296: Robin Hanson On The Argument For Variolation

Robin Hanson is an associate professor of economics at George Mason University and a research associate at the Future of Humanity Institute of Oxford University. In this conversation, we discuss the current COVID-19 response, why variolation is a viable option to develop a solution, what futarchy is, why the stock market is a prediction market, and whether aliens are real or not. =============================== Blockset by BRD is your hosted blockchain infrastructure. Blockset enables enterprises and developers around the globe to deliver high-quality blockchain-based applications in a fraction of the time, at a fraction of the cost. Using the services provided by Blockset, businesses can build professional custody solutions, accurate and near real-time portfolio management solutions, auditing platforms, commercial block explorers, and much more: blockset.com =============================== Crypto.com is the only all-in-one platform that allows you to BUY / SELL / STORE / EARN / LOAN / INVEST crypto all from one place. Join over 1 million users currently using the Crypto.com app. Download and earn $50 USD using my code ‘pomp2020’, or use the link http://platinum.crypto.com/r/pomp2020 when you sign up for one of their metal cards today. =============================== Pomp writes a daily letter to over 50,000 investors about business, technology, and finance. He breaks down complex topics into easy to understand language, while sharing opinions on various aspects of each industry. You can subscribe at www.pompletter.com

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  This is Anthony Pompliano. Most of you know me as Pomp. You're listening to the Pomp Podcast, simply the best podcast out there. Let's kick this thing off.

• 0:11Speaker 00

  Robin Hanson is an Associate Professor of Economics at George Mason University and a Research Associate at the Future of Humanity Institute of Oxford University. In this conversation, we discuss the current COVID-19 response, why variolation is a viable option to develop a solution, what futarchi is, why the stock market is a prediction market, and whether aliens are real or not. I really enjoyed this conversation with Robin, and I hope you do as well. Before we get into the episode, though, I want to quickly talk about our sponsors. The first is Blockset by BRD. They're your hosted blockchain infrastructure. Think of them like Amazon AWS for blockchain infrastructure. Blockset

• 0:53Speaker 00

  enables enterprises and developers around the globe to deliver high-quality blockchain-based applications in a fraction of the time at a fraction of the cost. Using the services provided by Blockset, businesses can build professional custody solutions, accurate near-real-time portfolio management solutions, auditing platforms, commercial block explorers, and much more. Go check them out, blockset.com. Again, blockset.com, your hosted blockchain infrastructure. Next is crypto.com, the only all-in-one platform that allows you to buy, sell, store, earn, loan, and invest crypto, all from one place. They've got over a million users currently using the crypto.com app. You can download and earn $50 using my code,

• 1:35Speaker 00

  Pomp2020, or use the link in the description when you sign up for one of their metal cards today. Crypto.com's not only got a cool URL, they also got a whole bunch of new products coming online all the time. Go check them out at crypto.com. Lastly, don't forget that I write a daily letter to over 50,000 investors about business, technology, and finance. I break down complex topics into easy-to-understand language while sharing opinions on various aspects of each industry. You can subscribe at pompletter.com, pompletter.com, or go click the link in the description. All right, let's get into this episode with Robin. I hope you guys enjoy this one.

• 2:13Speaker 01

  Anthony Pompliano is a partner at Morgan Creek Digital. All opinions expressed by Pomp for his guests on this podcast are solely their opinions and do not reflect the opinions of Morgan Creek Digital or Morgan Creek Capital Management. You should not treat any opinion expressed by Pomp as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his opinion. This podcast is for informational purposes.

• 2:40Speaker 00

  All right, guys. Bang, bang. Super excited to have Robin here. We got a whole bunch of stuff to get through, so thanks for doing this.

• 2:48Speaker 02

  Great to see you.

• 2:49Speaker 00

  Absolutely.

• 2:49Speaker 02

  And I don't have a cap on, so.

• 2:52Speaker 00

  Only reason I have one on is because I haven't gotten a haircut in a while.

• 2:56Speaker 02

  Well, I haven't either. I maybe I should, I should put the cap on thing. I'll get a haircut next week. I'm scheduled finally to get another one, so.

• 3:03Speaker 00

  Perfect. Let's, um, let's start with just your background. So those that don't know you, you've done a ton of different things. Maybe kind of give us.

• 3:10Speaker 02

  Because I'm old, I'm old. Let's make sure they know that, too.

• 3:13Speaker 00

  I, uh, I like to say it's not old, it's experienced, right?

• 3:18Speaker 02

  Uh, well, it's both. Well, old comes with a bunch of things, including experience and wisdom, but some other negatives, too. Sure. So I started out long, long ago, uh, as an undergraduate in engineering. And then I thought where I was, it was too cookbook. So I switched to physics. And then I got a physics undergrad, but then I decided to go off and do philosophy of science for grad school. But then after two years, I decided, oh, I'd learned what I needed to know about philosophy of science, the deep questions that answered enough. And I switched back to physics to get a master's in physics and a master's philosophy of science. Then I read some stuff about cool stuff happening in Silicon Valley about artificial intelligence and hypertext publishing, they call it the web. That was before there was a web. And so I went out to Silicon Valley.

• 4:02Speaker 02

  I worked at Lockheed because it's the first job I could find for a couple of years. And then I switched to NASA. And so for nine years I did computer research in Silicon Valley. And then I've been doing on the institution design as a hobby on the side. I finally decided to try to make a career out of that. So I went to Caltech, where initially I planned to do experiments because we, this is this respect experiments, but then I learned that you really can't do experiments there unless you have a theory. They don't just test institutions, they test a theory of an institution. So I learned to do more theory. I learned lots of things that I didn't realize social scientists know because businesses don't believe social scientists know anything.

• 4:43Speaker 02

  And uh even computer scientists, I guess, don't believe that so much. And I spent, you know, four years getting a PhD at Caltech in social science. I did a two-year health policy postdoc at UC Berkeley. And then I got my job here in 1999, where I've been, you know, for 21 years here. And along the way, I, on the side, did some things in The Great Filter, sort of, you know, Aliens, and Extraterrestrial Life, and dabbled in a, I have a book called The Elephant in the Brain with a co-author that's classified as psychology. I have a book, The Age of M, that's seen as futurism.

• 5:23Speaker 02

  And so I've really done a wide range of things, more so than academic, academia rewards. Academy mostly rewards you for being very, the best person in the world in a very narrow specialty. And I didn't do that, and I got away with it, luckily. And the thing I'm most known for is prediction markets, betting markets on things, but I'm known for a lot of things.

• 5:47Speaker 00

  So one of my questions is like, you went down so many different paths, right? And you've got a greater level of expertise or experience on those different paths than most, but you kept seeking out other paths to go down or other disciplines. And so what was really the driving force behind building that expertise and experience across so many disciplines?

• 6:08Speaker 02

  Well, I have excuses, and I have perhaps real reasons. So if I stand away from myself and don't look inside myself and just say, you know, why would Mike DePerson do that? I observe that most intellectuals, when they fail, they fail for failing to sufficiently focus. These most ordinary people, when they indulge themselves in intellectual things, they spread themselves out broadly. And even people who are trying to get PhDs, they still are tempted to spread themselves out pretty broadly because it's just really interesting. And so, you know, they, people like to be broad intellectually, and they have to learn to be narrow if they want to succeed.

• 6:50Speaker 02

  So that's the most obvious plausible explanation for me, that I'm like everybody else, and I didn't know it was, I didn't do what was good for me, and I sort of skirted by. Now, I can justify it in the sense of giving some benefits from it. So certainly, one thing I did was to keep asking myself, how important is this thing I'm working on? And can I see something else more important? And to keep switching when I thought something else was more interesting or important, you know, bigger. And most intellectuals, they sort of grab first thing they they seem to have some success at. And they don't ask the question, how important is this compared to other things? And so the work that intellectuals do varies enormously in its value.

• 7:34Speaker 02

  Because people are not sort of surveying widely and choosing the most important, interesting things. They're doing whatever they can grab. And the the world itself isn't doing that survey. It's basically opportunistically, you know, funding people who seem impressive. Uh, I can also say that I think of the strategy of looking for combinations of things and and intersections that are interesting is a useful intellectual strategy that requires that you look at a lot of things. So the only people who can really play that game are people who do look at a lot of things. And interestingly, it has a scale economy of the more things you know, the more intersections you can find.

• 8:17Speaker 02

  And so compared to some other strategies, it peaks later in life. Yeah, strategy of looking for lots of things and intersecting them. I'm still think I'm rising on that trajectory, even though my many of my abilities declined. My overall productivity at finding interesting intersections is still going up.

• 8:35Speaker 00

  Yeah, and and I forget the name of the book. I I think it might have been Range, where it basically talks about this idea of like, uh, being a generalist is actually an advantage, rather than early in life, getting a very specific focus and and becoming a deep expert on one thing. Having that kind of more rounded type approach can serve as a significant advantage later in life.

• 8:56Speaker 02

  That is a good book, and you know, it's it's giving a middle ground and saying people often over specialize. They they believe they should over specialize. So humans are relatively general creatures. You know, compared to most other animals like us, we took this generalist niche, and it's worked very well for us. Uh, it works better when the environments are changing more. So in some sense, generalists should work better and more in the modern world than in ancient worlds that were more stable. The slower things are changing, then the more by specializing you could win by, you know. Picking something out, but um and that's a big issue in our world today. A lot of things change a lot. But I think actually many of our institutions are not flexible enough because they have got stuck in a stable world. I think we're seeing that in the pandemic response.

• 9:42Speaker 02

  And in many other ways, the longer your world is stable, then the more your institutions and habits are tempted to sort of assume the recent pattern. And specialize for that recent pattern. And then when you have a shock and a change, it's much harder to adapt. So for example, in the pandemic, the nations that have been changing the most recently. And that have dealt with problems like this more recently have just done a lot better. Uh, because they're more flexible.

• 10:09Speaker 00

  Yeah. So you've been all over the virus, right? In terms of very early on calling, hey, this is going to be bigger than we think. Uh, this is going to get out of China, you know, a bunch of things that now we know as fact, but at the time. Uh, you were early to see that. Maybe just give us a at a 10,000 foot view like what has transpired since the end of last year to today? With the virus, both good and bad and kind of uh, not only the events that have occurred, but also the responses from various governments.

• 10:40Speaker 02

  Well, so it showed up initially in China. Initially, Chinese repressed news and let it get farther than it should have, but then they switched around and did a strong heroic suppression. And managed to keep it suppressed, not only in Wuhan where it struck, but prevented it from spreading to the rest of China substantially. A heroic and strong and expensive effort.

• 11:04Speaker 02

  And the rest of the world didn't sufficiently keep it from leaving China. Uh, unfortunately, and so, you know, the ideal thing would have been if the rest of the world had just locked down. You know, traveling from China and, you know, testing and things like that, then it would have stopped there. And a great many other pandemics have been stopped at that very early stage. And that's overwhelmingly the most cost effective point at which you you want to deal with it is just to make it stop at the beginning. But many governments failed to to stop it from spreading to the other places. Now, some of the initial places that failed and then, you know, like South Korea, for example,

• 11:48Speaker 02

  they had an, you know, an initial problem outside of China, but then they did a heroic, strong effort and managed to suppress it there. And some other places in the area did, you know, pretty strong efforts of grabbing it initially and stopping it like Hong Kong, Taiwan. But it's a big world. And a key thing to know about a pandemic is when you have any sort of growth process, when you average a bunch of different growth processes, the total really is dominated by the fastest growth process of the set.

• 12:23Speaker 02

  So that it doesn't that much matter what the median place does. It matters what the worst place does. And when you add up the total, the worst place dominates. At least unless you can sort of isolate them from each other and, you know, mean that when one hits a maximum, the other places don't get affected. So.

• 12:44Speaker 02

  But so it's a big world that's spread around the world. And some places did better than others, but of course, the basic problem is wherever does worse, that makes a lot of cases. Those people go elsewhere and that spreads. And so that's what we've seen so far is that even though many places have done very well, the worst places have dominated and now the US is kind of one of the worst places. We're we're the guilty party and not overall of the USA, New York and a few places. They dominated the total effect because again, it's not about the middle place and how well it does. It's about the worst place and how well it does, how badly it does.

• 13:23Speaker 02

  And so, um, you know, initially, uh, public health officials, when they saw it escaped China, they said to themselves reasonably, I guess that's it.

• 13:37Speaker 02

  Uh, this is gonna go most everywhere. And that's what I said. I said, shit. I even said it, I thought, as it seemed to be escaping from Wuhan. So I guess I was a little early. I was, I said, look, it's going outside of Wuhan. That's it. And the rest of China did manage, uh, surprising to me to keep it down, but then it went to the rest of the world. And so, public health people said, that's it. I guess we'll have to deal with this going most everywhere. Public health people have a lot of experience with diseases and basically, nothing has ever spread that far and then been contained.

• 14:12Speaker 02

  And so, they were perfectly reasonable to expect that that would happen here. But when they've said things like, we need to flatten the curve, spread it out so that there's not too much impacted any one moment for the medical system and the rest of our systems, then the elites in the world said, no. They said, no, no, this has to be contained. So they had suddenly woken up and decided that even though initially they screwed up. And so, in the United States, the health officials really badly screwed up, CDC, in terms of uh, preventing testing, uh, preventing masks, a whole bunch of things that you can read about, how badly we screwed up for, for a month there.

• 14:49Speaker 02

  Uh, but then, once everybody was, once the elites in the world were concerned about this and talked about the concern, everybody said, no, no, no, we have, we can't give up on this. We must contain. And so, ever since then, uh, you know, that, that was, you know, three months ago.

• 15:07Speaker 02

  Um, the, the elites in the world said, we must contain. And they started writing white papers and doing analysis and saying, well, look, some places have contained, you know, South Korea, Taiwan, Hong Kong, and, um, many places have contained. And here, we're not doing a good job, but we ought to be able to do a better job, because how could they be better than us? And they listed out, well, we need a lot more testing, and we need a lot more ways to do tests, we need a lot more tracing, and maybe apps and different, we need to hire more people, and we need more rules about isolation, and we need more masks, and, you know. People went wild, talking about all the things they thought we should do, and all the different big things, if we did them, then that should be enough, because hey, there's other places that are succeeding.

• 15:51Speaker 02

  But the fact is, we haven't actually gone very far in those directions. For whatever reasons, we haven't. Now, you know, one way to think about this is to say, look, policy is complicated.

• 16:05Speaker 02

  And it's less about each little piece than having good packages. So different countries in the world just have whole different governance systems and whole different strategies and whole different resources. And I would think if you saw some places are working and other places aren't, and you wanted to win, your best strategy would be to pick the package that works somewhere. And just slavishly copy that whole package. I mean, that is how innovation tends to work in the world. If you think, you know, some company like GM is successful and you don't know what about them is successful, you just try to copy the whole cultural package, all the little pieces, and you think you're gonna try to be like that.

• 16:40Speaker 02

  Unfortunately, that's not what people did. They didn't say, let's copy South Korea, or let's copy Taiwan.

• 16:48Speaker 02

  Or Wuhan. They said, we here in our different place, we're gonna do our, our package that makes sense to us. And they all different, different packages, and now they're all in different places. And even today, when we see our packages are working so well, we aren't tempted to do a radical, let's now go to South Korea package or something. And then even that, it might not be feasible anymore in the sense that we might be too past an early stage where certain things work at the early stage, but didn't work later. And so we still got all these people saying, if only you would follow my profit, we would all succeed. But they're not all behind something. So initially, back in, you know, February, March, the elites all got behind this story. We're all going to lock down, and we're all

• 17:30Speaker 02

  gonna beat this thing. But they let everybody say, well, you choose your own lockdown, you choose your own style and your own everything. And now, if they would all say, no, we need the South Korea package, or whatever it is, and all get behind that and say, and here's the budget, and we're gonna spend a lot, and, you know, we're just gonna do it, we would all probably go along with it, and that might work. But we're not. The elites are not like behind one common package. They're spread across a dozen different like priorities and what things we should be focused on. And there doesn't seem to be much of a movement to change the overall policy.

• 18:06Speaker 02

  And after this initial peak, we're going down a bit, but of course, the problem is we're not going down very fast. So I mean, the two key outcomes for a pandemic are it spreads and everybody gets it, everybody gets exposed, or it's contained and squashed down to a low enough level that then the traditional methods of test and trace can keep it down. That still can be expensive, but those are the options, but hanging out in the middle doesn't get you anywhere. And in fact, if you spend a lot to hang out in the middle, you're doing worse than going in either direction.

• 18:43Speaker 00

  For sure. And so I guess that brings two questions, right? One is, do you think what South Korea, Taiwan, and other countries who have, you know, done quote, quote, a good job, is it sustainable? Can they continue to do that? Or do they kind of expend all their resources and can't continue?

• 18:59Speaker 02

  That's still an open question. That is, you know, if the whole world contained it, then we could all rest and have it be contained and then open our borders to each other and trade and travel, because all the other places would also have contained it. The problem is, again, when we have variety and it's dominated by the worst, then even if most places have contained it, then if the place, if places haven't, and then it, you know, goes wild there and they get a lot of people affected, then you have to close your borders to them and all the people that they were be open to

• 19:39Speaker 02

  until some strong treatment is. So obviously, many people are hoping for a strong treatment like a vaccine, but that probably many years away and may never come. And so you have to be prepared to do whatever you're doing for a long time. So we're facing this prospect that, say, there'll be two kinds of nations in the world.

• 20:01Speaker 02

  Excuse me. The ones that have contained it and the ones that haven't. And they can't be opened up to each other, right? And we'll have two groups of trading and traveling partners. And these ones who are locked down, they're the ones afraid of the other side. And the question is, how long can they maintain those borders? Especially, say, in the United States. I mean, between countries, it's easier to imagine maintaining borders like Australia or New Zealand, islands far away. You can manage those borders because we have traditionally managed national borders. But within the United States, say, it's really hard to imagine California putting up the borders. So those people from NewYork.com, etc.

• 20:46Speaker 02

  So then what can California do if California manages to keep cases down and New York doesn't? Unless they can find a way to put up strong borders, then they're going to be vulnerable to the others. And that's the same as true through the rest of the world. I mean, we've shut down trade a lot and we've shut down a lot of travel, but how long can we do that? We are really doing a lot of damage to society and the economy with the shutdown.

• 21:08Speaker 00

  Yeah, and I guess that then brings the question of, you know, we now know to some degree what's worked and what hasn't up until this point. There's definitely open questions about moving forward. But if we had the benefit of 2020 kind of hindsight, what should the US have done differently to prevent getting in the situation that we're in?

• 21:30Speaker 02

  Well, so that's just, you know, a standard thing. Obviously, if we had taken it seriously early on, we would have just shut down travel from the, you know, infected areas. And of course, built up resources, including testing and not overregulated these things, which is what hurt us right at the beginning. So, I mean, I think the consensus is pretty clear on what we did wrong. And what we could have done with hindsight, but maybe, you know, it's not so helpful now.

• 22:00Speaker 00

  And then you've been a big proponent of kind of this Plan B in the finance or Bitcoin world, that Plan B is considered Bitcoin, but in the pandemic world, this Plan B, and I may pronounce this incorrectly, but it's verulation?

• 22:15Speaker 02

  Variolation.

• 22:17Speaker 00

  Variolation. So explain what that is and kind of why you've been talking about it.

• 22:23Speaker 02

  So we've been talking so far about these two scenarios. One, you keep it locked down until you squash it enough that you can manage that, or it goes to most everyone. So it's been looking bad for a while about the first option, the ability to keep a squash down, although it certainly is everywhere. So what happens under Plan B where it goes most everywhere? The question is, how can we minimize the damage there? What can we do? Sorry, I'm just got some reason I'm thinking of my throat.

• 22:50Speaker 00

  You're fine. We've got all day. You can drink as much water as you need.

• 22:54Speaker 02

  Anyway, so if it goes most everywhere, how can we minimize the damage? So there's a number of considerations there. The first standard one is don't overload the medical system, spread it out over time. And that means some degree of lockdown, but not nearly as much as if you're trying to squash and contain this thing. But people do overestimate the value of medicine, so that's a bit much. What else can we do? We could make sure that the young were the ones initially exposed more than the old, because they have much lower death rate from this. So we need to get some level of herd immunity, which might be, you know, some roughly half the population. But why not have it be the young half, the

• 23:39Speaker 02

  healthy half, rather than the old and sick half? And that's also capable of dramatically reducing the death rate. And then we could try to make sure that people were deliberately infected, just so we could control when and where it happened. So most of the lockdown is to prevent a tiny fraction of us from infecting others, the tiny fraction who are at the moment infected. But since we don't know who's who, we're locking down everybody. If you deliberately infected some people at a controlled location and time, then you could isolate them there and, you know, use your isolation resources far more effectively. And it also allows you to spread out the medical resources over time. You can choose

• 24:22Speaker 02

  to say do it earlier than the peak or later than the peak, right? So just the idea of deliberate infection, if we're gonna mostly all get exposed, has those substantial advantages, you know, get the young and healthy and get, you know, the control of the isolation resource. But in addition to these advantages, there's apparently a prospect, a very likely prospect, that there are big advantages in how someone's infected. So for most viruses, we know there is a dose effect. That is, the initial dose that you get of the virus matters a lot. So, for example, if there's a droplet in the air, how many viruses are in that droplet? Or how many droplets do you breathe in the first few minutes where you're getting infected?

• 25:04Speaker 02

  If it's a tiny droplet with very few viruses, that's a low dose. You know, you touch a door knob an hour after somebody else touched it, then that might also be a low dose. But if you get infected, say at home, by kissing your infected spouse, you're gonna get a big dose. And we have a lot of data on other diseases that show low doses can have much lower morbidity and mortality, i.e. you get hurt a lot less. And in fact, before we had vaccines, we were using variolation, in particular for smallpox. So, for example, in the United States, during the Revolutionary War, US troops in Canada were hit by smallpox

• 25:48Speaker 02

  and devastated. And that's why, basically, Canada's a separate country, because those troops couldn't fight the war there. George Washington was worried about this, and even though Continental Congress had outlawed variolation, intentional infection with low doses, he ordered it for the US troops anyway, in secret, so that the enemy wouldn't attack while they were sick. And basically, this reduced the death rate from smallpox from 20 to 30% down to 1 to 2%. And that allowed us to win the Revolutionary War.

• 26:20Speaker 00

  Is the worry that if we did this, there's too much societal pressure or like the headlines of, hey, the way to solve this is to actually intentionally infect people with low dosage? And we just can't as a society, even if the science shows that it's effective, we can't get there because there's so much fear and uncertainty in kind of the press? Or do you feel like it's actually viable that we could do this?

• 26:48Speaker 02

  So, there's a difference between we all deciding to do it together as a joint policy enforced and some of us deciding to do it because we want to. So, we don't, there are many problems with the pandemic that we need to do everything together. There are collective problems, but this isn't one of them. If we have a low dose way to get infected and we allow some people to get infected that way, that helps and we don't have to require them or even agree together that we should, we just have to allow it. So, and in fact, people are, in a sense, allowed to get infected, but the problem is

• 27:29Speaker 02

  the people who would supply the infection aren't allowed to supply it because of regulations of drugs and medical ethics experiments. So, actually, the current limitation is that we would just need a hundred or so people to just do a small trial and we can't get medical ethics approval to do those small experiments because the people who run ethics experiments say this is unethical because in their calculation, the benefit to society of finding a way to lower our death rate by a factor of 10, it doesn't count. The only thing that counts is these people might by getting infected, get sick and get hurt. And that's just the rule.

• 28:07Speaker 00

  But in that trial, those people would be opting into this, right? This would not, to your point, this would be-

• 28:12Speaker 02

  But that's still not allowed. So, medical ethics calculations say we're not allowed to let people volunteer. Now, of course, that's against our practice in the military and for firefighters and police. There's lots of people in our society who we allow to take risks for us and we pay them. But the medical ethics interpretation is that's not to be allowed of people who take medical experiments. That's just their decision of what ethics is and they are not budging in this crisis.

• 28:37Speaker 00

  I mean, it sounds pretty ridiculous, right? I immediately thinking of, I saw a video online in a prison where prisoners were basically gathered in a circle. And what was being described, and of course, we don't know with 100% certainty, but was being described in the video is that they were, one of them was positive for coronavirus, and they were actually trying to infect each other so that they could get out. Right? Which sounds pretty ridiculous, but basically what you're talking about is a much more medically-driven or scientifically-driven version of that.

• 29:10Speaker 02

  Right. So the key problem with people just getting infected in that way, first is they're not getting a low dose or trying to make sure that they're getting a low effect, but also they're not being isolated and preventing it from spreading. So what I recommend is something that I call the hero hotel. You go there, you pay to go there, and then they immediately infect you and keep you there until you're recovered. And so you're not inflaming the whole pandemic, you are just getting this treatment and getting past it. Now, you know, people may well want to do that for many reasons. So not just wanting to help society to get us all past this, but they themselves could get back to work afterwards and get back to socializing and we get back to their lives.

• 29:54Speaker 02

  And, you know, there, many people are really quite frustrated at being locked down. So I've heard many people say informally that they sure wish they could find a way to just get past this and they'd be willing to take some risks. But what we'd like to do is to have them have a much lower risk by doing it deliberately. And all we need is some small trials. And see, unfortunately, the problem is if people could sit in a circle with an infected person and try to get them, that's legally allowed, but we are not legally allowing doctors, say, to do something like this because the treatment hasn't been medically approved. And as a company, you couldn't sell people something that let them be affected with a low dose because that hasn't been approved by regulators.

• 30:36Speaker 02

  And so we're in this sad situation where you'd legally be allowed to do the worst thing, but not the better things because, you know, we're holding basically these companies and professionals to much higher standards than we hold individuals.

• 30:50Speaker 00

  Yeah, and this, I think, goes to some of the opening the economy conversation, right? Of like, I've for a while now, I've been saying, look, if you're sick, old, or have pre-existing conditions, stay inside, open up the economy, allow people to go back on a optional basis, protect the people who aren't comfortable doing that, right? So don't let them get fired, don't let them get kind of docked pay or anything like that. But ultimately, use like influential leadership to get people to go back to work by opting in to doing that, understanding the risks that they're taking.

• 31:20Speaker 02

  Now, you can understand how people who think we're going to contain this are terrified by that. They say, no, no, no, no, you're accepting the idea that this is going to go most everywhere. And of course, they may well notice that we don't very well isolate the old. So, you know, with a strategy like that, there's this key choice, how much to isolate the old. I mean, if you are an old person living with the young, do we make you move out, move somewhere else with only old people? Those are completely reasonable questions. But nevertheless, I think the key point is that that even saying that suggests to them that you have given up on the the strategy of containing this, which they are very emotionally committed to. So I like this analogy of the monkey trap.

• 32:02Speaker 02

  So there are places in the world where people try to catch monkeys and probably eat them. And to catch a monkey, one way to do it is you take a gourd, which is a empty shelf of vegetable, and you put a nut inside it, which is a thing monkeys really like, and it rattles around, and the monkey sees that there's a nut in there, and the monkey puts the hand in the gourd, puts the hand around the nut, and tries to pull the hand back out, but now can't get it out the neck because their hand is bigger with the nut. And apparently, they won't let go of this nut, and you can catch them and eat them. Now, so that's this metaphor for things that are so attractive that by trying to grab onto it and not letting go, you can suffer a lot more damage than you would if you just let go of the nut and say, I'm not going to get it.

• 32:46Speaker 02

  And so I fear that's what's happening and going to happen here with containing COVID. You know, this hope of containing it is so attractive and so emotionally compelling, and you feel moral, right about that, and those people who are doing it seem like evil capitalists who only care about money or whatever, that people will not let go. And of course, that's going to cost the most in the places where we aren't doing very well at containing it, but are doing well enough still to give you hope.

• 33:16Speaker 00

  Yeah, it's super interesting. How does this affect institutions? Like, you spent a bunch of time thinking about institutions in our society. How does the virus? You know, my inclination this entire time has been the virus kind of exposed a lot of shams in society. But how do you see institutions, one, weathering this storm? And then, two, coming out of it on the other end?

• 33:42Speaker 02

  Well, so institutions are really pretty stable most of the time. And it requires either a big crisis and where people lose faith, or some big opportunity and an entrepreneur like wishes for that opportunity in order to change institutions, right? So, I mean, our taxi institutions weren't in a crisis, but Uber still decided to do a revolution and that was more opportunistic. But other times, like World War I or II, after a big crisis, we say, what did we do wrong? And we try to reform some institutions. So this crisis will certainly be available as a resource for people who are going to advocate for changes.

• 34:23Speaker 02

  But of course, they're not going to just universally look for the best institutional changes. They will advocate for whatever changes they already wanted and look for some sort of a story to make that connection. So that's what you should watch out for is people trying. And so that typically always has in a crisis. People fight enormously over the story of what went wrong and therefore the story of who should be rewarded and what changes should be made for the better. Those stories aren't always correct. You can look at a lot of historical big changes that were made and the stories that were told at the time. And you go back and you find out those stories were a pretty distorted picture of what was going on.

• 35:04Speaker 02

  But they were a compelling enough story that got a lot of people to back a change. So, you know, we should watch out for people telling sloppy stories about what went wrong here and therefore what fixes should be granted, right? Like, I think some people even say, well, if we had single payer healthcare, then all these people out there losing their jobs who still have healthcare, that's why we need some single payer healthcare COVID shows. And you go, that's kind of a weak connection, right?

• 35:38Speaker 02

  But people will be eager for these stories.

• 35:41Speaker 00

  And that really brings us to what I think is one of the biggest problems here is those stories are being based on data that are questionable at best, right? So, you know, I continue to ask people, what percentage of the American population is infected? We don't really know, right? We've got guesses, we've got estimations, but we don't know. Even the death rate, there's people who are arguing it's undercounting, there's people who are arguing it's overcounting, right? And so, how do we make good decisions with maybe inaccurate data or questionable data? And then at the institution level, when you layer in all of the agendas that these institutions have, how do you kind of think through that decision-making process?

• 36:25Speaker 02

  Well, so, those of us who specialize in designing institutions, try to come up with institutional variations that are robust to these details. If you need to know these level of details this fast and this precisely, you're kind of lost in needing to do institutional changes because you rarely know that much, that detail. You're looking for robustness in your institutions. And that's exactly what we should be doing here. We should be not trying to make these institutional choices depend so much on these details. We want to say, so for example, you know, early on, the CDC really screwed up the tests. You might say, well, what's this robust solution to that? The robust solution is to not have them so much control over the tests. To let a lot of different people do different tests

• 37:08Speaker 02

  and, you know, some of them go wrong, fine, but that's, you know, a robust solution relative to saying, well, who should run the CDC next time to make, and what methods should they use, what procedures should they have in order to decide this better? Well, that's not a robust solution. That's saying, that's trying to make a choice that depends on a lot of parameters. Who should be put in charge? Well, that depends on a lot of different things about who they are and what the problem is, right? I don't want to solve our problems in the world by agreeing on who we should put in charge, right? So, for example, a lot of people want to blame this all on Trump. And there's no doubt, Trump has a lot of things to blame for, but if you're going to solve the problems we've had with COVID by replacing Trump, I think you're missing the larger, more institutional problems. You

• 37:50Speaker 02

  know, replacing any one person just can't do that much of the fixing.

• 37:57Speaker 00

  Yeah, it's super interesting. One of the other ideas that you've had that you've gotten a lot of notoriety for is work on prediction markets. And I think you've actually said that prediction markets is out of all of the ideas that you've brought kind of forward is your best or your favorite. Can we talk just a little bit about the work that you've done there and then we could talk through various aspects of prediction markets kind of in practice?

• 38:23Speaker 02

  Sure. So the key idea here is that when we make bad choices, it's mostly because we don't know they're bad choices. Or more fundamentally, we don't all together know they're bad choices. So sometimes we make a choice that

• 38:51Speaker 02

  is of us making these bad choices. So the fundamental policy problem is how can we collect the information we all have about the consequences of our choices? So that we can all pull it together into a form that we can share and use. And if you look at the various kinds of institutions we've ever used in the world to collect information and share it together into a consistent form that we can use together. The institution that seems to consistently work the best is speculative markets, betting markets, stock markets, currency markets, commodity markets. These markets have consistently taken, given people the incentive to find things out, bring the information they have to bear to this center, which is the market.

• 39:36Speaker 02

  Include their information in the current market price and then allow that current market price to be information spread everywhere that tells us all what we all know. So we've had many tests where we have had a betting market or speculative market price compared at the same time to other institutions that we often use like committees or expert surveys or all sorts of other mechanisms. And basically, we have many dozens of these comparisons and consistently what we find is that either they're both about the same.

• 40:11Speaker 02

  In terms of the accuracy of their forecasts, or the betting market is substantially better, more accurate. That's the consistent data we have. Now, we have some good theoretical reasons for understanding why, but I don't want to make the pitch here to be based on that theory. It seems safer to make the pitch based on the empirics. Consistently, these betting market prices beat. Now, you can understand in the sense that what they do is they offer a challenge to anybody. They say, "Look at my price. If you think this is wrong, come fix it and you will make money." And that's not only true with respect to sort of base things you might know about a factory burning down or something else. It's about the patterns, like, do prices go up on Mondays too much?

• 40:55Speaker 02

  Do they go down when it rains too much, etc.? And any pattern you can find where you can see a mistake in the market, you can make money by betting on that pattern and making it go away. And that's the powerful incentive that speculative markets offer. And in general, we've had speculative markets just on the random topics that have arisen, like the price of gold or IBM. But we can make a betting market on purpose on a topic you care about, say even COVID infections, and then get the information on the question you care about. So, for example, if you have a project with a deadline, you want to know, "Will I make the deadline?" Typically, if you ask people on the project, "Will we make the deadline?",

• 41:39Speaker 02

  what you get back is a politically correct response of whatever the leader wants you to think, which is typically, "Yes, we'll make the deadline." And if you say something else, you might get punished for, you know, by the person running it for violating their dogma. But if you have betting markets where people can bet anonymously, then you tend to get the accurate forecast. So, we consistently see that people get accurate forecasts and more accurate forecasts when we have betting markets on things. And there's an enormous potential for this to be applied across society and institutions, especially through what I call conditional betting markets or decision markets. And I've given the name "futari" to sort of governance that way.

• 42:20Speaker 02

  And the key idea is that you can bet on the outcome of a decision, conditional on which decision is made, and betting markets on that can basically tell you which decision to make. So my favorite example is firing the CEO. So you have a company, you have a CEO. Should you keep the CEO or should you fire him? Well, usually a board makes that decision, but that's relatively political and it seems like we make a lot of mistakes there. So the idea is you have a stock market where people trade on stock. And then you have a stock market where you let them trade on the stock conditional on whether or not we keep the CEO. We have two prices, the price of the stock if we keep the CEO, the price of the stock if we don't. And the difference of those prices tells us, should we keep the CEO?

• 43:03Speaker 02

  And that would be a straightforward way to give us advice on a key decision and we could do that everywhere. And so the bottom line is we have a lot of data on prediction markets in many different contexts. Showing that they basically work. We have a lot of innovations of better ways to make them work better and new contexts. We have a lot of tests of those, but what we don't have is a lot of adoption. And the key problem is that they are very politically disruptive. So for example, if you think about this person with a deadline and wanting to know if he'll make the deadline. He can make a prediction market on the, on the project and that will tell him whether he'll make the deadline. But the person running the project cares more about having a good excuse if he fails than lowering the chance of failure.

• 43:48Speaker 02

  And so typically a person with a project deadline. Their best excuse if they fail will be to say, well, things were going along fine. And then at the last minute, something came out of left field. No one could see him coming, knocked this flat. It's so rare and strange, it'll never happen again. So there's no need to hold anyone responsible here. Let's just continue on. And the person running the project wants that to be the excuse. And their boss and even boss's boss wants that to be the excuse because they all would look bad. If they were in charge of a project that failed for, you know, a reason that could have been prevented. So if you make a prediction market on this project, you could even do conditional versions, which will tell you how to make the project more likely to make a deadline.

• 44:28Speaker 02

  But the key problem is if the market says you won't make the deadline, it'll tell you long in advance consistently. And then you can't have this excuse that at the last minute, we were knocked by something out of left field. Everybody can say, look, the market told you, you were going to fail. You didn't, you didn't prevent the failure, you're at fault. And that's an example of how in our world, political disruption of prediction markets tends to make people not want to adopt them. That is, most people running projects and running organizations care more about defending themselves from blight than they do about making the overall organization work well.

• 45:06Speaker 00

  Yeah, this reminds me a lot of the book Super Forecasters. And so would it be fair to say that you think the stock market is a prediction market as it exists in today's form? Or does it not fit that definition?

• 45:18Speaker 02

  It does, but the thing it's predicting is not very easy to interpret. You know, it's like if aliens had a betting market and you could see the prices in the betting market, but you didn't understand what the words were on the bets, then you wouldn't be able to make much use of it, right? So the stock market is a bet on the value of that company, but that's a complicated thing. And so it's hard to apply that insight and information to other things you care about. So the more that we can make betting and speculative markets more directly on the things we care about, including especially the decisions we make, the more useful we can make them.

• 45:56Speaker 00

  And how does this get applied to government? Obviously, that is like the ultimate decision-making machine that probably has the widest impact on a citizen's life. How do we incorporate the ideas of a prediction market into that bureaucratic process?

• 46:12Speaker 02

  Well, mechanically, it wouldn't be that hard to do. The harder problem is, do people want to? So what I recommend is that we start with small-scale experiments and work our way up. So instead of trying initially to apply prediction markets to government, we apply them to small clubs, small companies, small organizations, and slowly show through a long, you know, detailed track record that this works. And in each case, try to entice neighboring organizations and slightly bigger versions to copy what it seems to have worked at a smaller scale. And then eventually, we could get government. So as you may know, government isn't usually

• 46:55Speaker 02

  the initiator of a lot of innovations in society, but it tends to belatedly copy them when something works well enough elsewhere. And so that's probably the best route to produce innovation as government is to produce similar innovation in the private sector and then shame the government into copying. But in order to inspire people about what should be eventually possible, I do sometimes describe government applications to give you a sense of just how far this can go. So for example, the simplest thing might be just, there are many government projects with budgets that lie. That is, there's an official budget, but then later on, they're going to come back and ask for some more money. And they didn't admit ahead of time, they wanted that extra money, but they kind of knew. So you can have a betting market on the budget for

• 47:40Speaker 02

  a project that says, well, eventually, how much will this cost? And you could even have that conditional, if we adopt this project, how much will it cost eventually? And now we can get a more honest budget on how much things will cost. And of course, people who are pushing these projects now don't want that to happen because they tend to want to convince us to adopt their project by believing it won't cost as much as it really will. You know, freeways cost more, and you know, all sorts of NASA projects cost more than they all initially announced. So that's just one simple example of how you can use it. But these conditional versions of the market would let us make big key decisions. Like, you might justify building a stadium by saying, oh, look at all the revenue we'll have if we have a stadium. But we might

• 48:23Speaker 02

  say, well, if we build a stadium, how much revenue will we really have over these coming years? And then that could be more disciplined, our choice of do we build a stadium? We could even do this at the, you know, presidential election level. We could have betting markets and say, there's the two, you know, Republican and Democrat, you know, what are the outcomes you care about? Pandemic deaths, war deaths, GDP, you know, stock market, whatever it is, even international respect, we could have betting markets in each of these things conditional on which candidate is elected.

• 48:57Speaker 02

  Actually, today, there are betting markets for which nominee should each party produce if they want to win the election.

• 49:07Speaker 02

  So there are betting markets on who will win the election and also on who will be nominated. And you just have to divide those two numbers to get the probability of being elected if nominated. So those betting markets could be much thicker and much more accurate. And then they would give a much stronger message to each party of who to nominate. Of course, the parties are pretending that they really want to win. But often, the insiders care more about who's nominated than who wins. So they may not welcome such things.

• 49:37Speaker 00

  Yeah, it feels like there's this very direct conflict between what the math and the scientific approach of prediction markets would tell us works. And then the elite, powerful people who have realized that if they can actually prevent the efficiency and things like prediction markets from taking hold, there's an opportunity for arbitrage, which allows them to hold on to power and to hold on to decision making.

• 50:11Speaker 02

  So this is, yes, but it's far worse than you say. This is true in almost all the organizations we participated, from your homeowners association to your firm, to your church, to schools, and all the way up to, you know, states and nations. In all of these contexts, leaders present themselves and justify their actions in terms of information and, you know, analysis of the consequences of actions. So in all of these contexts, the perceptions of which actions have which consequences is always very political and it's always manipulated in order to help powers that be win over their rivals.

• 50:56Speaker 02

  And that's just the nature of all the organizations we're part of. So say, most CEOs even might present themselves or managers as a scientific analyst. They've got a spreadsheet and they're calculating the costs of this and the benefits of that. And they're talking to people that get more numbers to put in the spreadsheet. And that's their job is to make the spreadsheet and figure out what to do. That's the presentation for most managers in terms of what their job is. And so that means they're committed to this idea that I want information. And so if you ask them, hey, do you want more information? They gotta say yes, but they don't mean it. What they want is information they can massage and control to create the impression they want.

• 51:36Speaker 02

  And they are very attentive and careful to to support information that will support their narrative and to resist information that won't. In all organizations, all people anywhere, if you don't do that, you don't last very long at these levels. Basically, everybody has to be doing that. They all have to be giving lip service to information. But in fact, in most organizations, they don't try very hard to collect information. And they don't very much want objective information. They want information that they can control. So so many organizations, for example, hire consultants to give them advice. But they don't just ask, hey, answer this question. They typically give them strong hints about what answers they want to hear. So that they can use that as a supporting, you know, some argument they're trying to make.

• 52:20Speaker 02

  And that's just the nature of all our organizations. So it means that if we could force our organizations to pay more attention to objective sources of information, then of course, they could make better decisions. But and they all give lip service to that. But they all know that in their struggle with their rival, if if they can just allow some out of control source of information, then they might well lose to the rival. So if they have the upper hand on the sources of information, they very much want to control it. I mean, it's, you know, most dictators say want to control the news media and they want to control the schools as major sources of information of the population that could threaten them if they were in someone else's control.

• 53:04Speaker 00

  It's fascinating to me that you get this balance between science and math with power and control, right? And it just feels like we take a approach where those in power benefit from most of the society not understanding the power of a prediction market, not understanding kind of the math.

• 53:31Speaker 02

  But again, this is far more general, like pretty much all societies in history. You know, powerful people have wanted to control and manage information processes, news processes, credentialing processes, authority processes. If the religion is a source of authority that people rely on for information about what's good, then they want to control religion all the way down the line. Science today also, you know, is that science wants to present itself as neutral and independent from all these authorities, just telling you the truth. And sometimes they do that, but less often than they claim. And so the key, you know, question is, how can we create a neutral informative source of information? That's been a long, difficult problem, but prediction markets in some sense solve that

• 54:16Speaker 02

  problem. But they don't solve the more meta problem, how can you make everybody want them or care? Once you know that you do have this independent, objective source of information, who will want it or allow it?

• 54:29Speaker 00

  Yeah. One of the things, so Laurent obviously was kind enough to introduce us and he said to me that you would have fantastic ideas around extraterrestrial life and alien. And one of the questions that I asked is if they believe aliens exist or not. So maybe let's start there, just on how you think about any sort of intelligent life form outside of Earth.

• 54:57Speaker 02

  Okay, so if you just go right to basics, the universe is enormous and it's been around for a very long time. And we're just on one tiny little quarter. So if you just went to basics and said, what's the plausibility there, there might be life, intelligent life even somewhere else, you gotta get, you gotta put it high. I mean, there's just no way you can't say that's gotta be high. But then you have to confront that initial plausibility with some data and then ask, what do we conclude after we've considered some data? So the one in your face biggest piece of data is the fact the universe looks empty and dead.

• 55:40Speaker 02

  Everywhere, everywhere we've seen, I mean, there's a lot of activity out there, but none of it looks organized, intelligent, purposeful. It's just dead on vast, enormous scales. I mean, there's vast energy wasted. Even quasars are sending out piles and piles of energy that just gets thrown away. And so that is the spectacular fact about the universe, which says that all these intelligent lives that might be out there, they haven't reached that level of impact. They haven't reached the scale of doing big things that we would even notice from here. They may be out there and doing big things on their local scale, but they, and it's not

• 56:23Speaker 02

  letting, and that's puzzling because there's been billions of years here. It's not like there's been a time shortage. They've had plenty of time. I mean, most likely there were aliens many billions of years before us, right? You know, we, it's now 13 billion years since the beginning, 14. And so, you know, they were just four billion years ago, and that's lots of time. So what the hell? Why is it all so dead and empty? So we struggle to come up with some stories there, but that's gotta constrain what, whatever they are, whatever they're doing, apparently they have not reached this massive scale. And it's a, you know, you might think, well, they have this rule that they don't mess with

• 57:06Speaker 02

  things, or they don't want to be, they're hiding, but it's really hard to enforce rules on really large scales, right? You're talking about like way out there, say a whole galaxy that would be filled with aliens and they've got this rule that none of them get, or ever leave the galaxy to go somewhere else and mess with things. Like, how, how could they enforce like a border on a galaxy and make sure nobody ever leaves? I mean, it's just mind boggling, the sort of coordination and control that would be required to prevent spread and becoming visible on cosmic scales. So, um, but that doesn't mean they aren't there somewhere.

• 57:47Speaker 02

  And it doesn't mean they couldn't come here either.

• 57:52Speaker 02

  But now, if you think, okay, could they be here? Could they be around here? And you say, well, we have to confront again the data point that they're not obvious around here, right? They don't have shining cities in the sky floating over us, right?

• 58:10Speaker 02

  They haven't remade the rings of Saturn, whatever. I mean, they're

• 58:16Speaker 02

  not visible.

• 58:18Speaker 02

  And we are, right? I mean, we are pretty visible. We aren't visible maybe for a thousand light years, but we're visible around here. So if they're around here, they are surprisingly invisible. That's also kind of puzzling. I mean, you know, the things that might lead you to prevent you from becoming cosmically visible don't necessarily prevent you from being locally visible if you're around. I mean, you could certainly have a little city, a little facility, some mines, some production, some whatever, right? Maybe you do fight a war every once in a while and you make a big splash where you slap each other. I mean, you know, these things would be visible. So the question is like, well, how do we understand

• 59:05Speaker 02

  that hypothesis? What, what, I mean, how much do we need to degrade the scenario by saying, oh, that just can't happen? Then we have to start to think, oh, what scenarios could there be possibly? That would be consistent with not only they're not visible on the cosmic scale, they're also around here and not visible on the smaller scale. And then the third data point that you might try to take into account is to say, well, we're not entirely invisible locally because there's all these reports of people who say they see them. And now you think, well, you know, they've been around for billions of years. There are, there have got to be enormously capable technologically because that's the only thing consistent with this age and distance they could have traveled. And they could just, I guess if they wanted to, just completely hide.

• 59:49Speaker 02

  And they could make themselves really visible, but no, they're right on this edge of visibility. And apparently they're smart enough to know that's where they are. And so on purpose somehow, they are just being kind of barely visible. Like, what's with that? What, what, what possible purpose or plan or, or set of constraints could lead to this combination of these three facts? Again, they didn't give rise to vast visible stuff in the universe. They don't even give rise to much radically visible around here, but they're giving rise to a small amount of barely visibility. Like, what the hell?

• 1:00:27Speaker 00

  So I guess part of this is like, should we be more concerned of them finding us than them finding them?

• 1:00:37Speaker 00

  Who gets discovered or infiltrated, they end up being the loser in the engagement? And so should we be worried about that?

• 1:00:48Speaker 02

  Well, again, the key thing we have to be very confident about aliens is that they are vastly more capable than us on pretty much any margin. So if they weren't, they wouldn't really be aliens. So you could imagine like, say there was a dinosaur civilization deep under the earth. I mean, it's a crazy scenario, right? But at least they would have shared a history with us, right? They would have come from a similar origin and have similar back and forth interaction. And then you expect a correlation in their capabilities and ours, their interests in ours, and even the conflicts. But aliens, sort of by definition, they don't share this recent origin and interaction. They just have a very distant origin. And so by definition, they, they just came from far away from long ago.

• 1:01:32Speaker 02

  And that right there tells you their capabilities have to be vastly larger than ours. So if there's, you know, they're vastly more likely to learn about us than we, before we learn about them. There, there's just very little chance that could be otherwise. If there's any degree of them looking around and us looking around, they're going to see us before we see them. Full stop, end of story. There's just, you know, in this contest, there is no contest. I mean, they are just vastly beyond us. The, the only advantage we could possibly have is this apparent advantage that they must think is not an advantage of being noisy and doing stuff and visibly, visibly active when they're apparently really hiding.

• 1:02:11Speaker 00

  It is wild to think through what is out there and and kind of what that impact on us could be. Before we finish up, I always ask everybody, what uh what's the most important book that you've ever read?

• 1:02:25Speaker 02

  Uh.

• 1:02:28Speaker 02

  Well, that's tough because I think for most people. There are times when they read a book and it's really insightful for them. But that was a coincidence about where they were and what they knew. And if somebody else had read a different book at a different time, you know, it would be less insightful. So, for example, at one point I read the book by Hugh Everett about the many worlds interpretation. When I was sitting in a library in University of California Irvine. And that was very influential for me because I had never seen a point of view like that. And that was very dramatic and that really influenced me at the time.

• 1:03:03Speaker 02

  Later in my life, I remember reading.

• 1:03:08Speaker 02

  Herbert Simons, you know, Art Sciences of the Artificial, which explained a whole bunch of views. But of course, you could by now get those views somewhere somewhere else.

• 1:03:19Speaker 02

  Twenty years ago, Geoffrey Miller's The Meeting Mind was influential for me. Had a bunch of concepts about evolutionary psychology. But of course, you could also probably get those ideas elsewhere, depending on, you know, what you read. Uh, so I I really, I think what you read when. Depends a lot on what you already know. So I recommend that say, most people just read a lot of textbooks. If you if you're if you're young and you want to learn a lot about the world. Like textbooks are our standard way to summarize a lot of things and put it all together. If, you know, there's not textbooks on you somewhere, is there a subject? Find a review article that summarizes a bunch of things. And that'll depend on what you already know, of course. So, obviously, you know.

• 1:04:04Speaker 02

  What books, I mean, the Bible, of course, is very influential for our culture. So I read the Bible when I was a kid and no doubt that influenced me a lot. How could it not? Spending that much time involved in this one book or in framing it. But I don't know if I can point to that many things now. But no doubt it's influential. So I guess I'm not that big a fan of the influential book question.

• 1:04:28Speaker 00

  Yeah, well, I couldn't agree with you more in terms of when you read a book is almost as important as what the book actually is. Like, I couldn't agree with that more. Your idea about reading textbooks is really interesting in that I think the more we go into this digital world, the more people want kind of the information delivered digitally. But we forget that the whole point of the textbook is to teach people about a subject. Right? And that's kind of why.

• 1:04:53Speaker 02

  And they can be digital textbooks. Right, but it's more about a systematic thing, right? Pick a subject and let somebody walk you through it systematically, rather than just grabbing a blog post here and a tweet there and, you know, a news article there and just piling these things up randomly. I definitely think people are way too into the news. So the time to pay attention to the news is where it's affecting your life and maybe where your expertise is now at the moment where you can say something to the world. But mostly you want to learn about stuff, not because it's in the news at the moment. You just want to learn about the fundamental important stuff in the world. What's going on and what are the key issues? And at any one moment, you could decide which subject you're interested in and maybe something will spark an interest.

• 1:05:35Speaker 02

  But, you know, use even a news article as a question. Do I want to spend a month now diving into this subject and really learning a lot about it? Is this news article so intriguing that that's what I want to do? That's a pretty high standard. You can't do that for most news articles, of course. But maybe that's the best question to ask about a news article. Is this a hint that this subject is worth just diving way into?

• 1:05:59Speaker 00

  Yeah, I think that's really, really good advice. Where can we send people to get in contact with you, learn more about your work, find your books and everything else?

• 1:06:10Speaker 02

  Well, I have webpages, hanson.gmu.edu. And I'm on Twitter, at Robin Hanson, blog, overcoming bias. And so, you know, you'll get more than enough at those places.

• 1:06:24Speaker 00

  We could use many, many more people like you because I think that there's a lot of bias. There's a lot of paying attention to the news right now. And frankly, I think the world would be a better place if we could get people to kind of go back to first principles, thinking and sound decision making.

• 1:06:38Speaker 02

  One last recommendation that I, people liked when I did the Sam Harris interview, which is to say, try not to have as many opinions. People feel this need to have an opinion on every subject that comes up, people talk about. And most of those opinions aren't very well thought out and so not helping very much. Pick your area of specialty, what you're going to really learn about and know well. Tell us your opinions in that area and then I want to listen and defer to you when it's not my area. I want to defer to other people who specialize in most of the topics. And I want to pick a small set of topics, which are the ones that I think I've specialized in enough to be worth having my own opinions.

• 1:07:19Speaker 00

  I think that is a very intelligent and prescient way to look at the world. So listen, Robin, I really appreciate you doing this. This is fantastic. I think people will learn a lot from this and hopefully we can drive some people to check out more of your work. I think that we could just benefit from more people thinking like you and kind of consuming the content that you're putting out there. So from everyone else, just thank you.

• 1:07:43Speaker 02

  It's been fun chatting and enjoy the rest of your day.

• 1:07:46Speaker 00

  Absolutely. And thank you to Leron, obviously, for making the introduction as well.

• 1:07:51Anthony Pompliano?

  All right, guys. Thanks for listening to that episode. I hope you enjoyed it just as much as I did. My goal is to educate as many people with these conversations as possible. So please go subscribe on your favorite podcast channel, leave a five star rating and a review. These things really help the podcast get higher up on the popularity charts, which ultimately brings more people to learn. Also, don't forget you can go to YouTube to watch each conversation in video format as well. Just search my name, Anthony Pompliano, on YouTube and you'll find our channel with hundreds of awesome and informative videos. Thanks again for listening to this one and I'll see you for the next one.