pile get bigger because they can't
[37:24] Speaker 2: afford to take it because they've got to use their
[37:25] Speaker 2: move on something else. It's a very very clever game.
[37:28] Speaker 4: I do enjoy it.
[37:29] Speaker 2: Yours also ask what non D and D games I
[37:33] Speaker 2: am enjoying. I am enjoying a modified version of Blades
[37:37] Speaker 2: in the Dark, which's a classic role playing game, very
[37:41] Speaker 2: fast moving. You get to do Ocean's eleven or other
[37:44] Speaker 2: kind of heists, and it's very modifiable. We're speaking on
[37:48] Speaker 2: a Monday. Just yesterday on Sunday, had a whole bunch
[37:51] Speaker 2: of old friends around to my house and we just
[37:53] Speaker 2: played this game all day and we had an absolutely
[37:57] Speaker 2: terrific time.
[37:58] Speaker 1: I feel like you're living the dream. Harford makes me happy.
[38:00] Speaker 2: Living my best life. Jacob, this has been such fun.
[38:04] Speaker 2: Thank you so much for doing this.
[38:05] Speaker 1: Ah, it was a delight. I'll come back anytime.
[38:09] Speaker 4: Love to have you back.
[38:11] Speaker 2: Thank you everybody who sent in a question. Sorry we
[38:13] Speaker 2: weren't able to answer all the questions, but we are
[38:15] Speaker 2: going to be back with another Cautionary Questions episode later
[38:18] Speaker 2: this year. Please do keep your queries coming, send them
[38:21] Speaker 2: into tales at pushkin dot fm. That's t a l
[38:25] Speaker 2: e s at Pushkin dot fm and I'll be back
[38:29] Speaker 2: with another Cautionary Tale in two weeks time. Jacob Goldstein
[38:33] Speaker 2: has a wonderful podcast called What's Your Problem?
[38:36] Speaker 4: Thank you Jacob, Thanks Je.
[38:45] Speaker 2: Cautionary Tales is written by me, Tim Harford with Andrew Wright.
[38:49] Speaker 2: It's produced by Alice Fines with support from Marilyn Rust.
[38:53] Speaker 2: The sound design and original music is the work of
[38:56] Speaker 2: Pascal Wise. Sarah Nix edited the scripts. It features the
[39:00] Speaker 2: voice talents of Ben Crow, Melanie Guttridge, Stella Harford, Jamma Saunders,
[39:05] Speaker 2: and Rufus Wright. The show also wouldn't have been possible
[39:09] Speaker 2: without the work of Jacob Weisberg, Ryan Dilly, Gretta Cohne,
[39:14] Speaker 2: Vital Mollard, John Schnaz. Eric's Handler, Carrie Brody and Christina Sullivan.
[39:20] Speaker 2: Cautionary Tales is a production of Pushkin Industries. It's recorded
[39:24] Speaker 2: at Wardoor Studios in London by Tom Berry. If you
[39:28] Speaker 2: like the show, please remember to share, rate and review,
[39:33] Speaker 2: tell your friends and if you want to hear the
[39:35] Speaker 2: show ad free, sign up for Pushkin Plus on the
[39:39] Speaker 2: show page in Apple Podcasts or at pushkin dot Fm,
[39:43] Speaker 2: slash plus

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