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Robin Hanson Interview Transcript
It's simplest to think in terms of someone lying or just being sincere. But in fact, we have a whole range in between. So one scenario is that some champion becomes known as a visionary for having introduced this new visionary process. They oversaw the process and then they get promoted and whoever replaces them, doesn't see the point of the team.
If you've got two options and you're finding it really hard to pick between them, that it probably doesn't matter so much. People hate states more, but they like, they like this community of, you know, keynote speakers and Op-ED writers and Davos men. Those people, they like. That's just how humans have done most, everything everywhere always.
Today, we're speaking with Robin Hanson, he's a professor of economics at George Mason university, the author of the elephant in the brain and the age of em and the writer of the blog overcoming bias. He's one of the leading experts in the field of prediction markets, an economic tool that uses a stock like betting system to predict future outcomes.
As you'll see his ideas on self-deception social change, institutions and economic progress are top notch. Before starting the show. I want to give listeners a brief audio essay on the nature of progress. The best way of framing this question is how in the world do we get new things or improve anything? This is one of the hottest questions in economics.
So there are plenty of reliable answers and perspectives floating around, including those of today's guest before moving on, I'd like to remind you of the things that you can do to help out the show. We're not taking donations right now. So the best thing you can do and which really only you can do is to recommend it to a friend or a family member.
You'd be surprised at how willing people are to take your recommendation. And it's a great way to help them find something more interesting and help us grow the show.
You can also suggest new guests by either commenting, giving us a review or by sending an email to contact at, fromthenew.world.
As we'll discuss the biggest differences in decision-making are typically the clearest to make well, the details of what the marginal tax rate should be, what licensing reforms to do and what environmental protections are actually useful are all important.
We're doing a pretty good job of those things. Not perfect, but decent, honestly, any argument where all sides are motivated by rational evidence-based or pragmatically self-interested. Or at a reasonable equilibrium, but you know, by now that politics is far from the land of the reasonable government policies aren't set by careful databased arguments. They don't balance the values and practical circumstances of voters, not even close. They aren't even just set by special interest groups, which while wielding out-sized power can at least be reasoned with. Instead they're often set by emotionally manipulated voters and activists who reject evidence, which disproves their assumptions, or don't even try to make evidence-based claims in the first place.
This includes social progressives in general and racial conspiracy, theorist, and specific who think that widespread unmeasured racism is across the United States. It includes Q Anon shoe anti-vaxxers and the stop, the steel enthusiasts, who are literally willing to go to the point of claiming that huge numbers of ballots were changed and secretly covered up.
You might notice that these are fringe movements. The survey by pew found only 6% of Americans are progressives of any sort. Well, 85% of the states are vaccinated. Nonetheless, these are organized blocks of activists, pseudo journalists, donors, and politicians. Then the answer to the progress studies question is quite obvious.
What is getting in the way of economic progress? The primary obstructor is the convergence of politics and emotional manipulation. The problem is the narcissist neurotics and high end paths who fall for moral panics over and over again. In fact, this isn't just a cool technocratic problem where we can all win.
The vast majority of us can win, but a small, yet incredibly important faction of people will not consider anything other than holding their mentally ill preferences over everyone in society. As a victory in short I'm sorry, progress studies friends, but there is an enemy and they are right there. Let me repeat that.
The enemy of progress is emotional manipulation. It is speech norms that prefer emotional safety to truth. It is the governing by moral panic that prefers visceral reaction to cost benefit analysis. It is the fake elites who are stupid and delusional rather than evil or corrupt. But this isn't just a problem outside.
It's a problem on the inside. As we'll talk about, we choose to judge those who are evil, worse than those who are stupid. What's the difference? Well, evil people want to reward themselves while knowing they're going to do a lot of damage while stupid people think they're doing good. Even if their actions are far, far worse, let's imagine a bit of a crazy scenario from the movie don't look up. The media is heading towards earth, threatening the end of the world. Would you rather deal with evil or stupid? The answer is obviously evil. Sure, they made extort you for a bit of cash, but in the end they will do whatever they can in order not to die. Stupid people on the other hand will destroy even themselves to serve their delusions.
But we're culturally used to the idea of stupid people being forgiven. So let's give them a new name, the delusion class, if you don't think any of this translates to real life, consider the complete failure to do cost benefit analysis under COVID. Instead, what we did was a combination of neurotic, paranoid reaction either to the virus itself or to the vaccine, consider the willingness of a president to let a delusional fringe come all the way to violence in the capital, which by the way, was bad for him. Politically worse than just conceding. Consider the decades of conspiracy theorizing practiced by social progressives, using anecdotes and outright lies to cast the problems of one race as a secretive plot by another leading to riots causing billions of dollars in damage. The delusion class is not fictional.
This is all real life. On the other hand, how much money does it really take to destroy their power? As Elan and Bezos have demonstrated centers of communication and therefore activism are fairly inexpensive compared to the problems they can create. Trump is the only one of those I mentioned who are remotely popular among the Americans.
The others are solely propped up by legacy power in Russia, we call that oligarchy. So buy them out by out the media companies, buy out the politicians by donating more to them as long as they reject social progressivism anti-vax and Trumpism. Yes, that last one might be a bit harder. If we can put together money to do large-scale regulatory reform, we can put together the money to do the obvious thing right in front of us, by the way, this doesn't mean we shouldn't advocate for pandemic prevention, higher legal immigration, chief, renewable energy, or any normal econ progress type ideas.
Those are all great and will be made easier, not harder by removing the delusion class from power directly. Parts of the delusion class will fight those ideas to the death, but indirectly irrational balance of power favors well rational policies. Since the beginning of time, the delusional few have been willing to fight while the rational majority have rarely been that's their immemorial advantage, but rational thinking shows, this is a fight that we can and will win
onto the interview with Robin Hanson, we'll be discussing various ideas focused mainly around economics and policy. This includes his Twitter polls prediction markets, the game theory around them. What happens if someone tries to manipulate prediction markets, his book, the elephant, and the brain, and the nature of self-deception the timeline and event horizon of quote unquote wokeness, the nature of economic progress, how elites communicate the threat of global conformity, the nature of innovation and institutional selection.
The nice thing about speaking with economists is that they always have counterintuitive, but evidence backed ideas that you can discuss and come to reasonable agreements over in this case, these topics are prediction markets and political economy in general, in any case, enjoy the show.
So I tend to dislike bio questions and I've been thinking of getting some good alternatives. I think a great way is talking about your Twitter polls. So essentially you run these polls on Twitter, which at least to me reveal a lot of counterintuitive results about how, uh, how we balance our values. So the first question is what are your most interesting critter polls and what do they tell us?
Well, um, my most interesting ones, aren't the easiest to explain. So the most recent poll I did was I just realized that a lot of spy movies have somebody from a government saying to some ordinary citizen, Hey, you need to help me with this. Why don't you do the following thing? And I thought, well, how many people would actually do that?
So I asked and the poll says, somebody privately shows you credentials of your government's main intelligence. I east by agency, they say is very important to your nations, that you do a particular distasteful and usually immoral act, but you can't tell anyone, do you do it? And the answer is yes, no. And I'm on the fence.
So you might think it's encouraging. Yes. Is only 6% and no is 71.5%, but I'm on the fence is 22.5%. And that's the worrying 22.5%. Because if somebody is in the business of asking people to do things, they're going to be pretty persuasive. They're going to be charismatic. They're going to be good at it.
Okay. So I'm thinking a big fraction of that. I'm on the fence. People would end up doing it. And now we've got a quarter of the population who will take some random government authority and do something. Distasteful tomorrow, presumably illegal because some government official told them to, and that kind of scary.
Yeah. But actually let's, let's ask her a question of the movies themselves. Right? How many of these people, especially those who voted, uh, just straight up know how many people would negatively judge, uh, the people in those spy movies who actually take the task. Right. We're in a spy movie it's set up so that they are good people.
Right? Exactly. So the propaganda is encouraging the sort of thing. Yeah. I think this, this hovers around one of the major questions you try to answer, right. Which is, uh, basically how and why people make decisions. I think this is a very good frame for people, especially people just starting off in the world to kind of go about asking your, your, uh, your self, those questions.
But I guess what's important is like, why did you start thinking about this and what were the kind of events that led up to this? So, I mean the larger context is just that I have 55,000 plus Twitter followers. And it turns out that if I asked a random, weird question like this, I can get in this case, I've got 908 votes so far and four hours left on the 24 hour polls.
So I can get a pretty large sample. Now they aren't representative. Us or the world necessarily, but I still think they're a good first cut at. Um, and so there's just a lot of ways I think about what do people like, what do people want to do? How do people choose et cetera? Where I asked the question, I go, gee, I wonder what people would do.
And having Twitter means I can just take a few seconds and write out the question. Boom, I get answers. So, uh, I've just been enjoying that the last few years, this ability to, you know, so of course in previous years you, you might think of such a thing and then you would have like thought, I wonder if I should do a research project where I take any thousands of dollars in many months to go answer this question and you'd have to, you know, think your question was really important to it's bothering for that.
And now in the brave new world of Twitter, I can just ask several questions like that a day. Yeah. I think what's interesting is the, the process of converting this kind of incomplete information into reasoning about the world, right? Because as you already said is not necessarily a representative sample, but you're looking at some kind of slice of the population you're looking at, what that, what direction or what information that gives you.
And, uh, what would you say is like your process in converting that into something that actually helps you to reason about the world? So, um, a lot of the theories we might have about the world. Uh, just are pretty uncertain about what responses you'll get here and they're uncertain over a pretty wide range.
So just getting a ballpark answer is plenty good enough for updating your beliefs. You don't need a very precise answer. So as you might know, like if you had a us representative sample poll, well, that's not representative of the world and not representative of all history, you're still taking some particular slice.
So I'm taking a slice of like people who follow me and happened to be on Twitter enough to answer the poll. I mean, that's not a representative sample of everything else, but we almost never have representative samples of everything polls. So, uh, if you, you know, the thing is maybe to compare some polls to others, they should be from the same sample.
But if I get lots of polls from the same sample, I can compare them. So again, you know, most of our theories of human behavior, uh, don't actually predicts that people in different times and places do things that different. Right. And so, uh, you're just trying to get the ballpark. So honestly, so this is a very important principle that, um, when you're trying to understand human behavior and your first cup should be just trying to understand the typical average across space and time, uh, try and understand how and why things have changed across time or why people are different in one place than another, or maybe different at some ages than another.
Those are all interesting, but they're the, the next, the second set of questions to ask, the first question is just what are people doing and why? So. I I'm surprised, but it turns out that there's a lot of very basic questions about what are people doing and why that we haven't actually had good answers to. And so just asking generically, any group will give you a first gut.
Okay. Yeah, I think that makes a lot of sense to me. I think in the current, uh, especially academic environment, there's a lot of debate over, uh, how, and to what ends you should be asking questions, right? So there's a debate over what models should be used.
What methods are kind of more, uh, quote unquote legitimate, uh, and this idea of essentially, uh, how, how quickly you should be willing to, to reason to certain points. Right. And I think one of the biggest questions that we face is like, how do we work off of incomplete information? How do we actually do some cost benefit analysis, say when we're looking at government policy and, uh, whether we can actually use those kinds of maybe intuitions or those kinds of leads that we get from a lot of these small damp sample points from a lot of these, uh, immersion points and actually put them together into making a good decision.
So the kind of work I do, uh, is to take. Theory and data and try to match them. And this is actually remarkably, rarely done. That is usually in our world. Theorists are people who take theoretical assumptions and work through their theoretical consequences by Seki, with game theory and other sorts of theoretical models.
And empiricists are people who take some part of the world and they very systematically collect data about it. And the actual task to be done is to match those two, to figure out which theories match, which data in order to select our best theories. Uh, now when I do that task, I do refer when possible to very systematically collect a data and very systematically worked out theory.
But in fact, I find that those are pretty sparse. And so I quite often need to do pretty seat of the pants analysis of what roughly do. I think a theory would predict, even though I don't have a formal game theory model of it. And I often just take observations from the world around me as data, even though nobody's done a formal statistical analysis of them.
And if the pattern is strong enough, that's plenty. Good enough. So for example, years ago, I read this book called the mating mind by Jeffrey Miller and, uh, one of the datums in that book that struck me. People are more eager to talk than to listen. And I immediately was true. Of course, yes, around me and myself, people are more eager to talk than to listen.
And that was a key data for me to try to model and understand the key motives for conversation, but I didn't need a formal analysis of that data. It was strong and clear and not explained. Right. I think a lot of these situations you have, uh, I think a good metaphor, uh, that someone I CA I can't remember right now, uh, set on Twitter was essentially that sometimes when you're, when you're looking at say a wrestling match or something like that, and you see their bone sticking out, you don't need to be a doctor to tell that they're, uh, to tell that they're injured.
So you have these scenarios where you have these incredibly big effects, uh, where even if you're maybe lacking a bit in rigor, you're lacking a bit in the specificity that you would like you have, uh, enough of a lead that I think in any kind of application to the real world, you would be able to make some pretty predictive guesses, right?
So there's this old principle in decision analysis, which says. If you've got two options and you're finding it really hard to pick between them, that it probably doesn't matter so much. Two options are probably close to each other. Uh, the important place to do decision analysis is where your analysis tells you.
There's a big difference between decisions, which means that it won't be as much work to figure out that one is better than the other. So, uh, unfortunately I think academia to a large degree is functions as an institution for credentialing impressiveness, and allowing people to associate with credentialed impressiveness.
And that's means that people look for the hardest things they can find to do and do those, even if the hard things aren't that much more useful, or even any more useful than easy things. Uh, and so like, as we see in this decision analysis, academic, we'll try to take the difficult decisions that you know, where the difference is small.
And they'll try to make a big, complicated decision analysis model of them. And you'll be really impressed with how good they are at decision analysis to come up with that very complicated analysis on which that edges out that AI is slightly better than B, but of course, three other people did papers were B was better than A, and they'll continue to argue about it.
But the most valuable decision analysis is something where nobody's done any analysis and it's an important enough decision. You find that it's pretty clearly better than B and that's the big news and that's valuable and that's typically more valuable to the world. So when I think about insights and ideas, I look for ones where upon being expressed or described, they're pretty persuasive and you don't necessarily need a lot of machinery and, and, uh, support to convince people because a small amount will do.
And that's good because that won't be disputed for decade after decade has different people come up with different variations to argue about it. Uh, you'll make a contribution with somebody will not and accept it and they can go on from there and do the next thing. That's where most progress comes from finding sort of things that people didn't notice or didn't even ask and then asking, or, or noticing them, and then making a pretty clear conclusion where you go.
Yeah. Okay. Pretty obviously this and let's go on, but that's not so impressive because you look at that and you go, well, anybody could have seen that in part because when you make an argument really clear and you make a case really strongly that it looks really. Right. I definitely see this happening in academia.
I'm, uh, I have a math background and I think certainly a lot of the time you have this experience where if you have a complicated solution to a problem, it's impressive for everyone. If you solve the exact same problem with three lines, very, very simple insight. Uh, even though let's say you're in a room with, uh, with your friends or with your colleagues for a couple of hours and they didn't, uh, they didn't solve the problem either.
You, you come up with this insight and, and, uh, in hindsight, everyone says, oh, wow, this was, this is so easy. This is so, uh, this was such a trivial problem. So you don't get credit for having found the, I mean, it's obviously finding the easy solution is a lot of work and impressive. If you understand what's hard and what's easy, but that's harder to persuade a wider audience who doesn't know what's harder, easy.
Yeah. I think this is maybe one of the main separators between science and academia and science and industry. Right? So if I've worked in, uh, a few environments where we're adopting essentially machine learning to a certain tasks, and I think a lot of the time people have this idea of, uh, people are doing cutting edge research, basically the same thing as academia, but on, uh, on say like a very specified.
And this isn't necessarily the case. A lot of the time, a lot of the time it's just using like very elementary methods and I don't want to cast too broad of a brush. I'm sure there are people who are doing exactly the former, but a lot of the time you, you actually don't need that to solve the problem.
Right. And actually like, uh, performs, uh, performs worse. I think I did a tweet sometime in the last five years based on talking to somebody I think in your role. And I think you'll agree, but you could tell me if you don't. They said that most people in firms who think they want machine learning to solve a problem, really just need to do regressions on cleaned up data.
They just really need to clean up their data set and do some simple regressions and that'll solve. That'll be what they need. And you know, they, they mostly want to do the fancy techniques. Cause that's what everybody's talking about and makes them look fancy. Right. I, I don't think I've worked in something like that in my experience, but I certainly, I certainly have friends who would, who would say something along those lines.
I think, especially, uh, especially if it's say like a very long standing institution, you got this, you got this all the time. Right? Right. So, so what you mean about industry is like product oriented industry, as opposed to research lab industry, because research lab industry is actually pretty much part of academia and they have the same norms and goals and priorities there.
Right? Right. So on this point, I think. One of the things that you're say an expert in that you've published a lot and spoken about is, uh, uh, is a way to kind of bridge this gap, right? Which is a prediction markets. So, so what are prediction markets and, uh, what problems do they solve?
So, uh, the fundamental problem, and a lot of our parts of the world is we have questions. We want to answer decisions we want to make, and we have some resources and we have people available who might be tempted to, you know, help us if we spent some of our resources and we want to get them to help us answer our questions. And that's in essence, what a lot of part of the world is. Uh, so of course we talk for other reasons, we entertain ourselves, we show off, we affiliate.
So we do a lot of talking for other reasons, but certainly, you know, an environment, some sort of engineering or practical or goal oriented sense, the value of information is that it helps us make decisions. And so the question is, how can you get people to help now? You know, one mechanism is you find the people with the biggest degrees associated with the category of topics that you are working on, and then you pay them a fixed salary and you say your job is to help us.
And then they do something and whatever they do, you try to use it. Whether or not it's useful. However, you can, that's one very standard approach. These are like grants or, or, or right, or just hired researchers or whatever. But I mean, the key point is, um, your mechanism isn't very incentive compatible, or you haven't thought very carefully about how you're going to get them to actually pay attention to your problem as opposed to helping themselves in other ways.
Um, and we have a wide range of mechanisms in the world. You know, for example, you might go try to look up newspaper articles or magazine articles about your topic and read them. Or you might try to find books related to your topic, or you might, you know, hold an event and ask people to submit papers and present them at your event.
You know, there's all sorts of ways. You could try to help get people to help you sort of answer your questions. Um, and prediction markets are a particular way to answer questions that is especially effective and robust now, uh, for the simplest versions, at least one constraint is you will need to know the answers eventually.
And then the market will tell you the answer before eventually. So you're going to use the eventual to pay off the bets, uh, in order to, uh, create incentives for people to bet accurately in order to tell you now what they will later, but a wide range of problems we're interested are of that for.
Yeah, let's just break this down a little bit, uh, for the audience. So I'm sure most of the people listening to me will be at least a little bit familiar with sports betting. So this is a very good example of say a, you have a bunch of teams, you have something that will, will eventually be sure of, uh, who was going to win the game. Let's say, uh, in the most simple level.
And so you know that in the future, at some point, the game will be done. The score will be on the scoreboard and you'll be able to say, okay, this team beat the other team. Uh, now we maybe want ahead of time. We want to be able to say, okay, well, uh, let's take some information. Let's take a bunch of people who have opinions who have data.
They, and people are competing. They say, oh, this player is playing well. Or these statistics show that this team is going to win well. Uh, how, how do we tell, uh, what's essentially just noise. What's not important and what is going to be, uh, actually good at predicting the outcome. And so two subtle corrections.
One is you can just offer the question and people don't actually have to have any data or have thought about it yet. They just have to have the ability to do that. So once they see your question there, they could turn to the topic, go do a little reading, do a little thinking, and then it gives you some insights.
And so you don't necessarily need anybody to have thought about it so far. And the second minor correction. And a simple betting market. Some people win at the expense of those who lose. And so there isn't necessarily an aggregate temptation to participate. So what you can do is subsidize a betting market in a way that on average, the subsidies go to the people who improve the price.
Who's made the price move from its initial place to its final, better, more accurate place. And that's a direct way to pay people to answer your question. Right. That makes a lot of sense. So, uh, what exactly are the uses of these, these, uh, betting markets? How do we, how do we actually use them to things that we're making?
I think people don't realize how wide the potential here is and how many things you can do with them. So if you thought about, say an election, you might think, well, yes, there's these markets betting on the election. They bet on who will win, but I don't care so much about who it went. I want to know who should win, but a bet on a betting market who will win.
Isn't quite the right thing, but it's actually not that hard to make betting markets on who should win. If we can identify particular outcomes that are connected to, you know, why you're voting. So we can take markets on, for example, the us presidential election, uh, if the Republican wins versus if the Democrat wins, we can ask.
Uh, how will GDP be? How will lifespan be? How will COVID deaths be? How will foreign entanglements in the wars be? How will international respect be? We can ask to predict stock prices, oil prices, uh, CO2 emissions. We can, whatever parameter that you are interested in, and you say, I want the president who will reduce the war or who will increase lifespans or make us have more international respect.
We can make markets specifically, that's say, you know, for each of the candidates, what is their likely outcome for on each of those parameters? And so, for example, you could say, if I want the candidate who will make oil prices go down, well, we have a way to set up a market, such that we get a price, which is the expected oil price.
If the Republican wins and they expected oil price, if the Democrat wins and you can look at those two numbers, and if they're different, you can see the advice that's given to you about who to vote for based on that outcome.
Right. And I'm, I know for sure that you've been asked this question many times, but, uh, this is probably the first time a lot of people in my audience would be hearing it.
But what happens if someone tries to manipulate the market? What happens if someone tries to put a lot of money into betting on the wrong results? Okay. So for context, what we have in mind is there's a mechanism. Like we talked to initially, it's like, somebody wants to answer a question. They have.
They're willing to pay some other people out there to work on their problem. And those other people do work on that problem. But now we're going to add a new participant in this process. Somebody who's trying to influence the decision and presumably not just by giving advice directly to the decision-maker, they're trying to distort or manipulate other people's perceptions in order to influence this decision.
People with agendas who want this decision to go their way. So, uh, all of the familiar institutions we have would face that problem. So if you put together a committee or a conference or whatever you do, uh, these manipulators could use those other institutions to try to distort the final decision. And so manipulation is generically a problem with all institutions, as soon as there's a decision maker and somebody who wants to influence the decision maker.
And then some people who will advise the decision maker. Then of course this, you know, one of the influencer was going to go try to influence these advisors and maybe even become an advisor in order to push the decision they want. So the question is among the many mechanisms we have, which are better at resisting manipulation, and we might be okay with accepting, there'll be some manipulation because all institutions are open to the possibility.
Uh, but we might still want to know which will do the best that. Now it turns out that prediction markets do just about as well as you might possibly hope in resisting manipulation, which is a pretty surprising claim. So, uh, we're going to imagine there's a market where there is some question and then there's some incentive to participate and then people are betting on that.
And we want to compare that to the alternative situation where everything is the same, except we add manipulators. We add people who are trying to influence this outcome. Now, um, we might usually expect that, you know, without the manipulators, we'll get some degree of accuracy for a certain amount of resources, say w you know, on a zero to 100 scale, we'll get it within 5%.
Say if we spent on $2,000 or something, right. And now mean my wonder, oh, when we add these manipulators, how much worse are they going to make our estimates? Maybe the error will go up to seven or 10%. And that might be what you're thinking that you have to deal with, like, okay, if the answer's going to get worse, but, um, I'm still going to get something.
And what else am I supposed to do? I can't, you know, unless I can identify and eliminate all the manipulators that I can have to accept this problem. Right. Well, it turns out with prediction markets when you have the 5% accuracy with the no manipulators, and then you add manipulators, the accuracy goes.
Not down. In fact, the 5% might go down to 3%. Exactly. Because a manipulator has been added to the system. That's what happens in prediction markets. So that should be surprising to you so I can explain, explain why. So in ordinary financial markets, the simplest model of them is that there's two kinds of traders.
One kind of trader is somebody who's has some information or can get it, and they are trying to make a profit. So they're going to look at the price and ask, what does their information suggest about which direction the price should be changed? And then they're going to trade in that direction. Under the expectation that later on, people will realize eventually, and they will make a profit from that differential trait, moving the price up or down, depending on what their information suggests.
That's a informed trader. However, uh, unless there's some other subsidies, like we discussed financial markets can't really exist with only those traders. They need also another kind of trader that we usually call a noise trader. They are just there for some other reason than having better information.
For example, the stock market, they might be saving for retirement. So they put money into the stock market and later on, they take it out because, uh, they retire. So that's annoys trader. They are trading for some other reason than the information they have and they are the ones, the noise traders want to trade it.
So if you're a noise, if you're an informed trader, I'm sorry, the informed traders want to trade against the noise traders. If you're an informed trader and you see that you have information suggesting the price should go up and you trade against somebody else, who's also an informed trader. That means they have information suggesting the price should go down.
You're canceling each other out on average. You're not making a profit, right? So, uh, just to kind of reiterate, essentially you have these two, uh, you have these two competing forces, right? You can think of them as kind of, uh, well, they're not, when, in some sense it's like sheep and wolves. The wolves go where the sheep are.
Uh, the sheep would be happy to go anywhere and they don't need the walls, but they don't even want the walls. But the wolves, when I go over the sheet part, so the informed traders or the wolves and the noise traders or the sheep. So in financial markets, typically the more sheep you have, the more wolves you attract.
And then the more informed prices get sheep, make prices more informed. I E add more noise and the price gets more accurate. You might think that's counterintuitive from other systems you're familiar with. You might usually think you add noise, all else equal. It's going to get worse. But here, the response to the noise is to put in more effort to profit from the noise and so add more noise and it gets more accurate.
So that's a basic, and that that's true in our financial markets. Basically, you look at a market say on the president versus the Senate race in politics, the Senate race has fewer noise traders. So it has less accurate prices. If you look at a big stock price like Google compared to a small stock price, like I don't know, um, Costco then, um, the big stock price will be more accurate because there's more people trading it for other reasons.
And therefore the price more noise, more informed traders come in now. So to get back to manipulation, the last thing you just need to understand is that a manipulator is a noise trader full stop. The reason they're there to trade is not because of information. They have. They have some other reason to try it.
I either trying to manipulate the price. Therefore they are a noise trader, therefore their participation when it's anticipated induces more informed traders and the price gets more accurate. And in fact, this is what we see in the field and in the lab. And in theory, consistently adding manipulators makes the prices more accurate.
Right? So one thing that I've actually been confused. Is that, uh, you expect people to eventually lose money, uh, via manipulation, right? Uh, and yes, the manipulators will on average this money. Right? Uh, so one question that I have is in a lot of these, uh, conditional prediction markets, right? You have one where, uh, the Democrat wins one where the Republican wins they're kind of double ended.
And, uh, only one of these things gets measured, right? So at the, at the end of the day, the Democrat or the Republican, uh, gets elected, uh, you can measure the outcomes. Uh, but what if someone tries to manipulate the end, uh, which they think which after the manipulation becomes very unlikely to win out.
Right? So let's say someone thinks that, uh, someone already has a very large advantage in the election, uh, or in one of these cases where it's even, uh, it's even more dependent on the market itself, right? Where, uh, the choice that actually is made is even more dependent on the result of the market. Let's say someone puts a lot of money into manipulating the end that they hope to eventually tank and that they hope to eventually make it, so that that choice isn't chosen.
And there's no way to evaluate it. Uh, how would the market play out in that case? So, uh, if the decision is made independently of the market, that's the first case to think through then, as a. Option becomes less likely than people have less incentive to trade on that option. So the key idea is you're going to make called off trades.
You're going to make trades conditional on a decision being made. If the decision isn't being made, then all those trades will be as if nothing happened. And so your interest in making those trades is proportional to the chance that that will be the decision. So if you had, you know, in, in, let's say the presidential elections, if you have 10 candidates for president, then if we're betting on the consequences of the president, say for oil prices, then your interest in betting on any one candidate and predicting what will oil prices be.
If that candidate becomes president that's proportional to the chance that that candidate will become precedent. So when the chances 5%, uh, you'll have, you know, that dilution of incentive relative to if it was a hundred percent, uh, and so you will expect more attention and energy to go to the prices which have higher chances of being realized, but there'll still be some energy going to the others.
It'll just be, they'll need a larger mispricing it before they think they see a profit opportunity to go bother, to think about it and, and to influence it. So we should expect larger errors in estimates about the consequences of unlikely choices. Now, uh, that means, of course, if you have a large error in something that large error could suggest that that's a really good decision to make.
So now if we move to the case where the choice is influenced by the market, now we've got a more complicated scenario where there's some tentative, initial, you know, chance of each decision, which is connected to the estimate of the consequences of each decision. But if you have some error and the consequences of a decision, it may happen to suggest that decision is especially good.
In which case the decision maker may become more likely to pick that decision. In which case now the market will pay more attention to that decision and then presumably reduce the error in the prices. So now the current candidates that are seen seem to be the best options. Those, those estimates will be more accurate.
And so, uh, you know, there's less of a problem in picking the second best choice versus the top choice among the two best choices, because those two estimates will be pretty accurate. Uh, but of course you still might make a mistake. But as we said before, when two things are nearly the same, it doesn't that much matter, which one you pick.
So that's not so much. The biggest problem would be from an option that you are not picking, which you are underestimated. That's right. Quality. Right. If you look at something and so that's, that looks terrible and everybody looks at it and says, that looks terrible. And now, uh, it seems very unlikely to be picked.
And so nobody gives it a second glance. Then it will continue to look terrible and then it will be chosen. Uh, but this does give participants the incentive to go look for neglected, uh, options. So if you find an option that seems to have very unlikely chance of happening and a very low evaluation, and you think about it and you go, Ooh, that's a lot better than I thought, well, now you have the strategy of, oh, well, let's bid up that price and let's tell everybody, this looks better than you thought.
And now once the price is up, they will take a more careful look. Now it's more plausible. That will be the thing chosen. And they will evaluate your judgment. Now, of course, probably usually you're wrong. Usually they say, no, that's still is bad. And here's why, and then you realize, oh, oops, I made a mistake.
And you know, then you, you lose on your venture, but sometimes you'll be right. You'll be right that this wasn't neglected option. And then, um, it will rise up and be more likely to reach out to so all of this works fine as long as, um, well, certainly if the decision is random, Or random with respect to the market.
That is if the decision is exotic and correlated right then, uh, there's really no problem whatsoever. Uh, right. The, the main problem can be, if you, uh, you know, when there's more of a connection and now one of the biases you could see that people have pointed out is say among the options, you find that you know more about a than B.
Okay. Right. Well then you'll want to push the market toward picking a, because you have information about it. So the more that you can get people to pick a, then the more that your information will be, get paid for it because you have information about it. Uh, whereas if you had information about B you'd want them to pick B, because now they're paying for information about me now, you know, if you were a monopolist, I E the only person to trade it on a, then you could do more malicious things about trying to trick everybody into thinking.
Eight is great. If you're facing competition where there's lots of other people who can knock you down and say, no, that's not right. That's more like the case of the manipulation, where your attempt to manipulate would be countered by other people equally. When against your manipulation. So, uh, now the safest thing to do, if you're really worried would be, say, have a 1% chance of doing the decision randomly and now have the markets all be conditional on that 1% chance that is have the markets be, if we choose a, and that happens to be chosen randomly, then what's the outcome.
And that we're a market. If we choose B and it happens to be chosen randomly, what's the outcome and compare all the outcomes under the condition that it's chosen randomly. And it's that random outcome that's chosen. Those market prices will be good, reliable estimates of the consequences of the choice you make.
If it's not made randomly, at least if the choice to make it randomly is made randomly. So 1% of the time you randomly say, okay, now we're going to make the choice randomly. And then you do make that choice randomly. And then, you know, it's not a problem right now, you know, you might think, well, it'll discourage the incentive of the traders because only 1% of the time are there trades realized and you're asking them to take on more risk.
And that is a cost. So, you know, against that scenario, you could say, well, let's just move closer to letting it be influential, but let's always have some chance of every option being picked. So if you have a very deterministic rule where. You pick an object, pick an option when it's absolutely has the highest estimate, then you can have more problems with the games.
So just as long as the speculators, aren't sure of which option you'll pick, you can still have decent incidents for the other options. So, so for example, in the presidential markets, you might worry, well, the chance of one candidate being elected might depend on other things that will cause the oil price to go up or down.
So I said, you know, we're going to have a market in what will be the oil prices. If this candidate is elected and that's imagining into the scenario that the president causes the oil prices, but you might think what if some underlying force causes both the oil prices and the precedent now, uh, these market prices won't be necessarily reflecting.
What will the cause, uh, you know, of the president have as an effect on the oil prices. So under that, if you have those concerns, when simple solution is to condition these markets on whether the election is very close, right? That is if the, if the elections within 1%, then which candidate wins is going to be pretty random.
And so it's as if you were picking this, you know, 1% chance of making the decision randomly, that's pretty much what you're doing when you're conditioning on the election, being very. Right. Or in general, I don't necessarily necessarily see that as completely a flaw because those correlations are kind of baked into it.
Right? If you have a prediction market, you assume that's the information people are operating off of. So they're not actually, they're not actually like making a mistake here, right? Th I mean, once you realize how most actual decision making happens, you realize this is a ubiquitous problem in all of the other mechanisms we use for making decision-making, uh, you know, your committees or your advisors, whatever they have, they also have problems disentangling the direction of causation and their advice.
And so whatever incentives you're giving them to make good decisions, they can also be polluted by these problems. So, uh, it's not to say these aren't problems, but it's not at all clear that the prediction markets have at worse issue of the problem than the other things you might do instead. Yeah, I would completely agree on that.
Uh, as I understand it, you've worked with, uh, DARPA for the audience. That's the defense advanced research productions agency, which doesn't just fund military stuff, but a ton of tech projects, a ton of internet projects. I certainly know people who have been funded by them. And, uh, you had a fairly interesting experience there.
So, uh, tell me about what you did with DARPA and, uh, how it went well, uh, around in 1999 or 2000, uh, DARPA initiated a project where they said, we've heard about these prediction markets being useful for them. Show us, them being useful for some military application cause DARPA is about defense. And so they did a call for proposals and I was part of a team that did on that.
And we bid on creating, um, betting markets about geopolitical events in the middle east. And we had proposed at the time to pioneer combinatorial prediction markets, which would allow you to talk about a range of if this country did that. And we did this policy, then what would these other outcomes be to talk about, you know, a complicated set of interdependent events in the middle east?
Uh, so, uh, we among, we were one of two teams that won that contract. And so we began to set up our markets and then, uh, we were ready to start beta testing. I eat to have an initial small set of users to start using the markets. And we put out a call for beta testers asking people to come participate. And that was the summer of 2003.
Uh, and at that point, uh, there was a press conference by two senators, uh, on a Monday morning when it's so happens. Probably not coincidentally that the DARPA DARPA PR person was out of town and unavailable where they accused DARPA of funding, betting markets in terrorist attacks. So our markets were planned to be again, geopolitical events in the middle east, uh, each event, each country's economic events, political events, military events, if it had them, um, that was going to be the topic of our markets, but the accusations, we were going to make markets on terrorist attacks and for evidence for that.
And they use some sort of hypothetical examples we had on our sample web screen when we were calling for participation. Uh, so, uh, in addition to these geopolitical events, we thought, well, let's have some other miscellaneous events that don't fit into a more standard category. And one of those miscellaneous events was that the Arafat, the president of the PLO Palestinian liberation organization at the time would be assassinated.
And another is that North Korea might send off a missile summer. And, uh, this was the basis of claiming that we were going to make a market in terrorist attacks. And, uh, between Monday morning when this press conference happened and Tuesday morning, the DARPA PR person was unavailable, but there were hundreds of news articles about this terrible thing.
And then Tuesday morning, the secretary of defense in front of Congress said that the project had been canceled in that 24 hours. Nobody asked us if the accusations were correct. Uh, and you can see that from the secretary of defense point of view, there's very little to be gained by, uh, defending. That is, it was a million dollar project, not a billion dollar projects there wasn't going to be any special interests defending it.
And, uh, we were accused of violating a moral norm that nobody should bet on death. And there's an interesting phenomenon that when people are accused of violating norms, they typically need to have an immediate response. If they say, I need to think about that, that doesn't look good. There's a sense of which norms are supposed to be automatic.
You don't supposed to need to think about them. You're not supposed to need to calculate things. You're sort of supposed to know immediately that it's a bad thing and you don't do it. So if the department of defense had said, well, w we hear you, we need to look into this thing. You just, you just told us then that would make them look bad.
And so the obvious thing to do is just to immediately kill it and say, it's done. And so that's what they did. And again, we weren't planning on having a market on terrorist attacks, although that's not a crazy thing to do. Uh, the two main accusations that when people complained about it, they didn't just complain, oh, this violates our moral norms.
They tried to pick more consequential, uh, things to complain about. One of them was manipulation. As you mentioned that, uh, the bad guys might manipulate our prices to mislead our prices. And the other thing is that we might be funding, terrorist attacks that is people might do a terrorist attack in order to win Betsy.
So that second accusation would require that, of course, that our markets have a lot of money at stake. So in fact, we were planning on having, you know, participants have 10 to a hundred dollars at stake in the market, which would not be enough to fund a terrorist attack anywhere. But nevertheless, um, again, people were mainly just looking for reasons to, uh, to find complaints so that they could respond, invoke their moral alarm against betting on death.
Right. Those moral norms are certainly very interesting. And I think we're going to just stick a bookmark there, and we're gonna talk about that, uh, uh, quite later, uh, in the podcast when we talk about your book as well. Uh, but I think there is this trend that exists, particularly in the U S where people just don't like betting markets for some reason.
So you, might've also heard about, uh, this, uh, this website, this startup, uh, poli markets, which is essentially, um, a betting market. That's just a general, uh, just a general normal betting market, uh, related somewhat to crypto. And it was recently, uh, banned in the U S it was banned to use, uh, regular money, uh, in a.
In this, uh, in this, uh, betting market, and this is actually quite unique if you just compare it to the rest of the world, there's, uh, there's no such bands on even just like traditional, uh, betting markets compared to the U S so do you have, this has more bans, but, but it is widely band. So, you know, most bedding when it's allowed is heavily regulated, uh, sort of generic free bedding on anything you want is not very common.
Uh, but there are some places to allow it more. So the first thing to step back and realize, uh, all the familiar mark financial markets, you know, about stock markets, commodity markets, insurance, et cetera, they were all illegal centuries ago under the generic ban on gambling. So humans have long just enjoyed gambling and their associates have often disapproved.
And so most societies, uh, through history have limited or banned various kinds of gambling or made it very limited. And that was an obstacle to financial market innovation. That is, uh, financial markets were also banned indirectly by these bands on gambling. So it took a lot of work and time for, uh, to generate consensus, supporting, allowing the exception for.
Ordinary financial markets that is so to allow stock markets, to allow commodity markets, to allow options, to allow insurance, all of these things required substantial lobbying and, you know, trial and error to get people to accept the idea that, oh, that's not gambling because that has a useful purpose. I mean, it is a gambling, literally it remains gambling and in the strict definition, but we carve out an exception and we say, oh, that's okay.
And we still often regulate them substantially. Well, under the rationale of, oh, we need to try to prevent the gambling from happening. So this a version of gambling is quite widespread. And so the problem is just, can we convince the world that there should be another exception? And I think that's interacting with the fact that we are just a lot more regulated than we weren't.
So almost societies had some regulations, but in the last half century, our world has just gone regulation happy and just has a lot more regulations on everything, which makes it harder to try to create new things that, you know, initially seem like they, they are violating the rules, but, you know, try to prove themselves to get it become an exception.
Okay. Right. So, uh, here, I see one of, one of those puzzles that I think that you're very interested in, which is that in many cases, I, I think that, uh, these kinds of situations. Kind of bedding or where it's like betting, but actually productive, possibly is more regulated than just like straight up gambling.
Like it's easier to go to Vegas than to invest in a private company, for example. Right. Well, Vegas is quite regulated. Yeah. But it's still a comparatively easier. Like I, I think that there is like these regulations on gambling, but at the same time, it seems like, uh, there are many specific scenarios where that gambling is possibly more productive, but investment is also heavily regulated.
So, right. I mean, so as you may know, uh, if you want to solicit money from rich people or from businesses, uh, you are allowed to have fewer constraint. If you want to solicit money from ordinary people in the public for your investment, you have to satisfy the constraints of a public investment and that regularly.
Yeah. So the question, or I guess you're maybe disagreeing with my framing. So my framing is that it's easier to, uh, to bet on something like a Vegas or a lottery ticket or something like that than it is to invest in a private company, uh, or do, or say like these in these, uh, conditional betting markets, these prediction markets and that this, this just doesn't make a lot of sense to me.
Right. And you have, you do have regulations in normal gambling is. But it it's, it's just fewer regulations. Right. And I don't understand why there's fewer regulations there compared to where your expected return might actually be higher. So w where is it? You're seeing fewer regulations. So, so there are less regulations and just straight up gambling, uh, and there are more regulations in some of these investment scenarios or in some of these, uh, prediction scenario.
It seems clear that straight up gambling is very heavily regulated, more heavily regulated than the others that is heavily regulated in the United States. Certainly. Uh, so, you know, you can gamble, you have to go in person to a casino and you know, who is allowed to offer a casino is limited, not just wide geographically, but even in those places.
Uh, and you know, what kinds of things you can do there is regulated and, and, you know, lots of things are regulated. It's very, it's quite heavily regulated. All right. So this is just a disagreement with the framing. I see. Um, yeah, there is a chance that I'm just wrong. Uh, I might not be, uh, this isn't a topic that I'm specific, uh, specifically as, uh, educated in, but I think just from a consumer perspective, it's, it's just easier to buy it kind of like straight up.
Uh, straight up like lottery ticket or something like that than it is to do this kind of speculate or do this kind of like a prediction markets as a consumer lotteries are regulated heavily in the sense that pretty much only the state can offer a lotteries. So they don't have a competitive market offer, different kinds of lottery tickets.
There's just the one lottery ticket you can buy that the state offers you and therefore the range of products of lotteries that you can buy or limited by whatever the state chooses to offer as are the prices they offer. And the ability. So lotteries are really quite ferry there, you know, in a sense when the government provides runs a whole industry, that's, you know, an extreme version of regulating that industry.
Right. Right. Okay. I think I see the problem in my, my thinking gear. Uh, I'm just approaching it from a completely, uh, consumer angle. Whereas most of this regulation falls on the other end. Right. So for lottery tickets, it's just, um, it's just a, as you said, controlled by the government, it's, uh, not, not an open market and you have a lot of these regulations that just are kind of, uh, are kind of hidden behind a, a thin veil that I just, that I just, uh, have not been paying enough attention to.
Well, if we're focused on innovation, then we're interested in how regulations blocks innovation. So that will have to be regarding what new products and services could be offered. So when the government runs an industry, that's a very strong reputation and what new products and services can be offered.
Even if customers are free to get the old products from the government. Right? Uh, yeah, I think I can, I can see this. I, I can see this just general, uh, reasoning off to go back and definitely, uh, give that a hard thing. Uh, so how do we, how do we go from a world where, uh, where prediction markets in specific, uh, are, um, are fairly heavily regulated to a world where there are less regulated?
How do we, uh, how do we change that policy? I'd like to question the framing in the sense of saying that we could actually adopt prediction markets within firms today without many legal barriers, but we don't. So that in that context, the barrier is not law. So there are other barriers and they are in fact sort of the most immediate barriers to what would be the most immediate valuable thing to do next.
So that's interesting. What are those barriers? So, so let's make a key distinction here. So an ordinary betting market say on a sporting event, the main customer is the people betting themselves, right? Whether they're willing to pay overhead and taxes in order to lose on average, in order to participate in the bedding.
Yeah, the information is generated by their bets, but that's a side effect from the point of view of the customer, the customer wasn't trying to do that, that's just happens indirectly from what they do. What I'm most interested in is when a person wants information on a topic, they are a customer to buy information and they choose to spend money to buy information via a prediction market.
That's a completely possible, and even sometimes realize scenario. And that seems to me how much more potential for realizing a lot of value in society. So if you're a company, for example, you could have a project with a deadline and you might want to know, will your project made it make a deadline? Or you might say, if we offered this product, how many sales would we get?
Or if we put this person in front of this division and what will be the division statistics, uh, there were a lot of important decisions that organizations make where they use various informal processes to collect information, to decide, but a prediction market could be a more efficient, more systematic way for them to answer their questions.
If they were to do that, the most straightforward thing to do would be to give their employees a certain betting market stake, say a hundred dollars in a market and then say, go trade in this market. And whatever you end up with, you can walk. You don't have to do you have to do some traits. You can't just walk away with what we put in at the beginning, but we're encouraging you to go trade in.
We'll even like, look at your betting market record as some sign of, you know, how your quality of an employee as an employee or how much you've contributed. We're going to say, this is part of our doing business. We're going to ask our employees and perhaps other consultants that are associated with us to participate in these markets, to advise us on these key decisions.
That's something that's quite possible to do now. And pretty much legal that that is it's not violating gambling laws. If you stake the employees and then they walk away with their cash for it to be gambling, the participants have to put in stake, get out stake and have chance in between that's the definition of gambling.
Ah, so why aren't these being implemented more widely? So that is the excellent question. Uh, we have had many experimental trials where people have tried them and we can say that in the trials, uh, they are typically successful according to the metrics of cost and participation and, uh, user satisfaction and more accurate information.
They typically improve information relative to some baseline of what other sources you had. The participants are enjoying the process and feeling like they're being listened to their cost is not excessive compared to the value being attained. They consistently achieve those ends. Nevertheless, they don't last very long.
That's interesting. So one scenario is that some champion becomes known as a visionary for having introduced this new visionary process. They oversaw the process and then they get promoted and whoever replaces them, doesn't see the point of continuing
and often when I've consulted on these things, you can go into organization. And the first thing I would ask is what are your most important decisions so that we can advise them and they usually go, oh, that would be too sensitive. Can we pick something else? So they're trying to pick something that's interesting enough and engaging for participants, but not causing too much disruption or controversy in its answers.
Um, if we let's look at the specific example of project deadlines, so there've been many organizations that have projects with deadlines, where of course often they don't make the deadline. And that's often even though, you know, repeated project meetings before the deadline said that. And so it's, you know, a common failure mode.
Now we've often introduced betting markets into situations like that, and then allow people to anonymously bet on whether it will make the deadline. And we quite often have a pretty dramatic difference between the project meetings, assessment and the pretty quick prediction markets assessment. The market says, no, you're not going to make the deadline no way.
And of course market is right. The markets prove right. And the other meetings were proved wrong. Yeah. And I think they get it wrong in a one particularly specific way. Right. There's a specific thing that people tell themselves. Well, so if you ask, did you want to know that as the project lead, it turns out the project leads go, oh no, I didn't really want to know that.
So if you're the project lead, uh, and you have a project that might fail to make its deadline, the thing you ask yourself is if I fail, what will my excuse be? And most everyone's favorite excuses, we were all going along fine. And then at the last minute, something came out of left field and knocked us flat.
No one could ever, no one could see it coming. It's really rare. It'll never happen again. So let's just forget about it and not hold anybody responsible. That's what the project lead wants and what the project leads boss wants, who will also be held some hours. Oh, that works. If you can get the project meetings to keep saying everything's going fine until the last minute when it fails and it doesn't work so fine.
If the betting market said months ahead of time, you're not going to make the deadline. Then you can't say it came at the last minute, made us fail. So project leads would rather have a more believable excuse than actually get more advanced warning about failure, even though of course, advanced warning.
Might've allowed them to try different things to change the project or the definitions resources, or even to abandon the project still it'll look back. Right. And that's just an example of the more general phenomenon, which is we think of, you know, managers in organizations as sort of scientific spreadsheet managers, right?
They've got a sheet of all their calculations in front of them for all the different options. And they're weighing these different calculations or to make the best decision. That's the way they present themselves. And according to that presentation, they should be eager for any bit of information that helped them update their spreadsheet in order to get the most accurate decisions.
The truth of the matter is managers are politicians to a large degree that they'd like to event. They put together alliances to support projects and they want those. To persist. So they want their projects to persist and they don't really want to have those projects be at the whim of some fluctuating estimate they can't control.
There's going to be estimates that influenced their projects. They want to control those estimates. They, for example, often you hire a consulting firm to do an analysis of something, and then you hint at them what answer you want to get? You don't usually want to just let them tell you whatever answer they come up with because that's out of your control.
And it would then put your projects at risk for them having a disapproving analysis. So in general managers or politicians in general, they are not actually wanting to have their projects be subject to fluctuate, fluctuating estimates. So they're in fact, not actually that interested in, you know, detailed information about which things are more likely to succeed than what at least at the last minute information.
Um, and therefore, uh, the very idea of prediction markets has given them more accurate information is appealing to the myth that they present, but not to the truth of what they are.
Right? So you had this very stunning quote, at least to me, uh, which on its face seems obvious, which is that, uh, prediction markets make it very difficult to have hypocrisy. Uh, and I think this actually goes to something. Very important in our everyday lives, which is that, uh, in many cases, hypocrisy can, can make things easier. Right? So, uh, what are your thoughts on that and why does that present a challenge? Why is this possibly a negative to some people?
So imagine in the C-suite I E the executive suite, you have executives and they vary in their personality and style and how knowledgeable, right? There are various things let's imagine putting in that C-suite and executive who is very knowledgeable about the firm and very knowledgeable about all the different options and their consequences, but has no political savvy whatsoever. When they sit at the big meetings, they can't read the room, they can't figure out who, who has what sacred, you know, Oxys that don't want Gord who has who's who's hinting in which directions about which things they want to hear, who, who is allied with who and who retaliates against who, they don't see any of that stuff.
All they hear is some topic being brought up. And when that topics brought up, when it's their turn, they just say the absolute truth about what's likely to happen. And what are the issues and what are the consequences? I think we all know if we have enough life experience that this executive will not last very long in the C-suite.
That is actual human groups will sort of, you know, fumble and move slowly toward making more effective decisions. But they will do that in the context of all sorts of politics, all sorts of human alliances and issues and things we want to hear and things we don't want to hear. That's just the nature of how humans interact with each other.
And so this executive in the C-suite will be gone. They might become a trusted advisor speaking into the ear privately if somebody in the C-suite, but they would not be sitting in the room talking. So a prediction market is basically one of these autistic advisors. They simply don't know when to shut up or who wants to hear what you ask them a question and they just answer it as truthfully and honestly, as they can.
And so the thing to realize is that's a pretty unusual mode to be in for humans. And it's not usually appreciated that much, not in the, C-suite not in the work line at the bottom, not in charities, not in politics, not in government agencies, not in academia. Everyone gives lip service to we're trying to get better informed to make better decisions.
And in general, on average, that is what happens slowly over time. But people are not very eager open for again, just an honest artist who just blabs, whatever relevant truths come to their head in the context of the topic at hand. Right. That's uh, that's certainly a common scenario for a lot of people. I think maybe this is a good, uh, opportunity or a portal to branch into a, another area of your work before you come back, which is basically, uh, why people are quote unquote irrational, right? So you wrote a book about this called the elephant in the brain. And so, first of all, what is the elephant in the brain? And, uh, what does it do?
So irrational, isn't quite the right word to use. But the idea is that when we do many of the things we do in our lives, we give reasons for why we do that thing. And many of these reasons seem quite uncontroversial, uh, pretty obvious. And so we usually accept these reasons from others because they're the reason we get for ourselves. And we go on talking as least as if these were the reasons for what we do. So for example, we say, we go to school to learn the material.
We go to the hospital to get, well, we vote in order to produce better outcomes for the nation we give to charity so that people who are suffering don't have to suffer as much. We talked to exchange information, uh, all the way down the list. We have a wide range of reasons we give for things. And as social scientists, we tend to take these things at face value as.
No initial point for starting in analysis. So when we look at education, we try to study it. We say, well, okay, they're here to learn the material. What material is it? And who's learning how much and how could they learn it faster? And then we just dive in taking is given basically the thing people say about what they're trying to do.
And, you know, after decades of exposure to, and trying to think about such things, I have a collected a wide range of puzzles that don't make so much sense from the point of view of these usual theories. And we're trying to ask how, oh, how could we make sense of these puzzles? And so the topic of our book, the elephant in the brain hidden motives in everyday life, which I co-author with Kevin similar, uh, the thesis is that if you will just reconsider these basic assumptions about what things are for, you can make a lot of a sense about these puzzles.
Uh, they don't look as strange anymore, but you just have to go back to basics and say, well, maybe school isn't about learning the material. Maybe hospitals, aren't about getting better, maybe charity isn't about making them suffer less. And of course these are hypotheses that people sometimes mention.
They aren't completely strange to us as hypotheses, but we usually hear them as associated with cynics who are thought of as disapproved disaffected, you know, uh, grumbling losers, who can't take the fact that the world didn't love them as much as they. Okay. Uh, we're saying that may all be true about those cynics.
Nevertheless, they're often right about, uh, what our actual motives are. Our motives are often less, pretty than we liked to put on, and then you can use that to understand a lot of things we do. Yeah. But I think you also get the scenario and you actually dive straight into the scenario where you can ask someone completely in private with no incentives tied to them with no possible reason.
You can be incredibly close to them. There's no reason for them to deceive you. And yet they'll still talk about these kinds of optimistic narratives and they really truly believe it. And why is that? That that happens.
So the first, just notice there is a continuum here it's simplest to think in terms of someone lying or just being sincere. But in fact, we have a whole range in between. And so we usually, when we're saying things that aren't entirely true or a little wishful or a little, you know, uh, looking good talking, we are varying degrees of awareness of that. And it varies among us as well. So some of us are just more intrinsically sincere about things we say, and others are just more intrinsically, always sort of trying to present a positive image that isn't quite what they think.
And so, in fact, if you want to look at the underlying motives behind many of these. It will be better to sort of pull people off in private, buy them a drink and like prod them for their complaints and their explanations. So for example, we're much better at explaining other people's behavior than our own.
If you want to look for cynical or, uh, less pretty explanations, you just have to ask people about the rivals and their opponents and say, why do you think those people are doing that? And they are much better able to come up with other theories than they would attribute to themselves. And they're much better able to do it.
If they're have years of experience and a knowledge about something and perhaps some resentment and some complaints, uh, you can get these other explanations to come out of them. But the interesting thing is when we most officially talk through public statements, uh, you know, statement of purpose in your application or a graduation speech, or a textbook on a topic or a politician's speech or proposal in those very public forums, that's where we move to the most extreme sort of positive view of things.
But if you go to the other forums, you will find the other more conflicting views that we are in fact endorsing in our book. So, but there is this continuum. And so the question is like, what causes this continuum? What causes this variation? Why aren't we fully aware and fully honest in all cases.
So the key idea is that your conscious mind, you like to think of it as if it's the president or king of your mind. It's overseeing this vast empire of the rest of your mind and telling them what to do and taking an input and reassigning people to different tasks and reassessing priorities. You're in charge making all these big decisions. And instead we ask you to say, well, you're not the president or the PR you're the king or the CEO.
You are the press secretary, your job. Isn't actually to make the decisions. Your job is to justify them, to makes, to make them look good, or at least okay. To an audience. So when the U S president press secretary speaks to the press about what the president is doing lately, the press secretary doesn't actually know they don't, aren't in all the meetings where all the big decisions are being made, but they don't need to be for each thing the president does.
They need to ask themselves, well, what's a good story about why they might be doing this and why that would be good for everyone and make them sort of safe from accusations. And that's what they do. And that's what you are doing. So. Humans are very social creatures and we have norms that you rules about what we're supposed to do and not supposed to do.
And because we're social and having norms, norms are actually very important to how you live your life. That is it's very important that you find a way to live your life, to avoid accusations of norm violations and to be able to plant some accusations on your rivals. And it's so important that that's actually what your conscious mind that's its job.
That's what you're doing. So norms often are expressed in terms of motives. So if I hit you on purpose, that's a big norm violation. If I hate you accidentally. Well, that can be okay. Don't do it again. Don't make a pattern, but still accidental is okay. On purpose is not. So one of the main things we're doing when we're trying to explain our behavior to explain it's okay, is to come up with a story for what our motives were for each thing we did, why we were doing that.
And why was that? Okay. And that's what you're doing all the time for everything you're doing. You're trying to accomplish the story. If someone were to challenge me on this, if someone were to question me and say that doesn't look very good. Why were you doing that? And suspect you of violating some norms?
You would have a good story. No, I was doing it for this reason. And that's what your conscious mind is for which means your conscious mind. Isn't actually supposed to know why you're really doing things it's supposed to come up with this good start. Right? I think what's very interesting about this idea is that they're extremely positive and extremely negative corollaries.
That is to say things that are equivalent in meaning, but emotionally sound completely different are extremely accepted or sound extremely, uh, uncomfortable. So I would say one positive corollary is something like, uh, we like to make ourselves the hero of our own story. While one negative corollary is we are constantly lying about our ourselves, uh, and we are, uh, not even aware of it.
And I really do want to make this point as clear as possible for my audience. I actually say in the, uh, in the introduction, uh, I think you have this quote, uh, when you're speaking with Sam Harris where you're talking about, uh, uh, a 20 year old, who's been saying for a while now everybody's bullshitting.
No one is telling the truth. Where can I find out what's really going on? And, uh, I really do think that experience is what drew me to your work, uh, in the first place. So you have plenty of examples in the books to make this point. And I think a lot of them are, uh, really powerful. So, uh, first you want to go over the, the one with, uh, with, uh, the split brain.
So the book is split into a first third that makes it plausible that humans might have many hidden motives. And then the last two thirds goes over 10 different areas of. Arguing that in each area, hidden motives explain many of the puzzles that would otherwise not make sense. So of course we didn't need that first third in the sense that you might've just thought it was plausible enough that we could just show you the hidden motives, but for many people, it helps to have this theoretical framing for which it would be believable or plausible.
So part of that is to show you that we are often just unaware of our motives and that we would mistakenly attribute them if we didn't know more details. So he gives some examples of animals where the, if we were to do such a thing, we would give a different reason than apparently the actual reason of the animals. And then we also gave concrete examples where people are actually just ignorant of their own motives.
So the split brain example is a famous set of experience in the 1960s. One of the main person got a Nobel prize out of it. Uh, they don't do this anymore, thankfully, but, uh, back then they took people who had mental troubles and they took the two halves of the brain and they actually split them apart.
They cut them and they cut the connection. And so these people are walking around with two brains in their head. These each of these brains has one eye, one ear controls, one hand, one leg, one of the halves of the brains controls the mouth. And so they can talk to these two have separately, for example, they could talk to one another.
And say stand up. And then that half will use its arm and leg and try to start to stand up. And then the other half, seeing that going on and we'll try to go along and help and they will stand up and then you can ask this person. So why did you stand up? Now, you're going to ask the other half of the brain.
You told one brain to stand one half of the brain stand up. And then you ask the other half, which controls the mouth. Why did you stand up? And of course the truth is it doesn't know you were talking to the other half of the brain. And so that's what it should say if it understood the situation. But the remarkable fact is that people don't say that they make up an excuse, whatever comes to their head, the most plausible story.
So for example, they might say, oh, I wanted to go get a Coke, because that might be a reason why they would stand up. And so, of course, we're not surprised that it's wrong, but what we're surprised is that it thinks it knows. And that's the key thing to notice about yourself. You think, you know why you do most, everything you do.
You're quite ready with an explanation, even when you don't know. Now, that doesn't mean that you don't actually usually know, but it raises the question. How often do you know why you do things? Because if you didn't know, you'd still make something up. I think another example that goes along the same lines.
This is also from your book. I wrote down this quote. I really do think it's a stunning quotes. Uh, and so the setup is that a bunch of men are asked to pick the prettier of two pictures of women. And, uh, the tester pulls a switcher. They hand them the one that they didn't choose and they ask that person why to explain why they chose that picture.
And, uh, this is not like split brain people. This is just like normal people. And, uh, here, here's the quote. So this is from the elephant in the brain. Not only did a clear majority of participants fail to notice the switch after being given the wrong photograph. They often proceeded to give concrete and specific reasons for their choice.
She looks like an aunt of mine, and she seems nicer than the other one, or she's radiant. I would've rather approached her at a bar than the other one. I like hearings even under the best conditions, unlimited time to make the choice pairs of women with different hair, colors or styles, the subjects realized they had been deceived only about a third of the time.
So you have these scenarios where people are given a given just exactly the opposite, the inverse of what they did of, uh, what their kind of expressed preferences. And they still still go out of their way to explain this, uh, to explain this. It suggests we feel a strong obligation to be able to explain why, what we've been doing and why.
Right. You might think at some point, well, it's none of your business, what I'm doing and why I don't have to justify it myself to you. You might say that apparently you don't mean it because you really are devoting a big chunk of your mental energy all the time to making sure you've got a good justification and you're quite ready to do that at pawn ass.
Right. And then the last, the last, uh, puzzle that I want to go over here is actually one that you alluded to earlier. Sorry. Uh, the one about conversation. So, uh, you might ask yourself, uh, why you want, uh, to engage in conversation. And usually the explanation is something like, I want information, right? You want to exchange information.
Uh, but, uh, as, as you, uh, as you find out that isn't necessarily the case, so what happens and why does it happen? So, uh, the structure of all of our chapters is to say, here's a behavior. What's our usual reasons we give, what are things that don't make so much sense from that point of view? And let's say better theory to explain.
So in conversation, if we say, look, when you order a pizza, when you tell the carwash how you want your carwash, you know, there's lots of contexts where we have very practical conversation, but then there are these other contexts. We are just chatting and talking without much in the way of very specific goals or agendas.
And we do a lot of that. We do a lot of just talking and we can ask you, well, why are you talking? What are you getting out of that? Because you're not ordering a pizza or doing something very concrete. And then people will often like, you know, not have an answer or not really know what to say, or as if, why should I need an answer?
But if you push them for an answer, one of the most common answers would be well. Um, we're exchanging information. Um, I know things you don't, you know, things I don't, if I tell you my things, you tell me your things. We walk away, both knowing more than we started with you learn useful things from talking and listening to people.
That explanation, however, is somewhat at odds with some of the details of our conversation. Stop. So, first of all, if it was a trade of information, then I would be more eager to listen and less eager to talk because, uh, you know, talking is, uh, where I'm giving out something and listening is where I'm getting it and I'd rather get them give, I would also, uh, keep track of debts and, uh, amounts on both sides.
I would say I've told you three things so far, you haven't told me any, it's your turn. You need to catch up. And we might talk about what was most important to each other. What had the most value in our lives and the most, you know, way it could influence decisions, but. These don't happen. So they call into question this idea that we're trading information.
And so our alternative story is that we are showing off a mental backpack of tools and resources. So the game is supposed to be that we're not supposed to control the conversation is, was just supposed to go. We're not supposed to sort of directly pick it out and say, I want to talk about this. Let's talk about this.
We're supposed to have it drift organically without much apparent influence and wherever the conversation goes, where each post to find something interesting and relevant to say, and that's the test of our backpack, wherever the conversation goes, we can each have something interesting and relevant to say, well, then that must be a good person to have around when you actually needed to have a conversation about pizza or car washes, they would have something in their backpack about that too.
And they would be a good ally. So we're trying to show off our potential as a good ally by showing off our mental backpack via somewhat random tests of what you got.
Right. And I think this begins to shed a lot of light on not just the puzzles that we've presented here, but also some of the puzzles that we've looked at already, uh, with regards to prediction markets, with regards to politics and. I think this leads us to, uh, the second half of your book where you ask the question, not only about ourselves, about the implications, to how we deal with each other on a one-on-one level, but also with our institutions.
So in the broadest way possible, uh, what would you say is the influence of these types of deceptions and self deceptions on the way our institutions function? So, first of all, we have say media and politics, where we are an academia where we are basically following the same conversation, norm and process.
So in the media, you're supposed to talk about whatever everybody else is talking about and not change the subject, same academic. You're supposed to publish it. Whatever is in fashion. And we're not very interested in how useful the things you say are, we're more interested in how impressive they are and people in all these contexts do follow the fashion of the topic and talk about whatever's impressive.
So this gives you a sense for the function of these institutions. Now we might ask, well, could we change our institutions? Should we change our institutions for the better? And of course that's a very valuable and useful activity, but this suggests that the problem is harder than you thought. So previously, if you said school is for learning the material and therefore in order to improve schools, we need to find ways for people to learn more material.
And many people have spent a long time doing exactly that there are no many people who study education, who do know exactly how to make more people learn more material faster. We know how to do that. And yet schools don't adopt those practices. They don't seem very interested. Neither teachers or students seem to care whether they're learning more material faster, which is a problem from the, of course the usual theory.
But it's also a problem from trying to reform school. So the way to think about this is to say, and every institution, there's the thing people pretend to want. And then there's the thing they really want. If these were the same, then you would just have to find a way to give more of it to them. And they would be all over it.
They would be happily to grab the thing they want. If you have people who are starving for food and you give them food, they take the food and they're willing to pay you more for it. Uh, if it's more food, but if people aren't so eager for the nutrition of the food, they're trying to get something else, neither offering them a larger plate of more calories and proteins and vitamins won't necessarily get many customers.
If that's not the actual product that they were buying, if they want it, for example, taste or variety or a story to tell from their food, you'll need to offer that in order to get customers. So in education, if people want. To show off their ability and conscientiousness and smarts and conformity, uh, as potential employees, then they kind of know that at some level, then you offer them ways to learn more material faster.
And they may well shrug and say, yeah, so what has, even though that's what they say they want, they kind of know it, isn't what they really want. And so in order to reform something like education, you'll have to find a new alternative that does two things at once. It has to let them continue to pretend to want the thing they pretend to want, and it will have to actually give them more of the thing they actually want.
That's not impossible, but it's a little harder, but the compensation hopefully is that if you can solve that harder problem, you will get actual take-up and traction. So a common observation, that's one of the things that started me off on this whole track is the observation that we economists and social sciences seem to know a lot of ways the world could be better and people just don't seem to be interested.
And we wonder, well, you know, in other areas of life where say physics or computer science, where people want something and you find a way to give more of that to them, they actually are all over it. They buy it, they pay a lot of money. If you can find a better, you know, low, super superconducting material, you can find a lighter structural material.
If you could find a better algorithm for sorting a number of lists of numbers, If you have one of these usual tasks that people are doing and you can find a more efficient way to do it. People pay a lot for those things. Yeah. For fundamental things like how we go to school or how we use doctors or how we vote, we can find big improvements and people don't seem to care.
And this is our suggestion that the key problem is, well, you're giving them something. They say they want, and it isn't what they really want. So why should they take right. And I think this connects very deeply to possibly the biggest overlap between your work and the frame of the show, which is the question of why civilizations and institutions decline.
I think, uh, the main question of the show really is how these institutions organize themselves, how they, uh, change over time and what really, uh, they're made of. Right. And, uh, so you're introducing a new key issue is, has changed over time. So as I said, as I said before, the first thing you need to analyze is just things that are constant across space and time.
Like, what are people doing in hospitals? What are they doing in schools? Those are timeless and spaceless in a sense like they're, they're just general questions. And that will have to be the prerequisite to theorizing about changes. If you don't know what the average is doing, you're not going to have much of a chance of figuring out what changes are doing.
So, uh, but of course it is interesting to ask what is changing. Uh, so, and you know, and the existence of a strong long-term history of civilizations rising and falling is, you know, the most disturbing evidence about our civilization and saying, well, how could we escape this pattern? And in the past civilizations, rose and fall in the scope, spatial scope of the region around them, that they interacted a lot with civilizations could fall in one region and not fall in another region because they were pretty disconnected today.
We have a global civilization, pretty tightly connected across the globe. So if this civilization fall is the entire globe falling together, and that's much more disturbing because in the past, when one civilization would fall, its ruins could be recolonized by another civilization, which hadn't fallen.
And so they didn't have to rediscover everything from scratch to, to start up again, they could just take transplants from other civilizations. If our civilization collapses, there's more of an issue of how will it regrow without having to rediscover what was lost, right? It's not only that you risk the burning of the library of Alexandria, but all libraries of Alexandria everywhere, right.
Or all chip factories everywhere, truly, truly terrifying, or all automobile factories everywhere. Right? I mean, That means now I still think, you know, it's relatively unlikely for, um, humanity to go extinct in such a scenario, but it's more likely than otherwise. And you would, don't definitely, don't like that scenario, so you'd want to prevent it.
Um, and you know, the time it would take even just to rediscover everything from scratch has short-term cosmological times. Right. So I'm actually more worried about, um, a sort of rot and lock-in than I am about to collapse.
Yeah. So I think we have similar models of how institutions, uh, rot and decline, but, uh, especially for the audience, go ahead and talk about, uh, what your model is.
Why, why do institutions rots? So, uh, systems we know rot in general, uh, the most dramatic example is just large software systems. In that case, the bits do not rock the code can be very reliably, stayed the same, nevertheless as the environment changes and as the demands and needs change. And as people participating change consistently slowly over time, the system, because.
More fragile with more interdependencies that make it harder to make changes to any one place without making simultaneous changes to many other places, they just become more rigid, less flexible, harder to change usefully. And therefore, eventually we just throw them away wholesale and started from scratch.
And once you see that in software, you can see similar things in other rule systems like rules of companies, companies tend to rot over time in terms of having a culture and set of rules that are slowly at the humidity and the sort of rot legal systems get more complicated or require more context, dependence, and detail on procedures.
And, uh, it seems to be a pretty general phenomenon and that's plausibly part of why civilizations have rotted. So we do have a fair bit of data about past civilizations and what went wrong. And part of it seems to be that you had an initially very homogeneous and unified culture and group that was small, and that was very well at policing itself.
And as it grew, it sort of accumulated more veto players, more places where somebody was focusing on their own interests and sort of had their thumb on some key, uh, flow and could demand a share of it in order to allow things to pass forward. Uh, more veto players basically accumulate to prevent useful.
And to demand their cut and that's a common process of civilization decline. Uh, and there are other correlates we see. Uh, but again, I'm more worried about not declining, but not rising either. I'm more worried about some sort of lock-in where we all sort of continue at a modest level of performance, but we become less and less able to change, but we don't collapse and stagnation.
Yes. And D perhaps even a great one, a very extensive a long-term one. And so, uh, I think some cultural trends are at risk for, uh, inducing this. So over the last half century or so, I think we've seen a remarkable degree of integration of elites around the world and in a remarkable degree of homogeneity in policies and regulations around the world.
So even though you might think that anyone regulatory issue, uh, if one nation doesn't allow something, some other nation would, in fact, uh, there are such a strong correlation in their regulatory policies that they aren't allowed anywhere.
Right. So here's a question from out of left field. Uh, have you heard of Steve jobs idea of the bozo explosion? Um, no. It sounds like it should have an easy explanation though. Cause it's got cute little name that usually goes together, right? Yes. So, uh, Steve jobs, everyone knows the founder of apple had this hiring philosophy.
Uh, but it also extends to culture and customs that, uh, they're essentially a players. And then there are B players or quote unquote bozos. And if you have a team of a players, they orient the hiring the culture and the customs around making things more competitive, around making things higher quality and around holding each other accountable.
Uh, but if you let bozos on the team, they will, first of all, hire people to make themselves feel good and to advantage themselves politically and also to change the culture so that it's more difficult to have them being, uh, held accountable. And, uh, this in his view is how, uh, companies or startups slowly grow from, or, well, not necessarily grow, but change from being their initial startup selves to eventually these, uh, legacy companies that are less productive.
And I think that there's actually a even stronger correlate to this model, which, uh, maybe as my, uh, most kind of well-known, uh, piece of writing, uh, about, uh, this, uh, model of what I call the kind of midwit cycle, right? You have this, uh, you have this, this, uh, self-reinforcing system where first you start off with, uh, some, uh, high functioning company.
You have an incentive to expand to grow. And this requires, uh, this is not my original idea, but this, uh, I heard it from Balaji Srinivasan, and I'm not sure it comes from somewhere else, but the most important thing goes from being, having, uh, iconic caustic people to having what he calls the bus number, right?
What is the maximum number of people, uh, who can be hit by a bus and your company will still survive, uh, a little bit of a grim metaphor, but, uh, essentially what this means is that you select for, uh, increasing homogeneity. So you have this, uh, increasing homogeneity and you get a pressure essentially for, um, just less interesting people or even just less, uh, less, uh, highly competent.
And what this creates is that it adds up with the, uh, with the Steve jobs phenomenon or with this phenomenon that Steve jobs described, where I think this is exactly where it lines up with your model. It becomes an increasing race towards conformity, towards mediocrity towards essentially having this highly unequal rewards function where you're punished heavily from deviating from the standard and where you are not properly incentivizing a new or kind of contrary and innovation.
So I moved the analysis up a level of abstraction, and I'd say, uh, if we just have a world of small firms, some of whom fall into a good state and then grow into being bigger ones, but then consistently fall back into a bad state as they get bigger, or as a result of getting better or just randomly then the dynamics of the world is that, uh, as long as we have many smaller organizations competing than sort of the overall level of quality can be maintained, even as things consistently get worse for individual organizations, because you'll keep repopulating them with new, smaller ones that are better.
Right? So, uh, the, the fundamental problem is when you no longer allow this larger scale competition that allows this repopulation with better firms. So as we produce sort of global organizations, which lock themselves in. And don't allow competition from smaller upstarts to replace them. Then we will have this larger civilization decline where large organizations are just consistently getting worse, but they don't get replaced.
That's the scenario I'm most worried about. And so that's about when do we have these large-scale structures that can't be replaced? So one key issue is that, you know, in the past we have mobs, but mobs are local. And so with one mom is bad. Another mob, somewhere else can be better. And the mobs don't sort of get worse.
But once we have a world mob where elites around the world, they're all in the same mob. If that mob gets and worse, it doesn't get replaced by some better mob. It just gets worse. Similarly, when we have regulation around the world and it's worse, nobody can defy the regulation and therefore we don't have a place that defies the regulation and shows that it's better.
And then other people say, oh, I guess we should change a regulation to be what this one is, regulation. It's like, we just, it all just gets worse. So this to me is the more fundamental problem when we have global scale coordination and organizations and they just don't face competition. Right. I think I agree with a lot of that analysis.
I just want to focus on one point that I kind of want to reject, which is that you use the idea of, uh, elites, right? And, uh, this is something that I've often, uh, often had a lot of conflict with conservative or libertarian people, which is. Uh, I very much liked the idea of elites and I think it is a word that is used almost always incorrectly.
So, um, the idea abstract of elites is that we have someone who is very competent, who has proven themselves to be able to, uh, win out who ha who has possibly, uh, uh, very, uh, um, very successful in a given field. And often, most frequently when this is used, particularly by those groups I mentioned before, but also in general, uh, it's, it's used to describe something quite different, which is it's described, it's used to describe essentially an inheritor class.
So let's just flush things out a little bit more. Uh, you, you kind of have two ways to, you kinda have two ways to power, right? Uh, or you have two categories to power. You have, uh, the construction of power. So, uh, building a startup say, or perhaps even, uh, starting a revolution, uh, government, although that's not necessarily something I endorsed, not something that I endorse at all I should say.
Um, and then you have, uh, you have ways to inherit power, and this is not just, uh, with regards to, um, literal inheritance, like, uh, my father died and now I have his castle, uh, but also with, uh, political or social media, So you get appointed CEO, not necessarily because you've proven yourself, but because you have the right connections, you get put into government and power for similar reasons and so on and so forth.
And why, I think it's so important to make these distinctions is that I tie this greater social erosion or this greater, um, formation of, uh, global mobs or larger mobs as directly tied to the attempts of inheritor class people to, uh, to further entrench their, um, inheritance or actually just to further entrench their power.
Um, so the problem I have here is that a lot of problems we need actual elites, right? This is, I think the thing that I disagreed with conservatives about is that, sorry, let me just, uh, say what I mean by the word and you may not disagree with that. Okay. Um, so what I have in mind is that, um, you know, in, in any small society, even like a firm or a club, uh, there is an inter inter mixed process of gossip and status.
Okay. So, um, people gossip. But then people who are high status count more in the gossip, the consensus that's formed is influenced more by the people who are seen as high status. And one of the things people gossip about is who's high status. And so the gossip forms the status and the status selects the gossip.
And that's just how most societies have always been that there's, there might be somebody formally in charge, but formal formal powers. Aren't the same as this gossip process. Sometimes of course, the, for the formal person in charge has more influence over the gossip and the gospel goes their way.
Sometimes the gossip may disagree with a person in charge. Uh, but the key process is there is some set of status. And when people hear gossip, they hear what different people say and they weigh that according to their status. And then they talk about who's high status. And so what I mean by elites are just the people high status in that process that is they are seen as counting more in gossip and the gossip sets that ranking of how much they're counted.
So that's one kind of gossip and one kind of eliteness. And there are of course, many other ways to talk about eliteness, but that's the kind that's relevant for gossip. And the consensus is formed from gossip, which is a powerful force in most societies, even in firms and clubs and churches, et cetera.
And th the key thing that's happened is we've formed this global community of gossip with global high status people in that. And those people go to Davos. Those people show up at, you know, Ted talks, those people go to the same events, the same parties, you know, and they form a consensus. And so, for example, at the beginning of COVID, there was this dramatic process whereby at the very beginning, the usual public health experts said their usual things.
And then all of a sudden, all the usual world elites in this sense got together and started talking and talked fast and a lot. And they came to a consensus that differed from what the public health people had said. And they said, no, it needs to be this way. And then that's what everybody did the whole world together.
Did the thing that those elite seven that's the key point is to notice the athletes are different from the experts there. That is the experts are people who have positions of authority and standard organizations who have trained in those things, et cetera. And there are elites in that sense, but by elites, I mean, these people who talk a lot who talk to each other and are deferred to a lot, including by the experts.
So another analog is when somebody wins a Nobel prize, commonly, the next thing they do is try to write up it, right? That is, they're saying I'm an expert, really good experts. So maybe I could be treated as an elite. I want to take my, put my hand at being one of these talking people that people listen to and it influences the talking consensus.
So that's what I mean by elite. And again, I don't much care what the word is, but it's a distinctive process that needs a name. And it's very important in most, most organizations and most, you know, societies. And the unique thing is we are not having a world version of that. So when the world gets together and talks about something, what to do, then the whole world does that.
And now that prevents a lot of variety and innovation, uh, that if it goes against that consensus, right. And I want to be clear, I wasn't necessarily, uh, accusing you of misusing the term, but I think the word elite right now and why I take particular, uh, problem with it is that it's almost a perfect scissor.
So you are, are you familiar with Scott Alexander's idea of a scissor? Um, I probably could use some rep from reminder. Yeah. And this would be good for the audience as well. Uh, essentially a scissor is, uh, uh, an idea that that completely splits the population and usually close to around half and half. So a good example of a scissor might be say, uh, the, uh, Kyle Rittenhouse.
Where, if you were someone paying attention to the conservative media, you would have, uh, thoughts that, uh, he is someone who, uh, did nothing wrong. Someone who is honorable, who is patrolling, uh, for, uh, the sake of his own community. And some of those ideas are wrong. Uh, and, uh, if you are someone who has a liberal media, then you would have the idea that he is, um, uh, he is quote unquote of white supremacist.
There were polls taken that showed some high percentage of democratic voters who thought that he, uh, shot and killed black people. So you have these other, uh, misconceptions about him as well. In general, when I pick words, I try to avoid sort of political potency or are, you know, triggering that sort of way.
And I look for words that could be more neutral. I just didn't find one in this case. So if you have a suggestion for a word I'm all ears, what would be a good word for these? I mean like talkie, you know, people who talk a lot, a better term, but socially it has a more specific connotation of a particular sort of elite heritage and association.
So if you think about people at Davos Davis, man, his day was man a socialite. Well, not exactly. I mean, a lot of them are, but they're, it's a different concept. Uh, you cannot, you know, keynote speakers maybe, or, uh, you know, intellectuals, intellectuals, isn't the right word either. Um, because a lot of these elite people have our athletes and their CEOs.
And I mean, there are many of them aren't intellectuals by, by their official role in society. But the key point is like, if you put them on a panel, they defer to each other to the grief, they have some overall status that can be gained from many different sources. Uh, actually, how about this? I'm not sure if this is a widely used her and I'm sure someone has probably said the words before, but how about we call these, these groups of people to status class, um, statusites, yeah.
I just want you to avoid this. Um, but I mean, you know, I, I am also reluctant to the noises invent my own words because, uh, you know, that people don't usually pick up new and newly invented words, but if you, if existing words just don't do well enough then, uh, sure. In fact, right now I'm tempted to use my power of the poll to pick forwards here and put a poll, let's say, what should they be called?
That would be a, that would be interesting indeed. Uh, but we got to choose two options now, status sites and elites. And we just need to, you know, our socialites, I guess third. And so I just need another one. I'm ready. Ready to go for the poll, yeah, I think I just have a particular problem with, uh, the word elites based on, uh, the kind of conversations that I have.
I find it to be almost a perfect scissor across people who I interact. So, uh, just that problem, maybe it's, maybe it's not as widespread this term, as I defined, it includes a lot on the left of the right, right. This, this isn't a left versus right. Split in terms of the, you know, these particular class of people that the dateless man or people on panels or people who write op-eds et cetera.
Yeah. That could be it. Right. But the Davos man, right. A little that, that doesn't quite kind of take like the whole larger world of such things. I mean, so that's sort of, he's an exemplar, but he's not, you know, most people who are this have never gotten to Davis, that's true. This is, this is a difficult task.
Hm. Maybe we should set up a betting market on this. Well, first we need options. That's the first key. Again, I I'll do a poll of four options. Give me four, you know, but, but, uh, I'm happy to let's status sites is I'll. I mean, it's almost there it's, it's kind of awkward sounding it doesn't roll off the tongue very well.
But, uh, look for, you know, I mean, we have a bunch of, we must have lots of synonyms for big shots, right? I mean, like the word big shot, right? So big shots if more cannots power, but that does connote sort of awareness and like that people are aware of them or something. Yeah. So it's not, it's not so bad.
Yeah. I think we can settle on big shots. Uh, so, you know, they don't. But yeah, the idea is to identify this concept, right? And that the key thing is that we don't have competition so much for this thing anymore. And in some sense, this is a key axis that is around the world. If you look at variation in policy, a lot of the variation is the farther you are away from these big shots.
The more you're willing to do things different. So like rural, rural Guatemala, like defy this sort of global consensus on COVID because they're just far away from that, you know, strong consensus center. And, uh, but the problem is these different people far away from center, they don't, they aren't coordinating with each other so much.
Right. Uh, they're sort of out in the periphery of the network of connections. And so there, aren't going to be able to very strongly coordinate to oppose whatever the big shots, um, agree on. Basically the main way to do it. Say we have black markets or just defiance or, you know, being hard to see. Right. I think as someone on Twitter said, uh, to show how dominant the U S still is even compared to say Russia or China, um, just, just draw a map of the countries in the world that offered asylum to Snowden.
Right. And I, I mean, this is a few years in the past, but you can make a guess at which countries would do so, uh, again, and you would, I think you would see that there is still quite a good. Um, global, uh, hegemony
right, right. And, you know, and there's, I mean, to me, the biggest problem is that, um, over the coming centuries, we will, this will happen stronger and they will have many big wins.
I think we have to grant that, uh, a stronger global hegemony and integrated global big shot culture. They will decrease war. They will coordinate for global warming. They will promote innovation. They will make PR you know, fisheries work better. There are a lot of things that will go well because of this larger global coordination.
And people will be proud of that. And they will like sort of the emotional sense of us all being part of a global community that solves problem together. I think sort of, that's sort of a very ancient human thing that is human foragers for a million years. At least we lived in small groups of roughly the size of 30.
We only ever met say four, are there groups of like that in our whole lifetimes, maybe 150 people that we ever met in our lifetime. And, uh, the way we did things is when there was an issue, we sat down and talked around, the campfire, came to a consensus, and then we did that and we enforced it. That's how humans live for a million years.
And I think it's deep in us to want to keep doing that. And global governance gives us sort of that feeling again, the whole world sits around and talks and decides, so. The global government will be popular. We don't even need a global government. You see, we have a global mob. And so people have been focused on the global government for all this time and worrying about it and then resisting it.
And so we don't, we have very limited weak forms of global government, but we have a pretty strong global mob, the global model of big shots. And that's w that's mainly what it will take. That is again, like we did with COVID all the big shots got together, talked a lot for a couple of weeks about what to do about COVID.
They came to an agreement and then the whole world did it, wait, I want to go back to, or maybe I reframe the question wrong, or maybe I phrase the question wrong, but do you think the global mob would be popular? We'll go. Well, the mob is popular. Mobs are always popular among the people, the people that are in them.
I just don't find mobs are popular, are among the people that are in them. Yes. But I think I'm much more concerned about like this kind of overreaction and this is not a new idea. Right? You have like Nietzche, uh, here, here is the, here is the coldest of cold monsters. Here there are no peoples, their only states the coldest of all cold monsters.
So I think you have like this, this trend around the world that people actually really hate the global mob. Right. People hate, but they like, they like this community of, you know, keynote speakers and op-ed writers and Davis men, those people, they like, at least to the extent. Yeah, they can't coordinate to impose them much.
So there's no center there I agree with. But so if you read the, uh, Martin Gurri's book, the revolt of the public, yes. Okay. And so his thesis is that, you know, movements in the last decade or two have resisted having a hierarchy and having leaders and people who represent them. So they can have a sense of egalitarian participation, but nevertheless, they agreed on what to do and they did it together.
And they really liked that people in those movements really liked being part of a movement where they all kind of agreed on what to do and did it together and had a strong consensus. That's the sort of thing people really like. And that's, that's a key romance of revolution. If you think about it, people like the revolution before the new government takes over, they like the romance of all the people getting together and doing something together that they all want, even if it's very destructive.
Okay. So let me try to get the, get the line of reasoning. Right? So step one, people like these kind of mob style discussion slash decision-making, and then that leads to people being in favor of this kind of global big shots, mob type discussion decisions. Well, it's more just, this is the natural human thing that humans did for a million years.
It's the thing we recreate in any small scale community that isn't, doesn't have some other structure. You put a hundred random people together on an island. The first thing that happens is they form this can talking community that creates consensus and that's how they make their first decisions. And then they might decide to create some formal organization after that.
But for awhile still, this, this gossip will be the main thing in control and the formal organization. You can't really define it if it wants to stay in power, that's just how humans have done most, everything everywhere, always. And so the point is that that's always been within the scope of smaller organizations and parts of the world, but if we formed that same structure at the global level, that we will no longer have sort of outside competition to, uh, to discipline.
Yeah. I think the main difference here is actually like one of like consequences, because I think you see as a big problem. And I agree that it's a problem. It's like a grievous problem of over, over coordination. But I think, uh, but, uh, I think that the thing that is likely to hit us first is this kind of populist reaction to the, uh, to the global big shot class. Right. I, I think that there is a very high degree of ferment and dissatisfaction. Uh, particularly during COVID, but before as well with the global big shot class.
I, I could agree there can be a lot of dissatisfaction, but the conflict is pretty one-sided. So it says United States basically, um, you know, the, the elite cultural elites control, academia, law, journalism, tech, finance, uh, and now even the military and medicine, uh, they have a pretty strong lock and consensus control.
And those major institutions of life Congress say is more emphasizing rural areas. And the rural areas are more disaffected away from those elites. And so they will be able to sort of control Congress and the presidency and express their unhappiness in the way Trump, Trump expressed it. That will continue for many decades all around the world, to the extent that people are unhappy with these elites and resent them, they will take control of the, sort of the populous institutions that they can take control of.
I E uh, elections, there are major candidates, but those will have limited influence on the world. Like even today, civil servants are largely controlled by the same cultural elites that, uh, the populous president, like Trump can't do much with Trump. Wasn't angled to actually to make the government agencies tow his line very much when they didn't like him, they could, you know, like when the FDA basically refused to, uh, To, uh, announce the results of the vaccine trials before the election, because they didn't want to, you know, they weren't just able to get away with that.
They did it and they got away with it. Those people stayed in office and they are still there. Right. Okay.
Yeah, I think it makes sense to continue in our current direction. So I think I do want to go deeper on this question of what are exactly the consequences of this increasing, uh, increasing homogeneity or this, this decreasing competition. So let me make 1.1, one point to frame it, which is, uh, so I studied formal political theory, uh, which is usually up democracy and a standard result in formal models of democracy is you get sort of a main access of disagreement and sort of parties say equally split along this axis with roughly equal power on both sides.
So that's this sort of common observation we might see and say the left, the right, the United States or other areas where you just consistently get, uh, people converging on one main axis and then roughly splitting down the middle of that one access. And there are plausible reasons why that would be a robust thing to happen.
But notice that in say firms or towns, or even cities in governorships in the United States, that's not how it goes far more common pattern at the smaller scale, if there's just one dominant party that wins and the other party doesn't, and that is in fact of pretty stable situation in a lot of contexts.
And that's how most ancient civilizations were. Okay. So you have to say a plausible future scenario for something like the United States. We switched to that one party just takes over and wins and then dominates. Uh, and the other sort of has symbolic opposition, but doesn't, you know, actually have that much power again.
That's how most firms are. That's how most towns are, how most cities are most clubs, churches. That's how most of them are. They are dominated by a single main coalition that just keeps winning. So that's a plausible future for the world as well. And what does that do? What's that, uh, either cut us off from, or what does that, what problems does that create?
Well, again, the key problem, I think, is lack of competition at the larger scale. Again, we talk about the rise and fall of nations, the rise and fall of empire, the rise and fall of firms or clubs. The key point is there's, there are often consistent trends, whereby things get bad as they get bigger and older, but that's problem is fixed by older ones, die, go away, replaced by younger ones.
And so that can be, you know, longterm growth and development through that process of replacement. Whenever we get a thing that just locks in and won't change, then we have much more problems. So there's the classic example of course, of Europe versus China for the industrial revolution and a classic argument for Europe.
W why it happened there and what elsewheres Europe was permanently split among many different powers. They had a shared culture, but no one power took over. And so they had a lot of competition and innovation in that mix of, uh, different powers. Whereas China, you had a single power takeover. It could impose regulation that limited innovation in many areas. And therefore it did not produce the industrial revolution.
Right. And I think this actually ties into maybe some of your broader theories about politics, because I remember seeing a, on Twitter the other day, you said that you think, uh, quote, unquote, wokeness, whatever you think, whatever we define that as we, you can define it later, we'll peak after 2030.
And this was very different from the results of the poll, uh, that you, that you sent out. Uh, so, and also different from what I thought, I think that it would, will peak like either, uh, very soon or that it already has. So, uh, why am I wrong about, uh, where, where this movement will peak?
So the main evidence that I would point to is seeing things behind the scenes about woke powers, taking and exercising control in large areas of life.
Uh, so I hear from finance people that they have to. Be be certified as in a sense woke in order to be allowed to offer their investments. I hear tech people saying they can't work at tech firms unless they show sufficient loyalty to woke. I hear people in academia, uh, accreditation, uh, using that to, you know, decide whether high schools and colleges can be accredited.
Uh, I, you know, I see that just in charities, uh, charity organizations, uh, you know, having these, uh, struggle sessions and, uh, you know, explicitly checking that everybody is loyal and making sure they do big symbolic things to show their loyalty to these things. I I've over last year, I've seen in things I have personally been associated with.
I've seen those things and it at an increasing rate. And then when I ask other people, they've also seen such things. So it's not making the news, but there is really a lot of movement behind the scenes in big ways, whereby this is growing and taking a lot of control of a lot of things. So that doesn't look at all my, like, they're ready to sort of back off in a year that this is long-term planning, long-term, um, choices spread.
They they've been, they, this is the culmination of many years of efforts, uh, and they are digging in and trenching for a long battle. Hmm, that's interesting because I think there was an alternative way of this, of viewing this, where it's just moral panic. Right. So you could say that in the wake of, uh, two in the wake of nine 11, you had a lot of, uh, motivation for companies to suddenly be very patriotic, right.
And you might ask, well, why, why specifically after nine 11, what specifically happened in corporations that would make, uh, them, uh, specifically, uh, want to be more patriotic? Uh, I don't think everyone is just coming up with this. It's like, oh, it's a 2002. This is a good year. I'm feeling patriotic today. No, it is because there is this kind of a wider social reaction, right?
And so you get this very big swing in one direction or another based on, uh, based on these events. Uh, and, uh, I think that, that way of thinking about these things would say that this is just one of many kinds of moral panic types things. And, uh, and eventually this will, this will be gone as well. Well, so nine 11 as a reaction to a very particular, random disturbance of a large magnitude.
And so, right. Yeah. I should say that that one is not, not a moral panic, but, uh, this one is much more of like, basically there's a social process that creates accumulated positioning. Right. But in order to believe that there was a temporary thing you need. Temporary random thing to happen that you'd believe was sort of an outlier.
That's going to fade away and its effect, uh, that, you know, that's different from these movements that slowly accumulating grow, where each event, isn't a random event, it's the culmination of previous parts of the movement. And so, you know, as a movement gets bigger events, you expect even bigger ones, if they're part of a growing effect.
So I, you know, I say, well, sure, nine 11 was this exogenous large event that produced a temporary fluctuation. But, uh, the question is what is the temporary fluctuation that you would attribute? Let me say, COVID doesn't really work, right? This was growing before. COVID it doesn't look like COVID is necessarily changing it very much.
It's not really COVID oriented this isn't about COVID COVID is certainly the biggest, most recent, random fluctuation that you might attribute anything to.
Right. Uh, I think that makes a lot of sense. I'm actually probably more convinced towards this direction, at least compared to, uh, what I was, where I was before. But again, I think there really should be as just some good actual surveys of what's happening behind the scenes that aren't being reported, because it does seem like the media is selectively not wanting to report on these things that, that is they, when they reported on some things, it was a lot of ferment and that encouraged say Trump supporters and they've learned not all we don't want to, we don't want to say things, that'll encourage them. And so there's a lot of, you know, not lack of reporting. Yeah.
Right. So the final line I thinking I want to, uh, I want to go down is a for today, at least is, uh, how do we reverse or how do we, uh, counter incentivize, um, this, both this homogenization and, uh, whatever movements might be connected to it. How do we incentivize more competition and more, um, and, uh, disincentivize, hegemony?
Uh, it's very hard, but basically, um, so I went to an event, um, where people were talking about, um, you know, innovation and education and, uh, people were there, like some people who are like starting new colleges, you may have heard people trying to open new colleges. And they're saying basically, well, the existing colleges are too stuck in their ways and too woke or whatever.
So we're going to make some new ones. And there was a guy there who made the plausible counter argument. He says, you don't get it. You need to be retrenching and defending. This is not a time for the offense. Uh, that is the, uh, the, the power on the other side have already taken over the accreditation boards and they're already ready set to not accredit your new schools.
Uh, and you need to find ways to defend what you've got and not try to go for it. So, so the simplest thing is to realize, just realize what you're up against here. Realize how big and how strong a wave it's coming. Right. You know, try to find something to hold on. So the tidal wave doesn't wash you away.
Um, that that's unfortunately, uh, where I think I have to come down, I have to say that, you know, we're at the beginning of what's going to be a much larger thing and it's going the wrong direction. And, um, I, in the long run, I have lots of innovation things I want people to try, but I haven't gotten people to try them for decades anyway.
So I suppose sure. Come and try my new things. But like from the more fundamental, larger thing, things like ask yourself how to, how to protect the downside here. Like, so think of, you know, I guess, I don't know, Canticle for LIBOR, what's the famous science fiction, novel civilization collapses, and a few people have saved a few remnants of civilization in some place that they hope to rebuild from.
Like what can you protect and save? Right. Um, could could the thing that ends up getting productive just to be, um, I mean, you can have plenty of disagreements with how that country is being run, but. Could it be something like hungry or something like Poland, where there they might have, um, possibly other problems, completely different problems, uh, that we ended up we were concerned about, but that you would have the, that you would have the, um, desire to protect simply because it's different.
Would that be one strategy, certainly like help on the margin, helping promote just things that are VR VR have variety in various ways, things that are away from the center and all sorts of directions. Uh, one of the long-term problems I actually see is a fertility decline. And honestly, the best scenario I can imagine to reverse fertility at Kline is some relatively insular high fertility culture arises.
And that insularity is a big problem. But if you think without that insularity, it sort of gets absorbed by the larger culture and assimilates the larger cultures, fertility values, and habits. Then I guess you're kind of expecting that there will be some insular counterculture that has high fertility and maybe like trying to promote that or be connected to it or a part of it or something and, or various attempts at that.
Um, you know, I guess like, you know, you've read Osmos foundation series or seen that TV series version of it, right? The story there is civilization's declining in. You can't stop that, but maybe you can set up something for the next revival, right? Uh, Mars, any, any, any worth it, any good chats? It doesn't seem that all plausible to me, that is, well, I mean, obviously you can put some people on Mars, but per dollar spent, it's a crazy expense for the number of people that you, I mean, Mars, isn't actually very independent from earth is if somebody on earth doesn't like, what's happening on Mars, you can throw something at them and smash them pretty easy.
So I would, I would rather have sort of diverse subcultures and places on earth, which sort of are under the radar, but, uh, having different technologies and abilities and, um, you know, that are available to, to revive later.
Yeah. Uh, let's try to maybe end this on a more positive note. Uh, what opportunities are available here for contrarians? I think, uh, Peter Thiel would say that contrarians matter more than basically anyone else, since they're the ones who create these kinds of new dimensions for us to explore, as he says, uh, go from zero to one. Uh, and I think a situation that you have right now is that the more kind of, uh, homogenized a lot of these spaces are the more.
If you win, if you end up winning as a contrarian, the more those rewards increase. Right? So how, how should we go approaching this problem? And is there a way to inspire and empower these contrarians? So the more conformity there is a more conformity pressures. There are then the more marginal gains you get by defining them if you can sustainably do so.
So if, for example, you get tenure as a professor. Then at that moment, you can defy all academic conventions and pressures to just research, whatever you think is interesting, regardless of how much other people think it's interesting. And there are many other places in the world where you have such slack or freedom.
Once you pass a certain point where you can just do it your way. And so, uh, and then of course the more conformance everybody else's research is the more progress you can make with your alternative research in areas that are neglected. I would say that for the larger society, the biggest leverage things to do are, uh, trials of innovations.
That is a new social institutions. In particular, the main limitation is just small scale trials of them. Uh, you know, forgets like even we talked about prediction markets, the problem, main problem isn't trying to pass change the laws about vetting main problem is just to get any small companies to do these small scale trials of things that would be very valuable.
And once you, once you get some small scale successful trials, that of course you could get others to be envious and jealous, to copy and spread them once you have concrete successes. And there are a lot of. Ideas that are promising to try at small scales that could then spread. And so that's, to me is the obvious thing to recommend is if you're willing to be a contrarian, don't just write contrary and blog posts or Twitter tweets, or, you know, contract contrarian, screeds, or podcasts, uh, where you're just talking about how everybody should be contrary and just go, go do something, pick a concrete thing and just try a concrete idea out differently.
And, you know, there's plenty of ideas that are, that are worth trying out concretely. But of course you want to get as quickly as possible to a point where you get data, where you see some feedback about how it's going. It doesn't mean you completely rejected if the first date is negative, but that's where you get to the point of learning what else to try.
So, uh, yes, and in venture capital is of course does a lot of that, but there's a lot of things venture capital won't do because you know, of the particular ways that you have intellectual property and kind of markets that exists. So, um, there's also room for non venture capital based trying something new, right?
Uh, I think that's actually a very hopeful place and I do plan to be doing some of those types of things in the, in the very near future as well. Uh, with that, thank you so much for being here. This was an incredibly enjoyable conversation and I really do hope to, uh, invite you on very soon, uh, to have even further discussions on some of these ideas, happy to talk.
That was our podcast with Robin Hanson. We covered truly a wide range of topics, but somehow we still didn't have enough time to hash out the most important argument of all, at least in my opinion, that was of course whether our decline and progress was caused by a failure of institutional selection or global conformity in a way, this is the best type of argument to have since neither of us really disagree on the direction of the problem.
But we disagree in terms of priority. That being said, this has made me rethink my approach to libertarianism and my approach to these kinds of more decentralization type movements. I think Robin really does make a convincing argument and especially for the short and medium term, it does make a lot of sense to go with these types of approaches.
I'm not quite sure about the same thing in the longer term though, because even while you've had either decentralization or centralization in various areas and sectors, you still have this underlying pattern. And not only that the political effort required to achieve these two things. I think at least in my view, don't differ nearly as much as he thought.
I'd really like to have him on again, at some point in the future, in order to just hash out these disagreements and really get into the nuance and data in terms of analyzing these two different paths moving forward. Other than that, it was a really enjoyable interview and I'm glad to have had him on the show.
If you enjoyed it, as much as I did, then please subscribe to the podcast for more episodes like this. And also, as I said already, please recommend it to your friends and family. This is something that you can have a lot of power over, and it really does matter in terms of what podcasts have an engaged audience who are really into the podcast who want more people to find out about it.
And what podcasts are just kind of floating by. And I would really appreciate you helping make us more of the former. See you again for another great episode. Next time.