Robin Hanson on prediction markets and decision markets

Transcript

Roughly a century or so ago in the United States, there was more money bet in US presidential election betting markets than there was in the stock market at the time. A federal judge overruled Trump's tariffs, and now the market is left waiting for a ruling from the Supreme Court. Meanwhile, Trump is trying to fire a Fed governor, all while pushing for interest rate cuts, which are expected later this month. Markets hate uncertainty, and it seems that we have run into a lot of it lately. Hello and welcome to Milk Road Macro, the podcast that isn't afraid to ask if the Supreme Court wants to just join the FOMC2 because at this point, why not? I'm your host, John Gillan. Today is Monday, September 1st, and today we are joined by the illustrious Professor Robin Hansen. Professor Hansen is an economist, author, and uh many other

things. He serves as the associate professor of economics at George Mason University and was previously a research associate at the future of humanity institute at Oxford University. He holds degrees in social science, physics, philosophy, maybe a bunch of other things. I don't even know. He does everything. And he is perhaps best known for his pioneering work on prediction markets. I'm going to have Robin predict the future for us and tell us everything we need to know about the market. So, if that sounds good to you, make sure that you like and subscribe. Share this episode with someone who would enjoy it. Uh, and as a reminder for our listeners on YouTube, make sure you hit the like button, the subscribe button. our podcast listeners, a five-star rating and review would mean the world to us and really helps us grow the channel and get this great content and educational

material out to more people. Uh, and as a reminder, today's episode is brought to you by our wonderful sponsor Ken, the verified distribution protocol changing the growth game. And without further ado, welcome to Milk Road Macro, Professor Robin Hansen. How are you, sir? >> Great to be here. What should we talk about? >> Well, that's what we're going to have to figure out, I guess, between the two of us. Look, I I think a good place to start, something that's on a lot of people's minds. Um the there was a federal judge who recently overturned President Trump's tariffs policy. Now, we're going to wait, it looks like, for a long time, on a ruling from the Supreme Court. Uh the legal system moves very slowly. By the time this gets to an adjudication, there could be trillions of dollars at stake one way or the other

on this. Um what do you see happening here? What what are what do investors need to know? And how do you think the markets are going to digest all of this? Okay. So for framing, I specialize in thinking about the big picture over the long run. And so for I don't specialize in thinking about shortrun very specific things that happen. So I'm going to mainly try to put shortrun things in the larger context. >> That's totally welcome. It's called milkro macro. The big picture is always welcome. >> So first, as an economist, I got to say we economists are not too big on tariffs. Tariffs just look to us like mostly a mistake. And that's what I got to say as an economist.

But uh this is in the larger context of Trump and anti-Trump which is you know this huge cultural fight today. And clearly part of Trump's stick has been that he's not the usual sort of you know president with the usual sort of policy things. He's going to defy the usual frameworks. He's going to defy the usual you know restrictions and you know constraints. He's not just like going with the system and doing what it says. So, this does fit his brand, right?

He is going with his brand by saying, "I've, you know, there's this other thing. Other people say it's bad, but I say it's good and let me show you what I can do." And of course, it goes with his deal brand, right? One of his main brands is, "Hey, I can make deals and other people just don't make so many deals, and I'm going to make the world different as president with my deals." And so, he's trying to do that with peace, right, with Ukraine, etc. and he's trying to do that with tariffs. That is he says I'm art of the deal guy. I'm going to do deals. So it makes sense that this is the sort of thing he's doing. Uh but you know he is putting more weight on this than other things you might have thought he'd do. But it does fit his brand. And from the economist

point of view, you have to say overall it's not going to go that well. But it could solidify people behind him. If it doesn't go too badly, maybe the people on his side will say, "Yeah, we elected a guy who's going to do something different. He did something different." You know, all right. then they like him for it and like it he goes on and you know Vance becomes the next president etc. this could go kind of well for him in the long run if if he doesn't screw it up too badly like right >> there's a potential to screw this up pretty badly because tariffs can hurt things a lot but he he might not right so and it it's working for him and his brand >> this is this is an interesting point and I think you know obviously the larger cultural you know arm wrestling match

that we're living through is is part of this but there has been a lot of uncertainty that's been created by the the kind of radical policy changes he's made there's also opportunity there so I think you're kind of right. He either, you know, ends up on Mount Rushmore or somewhere else. Um, well, look, let's pivot to something that I think is a little bit more >> just one more thing on this. Like, if he manages not to screw this up and get more support, that might mean he can do other things he wants to do. >> Sure. >> So, right, whether or not this goes especially goodly or badly is like a entry. I mean, we saw the dodge at the beginning trying to do some lots of big things and may, you know, that seems to be cut back, but there's a potential clearly for Trump to do a lot more

bigger things than he did in his first term. even if tariffs go badly for for God's sake. So that's part of the big bet here is where else will Trump go to try to do big things. >> Could he make a deal social security to reform it, right? I mean, you know, what sort of big deals could he be trying to make? >> All right. Well, let's let's not go thrown around. Let's not give him any crazy ideas here, but um yeah, well, this is this is part of what I want to kind of keep this conversation going because um seeing what happens next and following these major trends is is really important, I think, for investors to do. And it's not so much dogmatic about what must happen or what will happen, but just seeing what does happen and and what might potentially come on

the horizon. Um I I want to pivot to prediction markets because I think our audience is particularly interested in the rise in popularity of prediction markets. Your work for many years has focused on these. Um tell us just a little bit about how you initially got interested into the subject of prediction markets and why are prediction markets so important and such a powerful tool. >> There was a time before there was a worldwide web. You might not know that or remember that, but there really was a time way back in the, you know, around 1990 or so when there wasn't a worldwide web. And I was then involved with a group of people who were envisioning the worldwide web and where what it could be and where it could go. And in the process of digging into that, I came to doubt more that the worldwide web could

achieve the hopes we had for how it could reform society and make everything better. And so I was looking for something different that could achieve similar goals. And because most of the people I hanging out with were kind of rabid libertarians, the idea of betting markets was accessible to me. And so I thought, well, could we use betting markets to solve many of our social problems? Many of our social problems being we don't know what to do. We we don't know what our problems are. We don't know which policies would be effective. And then it looked like, oh well, betting markets could address that. That is, you had a betting market on a policy question, a big question.

Then the odds would be a good consensus on that. And so I started to think about and talk about how could we use betting markets much more than we had to address big important questions in our world. And so I started to do that in the you know around 1990 so 35 years ago now. And uh then you know in the next you know decade there started to be this burst of prediction market activity >> right >> in the United States in the as there were a number of companies doing this and experimenting with this and so I was involved then with you know advising and helping people try things and the key idea is that we've had speculative markets for centuries whose main

function is to hedge risk as many of your presumably viewers here are very well aware most people think of the main function of financial markets as hedging risk. They have a side effect of generating information but we don't tend to think of generating and creating a market and sustaining it for the purpose of the information. But the new idea here is oh if you want to know say if your project will make its deadline you make a speculative market on that and then the odds right there tell you will you make your deadline and you can then do things to you know based on that information right >> so it doesn't have to be a large market it doesn't have to have a lot of money involved >> in order to aggregate the information of the participants >> and so it looked like a promising

mechanism for society not just that but many organizations like which have issues like what project products to produce or what projects to work on and continue with. And so there seem to be this potential for informing organizations by their purposely making vetting markets and the topics they cared about. >> Gotcha. So the the benefit of markets is not just financial return or hedging out risk, but there's also a aformational element to a marketplace and that can help be used to help drive decisions. Um I I've seen a lot of research that says that prediction markets are actually oftentimes much more ex uh accurate than experts. Um we've seen this in elections and so forth uh recently. Um why do

prediction markets so often beat expert predictions? What makes them so much more powerful and accurate than experts on these things? >> So so the main thing to say is as an economist, I'm a professor of economics. We know a lot of things and people in finance know a lot of things about how financial markets work and why they are accurate. But the main reason that you should be interested is we just have this empirical um literature that says when we systematically compare financial market prices to anything else at the same time estimating the same thing with anything remotely comparable resources the prediction market is either about the same or substantially better.

That's the empirical fact and that's the main reason I would recommend it to you. Now you're curious and you think why and then I can tell you why in part. So one reason is just you have this straightforward financial incentive. If you see a price and you think it's wrong, you expect a profit by fixing it. So that's as with all financial markets an incentive to you know correct errors and add information. But more than that um it's not just that base information. It's looking at any biases in the process. So if you look at the process of prices in the past and you tend to you find any sort of bias any pattern whereby you could have predicted things better than the price did at the time

you're empowered to use that to make money. So that is this is a process whereby people come in what they know and they try to you know use it to make money but that could have all sorts of errors and biases but if you can find any error or bias in that process you can fix that by betting on that. So, it's a way that it corrects for its own biases. And in addition, if you think you know, but you're wrong, this process teaches you to shut up and go away, which is a problem with all the rest of our institutions in the world, including podcasts and everything else. There's a lot of people out there who don't know what they're talking about, but they keep talking and it's hard to make them shut up and go away. But prediction markets have this nice clear signal. You

think you know something, you trade, and you just keep losing money. you soon after you go, I guess I don't know as much as I thought and you go away. >> This is going to be a great conversation. Um, I'm enjoying this a lot, professor. Um, so, okay, I appreciate that. I think that what you're saying is that it educates people who have incorrect information and it rewards people for, you know, if they have correct information or they know better than the market, it rewards them and incentivizes them to contribute that information and to correct that that in the marketplace. Um, I'm I'm really curious your thoughts on what makes so many people so uncomfortable about the idea of permission prediction markets because it's taken a really long time to get these to become more mainstream. Uh, I don't want to go too much down a rabbit

hole on this, but this was a really interesting anecdote because you have a long history in this. You'd worked with DARPA on on a prediction market for, you know, some sort of counterterrorism thing and were accused of creating gambling markets for like, you know, betting on terrorist attacks and all kinds of things like this. what is the the the driving thing behind all this push back against and resistance to prediction markets and the benefit that they could offer to society. >> So our world is full of social institutions and it's full of talk. We're talking right now and the world does a lot of talking and almost all of our institutions are full of talking. So we have, you know, churches and we have firms, we have government and in all these places we've got people in roles who have responsibility supposed to make

choices and they have some incentives. They get promoted or fired and they're just talking to each other all the time. And we're pretty comfortable with the idea of people talking, maybe they could lie, maybe they are telling the truth, and using all sorts of ways to judge who should be in what role and who do we trust based on what we've heard them say and who's talked about them and what sort of credentials they have in terms of education or other sorts of prestigious backgrounds. That's our world to a large extent is organizations, individual making choices in the context of prestige credentials education all based on talking. Education is almost entirely based in talking. Most prestige

is based on talking. People of the New York Times or people who have government, you know, positions, etc. It's all really based on people talking, people judging what other people say, people saying who's impressed with people talking and then promoting people and firing them all based on all this talk. Our world is full of stuff that happens and a world of talk about it. And we're we know talk can have many problems, but this is the world we're comfortable with. And when you think, oh, what if we use betting markets in that world to replace some of this talking? Well, first of all, that's jarring. Okay, it's like this alien thing coming into this world of familiar. It's like, you know, you have porpuses and other nice mammals in

the ocean. Then you got octopi. What the hell is this octopi thing? It seems really strange to include an octtopi among all the familiar mammals or something. And then we we know that prediction markets and financial markets, money's involved very explicitly. And the fact that money is so prominent and forward makes us a bit uncomfortable. line. We know in all this world of people with jobs and positions and talking, money is heavily involved, but we suppress it into the background and we kind of pretend that money is only, you know, a consequence and side effect of the talking. And here in the betting markets, the the money is directly driving these prices and without talking being the intermediary.

Like somebody is just allowed to have money and then bet and then have stuff happen and the rest of us don't have to approve of him. He doesn't have to get our, you know, oh, he went to Harvard, what a great guy. this this stuff can just happen without the world of talking approving and that feels like it's out of control, right? And then people have this intuition. Look, when there's money involved, people could cheat and they and they could, you know, manipulate things and you have to you can't trust places where money's involved. Well, like, well, of course, money is involved all the rest of our world, too. But somehow we think, yeah, but we've got it under control. Sure, there's money there, but the talking is like managing all that and making sure the money doesn't get out of hand. But if like

money is straightforward the central thing and money can just have force and and have effect without talking like being the controlling and then we go that's just out of control. So this isn't just hypothetical in our standard organizations. Um in fact a lot of the talking is not entirely honest and people in them kind of know that. So you know the standard way an organization presents itself to the world is as if each person is kind of like a scientific decision maker.

He's got a spreadsheet. He's needs to fill in the cells with the numbers he gets by talking to people and he makes his calculations and what decision to do. Pass that up. The world of management presents itself as this scientific decisionmaking process of collecting numbers and filling them in. And each one is just an information aggregator and decider that's making these calculations. That's the presentation we give. And then every speech they give is saying, "This is just the results of my spreadsheet. Here's just the calculation I did. That's just, you know, what we're doing right here." Now, if you allow a prediction market to sit right next to those things people are saying with words, claiming they come from their spreadsheet, what they know was that the prediction market won't agree with the

words they're saying. And they most people in management know that the the words they're saying are a carefully calculated impression management uh strategic choice to inspire people and to push away rivals and to sort of set agendas. People involved in organizations know that the stuff they're saying isn't literally true and isn't the best estimate of the fact, but they have reasons why they think that's the most effective thing to say for organizations. >> It's public relations. Yeah. >> Right. Right. Of all sorts. And many of it is sensible. We can talk about some of it like some of it that actually makes complete sense.

But so it's like imagine at the seauite table there's all these smart people sitting around having their discussion about you know the firm's business and so and say you put an autist who just knows the company really well but has no clue about social skills. They they just have no idea what anybody wants to hear, what anybody's agenda is. So whenever a subject comes up, they just blurt out their best expert, you know, knowledge on the topic. That's going to be really disruptive at that table. Why? And and they won't let that person sit at that table very long.

They might become a good trusted adviser to somebody sits at the table. They will not be allowed to sit at the table and just blurt out whatever pops in their head. That just won't happen. But that's what a prediction market is, >> right? So >> prediction market has no social savvy. It has no idea what anybody wants to hear and what anybody's agendas is just going to blast out every piece of information it gets in regardless of who wants to hear it or how that would disturb anybody >> and that's what it's just doing all the time. It's very disruptive to the usual process. >> It's just h giving people an opportunity to aggregate truth and it's telling that truth without reverence to whose feelings might be hurt. I think something else you said that I want to underscore from that answer um is that

uh you know talking verbal communication physical non-verbal communication is something we're very familiar with in our society. However, like you you're saying these markets really are a way for people to articulate themselves and to speak in a different language, right, which is the language of capital. Um that is a very powerful thing to unlock. Right. Right. When they don't need to ask anyone's permission, they can do it themselves organically. Um so I I can definitely understand why that makes people uncomfortable um or certain leaders let's say uncomfortable. So I just wanted to like give that context to the audience. Um recently especially in crypto digital assets we've seen a a big rise in popularity of prediction markets things like poly market um ki um where users are betting on things like the

outcome of uncertain f future events like elections and so forth. Um what has your estimation of this been? Do you think this is a net good for society? Do you like these these prediction markets that you've seen? What would you like to see change? Like what are your thoughts on this general trend that we've seen in the market? >> Okay, so if you go back centuries, certainly a century, I don't know if most people know, roughly a century or so ago in the United States, there was more money bet in US presidential election betting markets than there was in the stock market at the time. Stock stock market was small. Most businesses got their investment through banks, not through stocks. And so, uh, we've had a long history of there being markets where people traded. And the main two reasons people traded in those markets

was one, you want to hedge risk, and another, you want to speculate. You just, you think you know something, you think you know better than other people. And you want to bet to show that you know better than other people and maybe make some money as a side effect. Okay. So, the point is those two functions of markets aren't the only things markets can do. Another function as we talked about was aggregate information and that's the new thing that I'm hoping to see explored a lot better and if you look in say Khi and poly market it's those two old functions that are primarily h being achieved there that is most of the people trading or sponsoring markets in poly market are still doing it because either they're trying to

hedge a risk or they think they have some insight that they're trying to you know win out over other people The thing I'm most excited about is when this third function becomes prominent and central and that's not happening so far yet in poly market or calci but it is happening in some of the crypto organizations that are using what I call decision markets or futuri to make decisions in the organization. So the first one that started doing that a lot was called metadow. Uh then uh since then some other organizations including um butter and uh nosis through futarchy fi and seed there's a number of

experiments going on where people are using decision markets to advise decisions in these organizations and that's what I have hope for because that's the new purpose of using the markets to get information and so in this new purpose the customer is the person who wants the information. There's the one who's paying primarily to make the whole thing happen. And then because of the subsidies they create, other people are willing to participate, not because they want to hedge risk, but because they want to get paid by these subsidies. And then the information that's being asked for is generated and then decisions get made based on that information.

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So, I wanted to I'm glad we got to this point. I think there are a lot of directions I could take this question. Um, the first one I want to like tie this back to the headlines and sort of like a so what thing and I'd like to ask about the the Bureau of Labor Statistics and the labor data. Uh, and then also I like bring this towards the Fed and interest rates, but I want to start with the the Bureau of Labor Statistics. There was a huge negative revision in the jobs data that led to Trump firing the head of the Bureau of Labor Statistics. Um, I want to get your thoughts on the the data collection methods that they're using there and how prediction markets could help with something like this where we're trying to get aggregate economic data that we want to, you know, have be high

veracity, right? Where we're not having huge revisions in the data. >> That's the question. Do we want that? >> So, so like well I mean theoretically we would like to have good data to make decisions. >> Theoretically that's the whole point here, right? So just like in most companies, people in management, the words they're saying are not the best estimates that they know privately because they think they are they have these various things they're trying to do. That's also true in government with respect to labor data or the estimates of cost of projects of all sorts of things. In government, we create a lot of numbers. We have statistics departments that generate numbers and then they forecast numbers. We have numbers forecasted for budgets, numbered forecasted for the public and the people

generating those numbers and the people you know talking about those numbers and presenting them to the wider public they all have agendas about what they want people to believe from these numbers. So none of them are purely just trying to get the number to be the most accurate and most people in this process know that but still they present themselves to the world as if they were just trying to get the most accurate numbers. So if you put this autistic market process in the way you would in fact get the most accurate numbers and they're not so sure they want that. So for example um many years ago the US had probably still has what's called a missile test agency.

This organization puts together missile tests and these are these require different organizations in the military and the missile related government agencies to coordinate together. basically, you know, they're somewhere a missile's launched and it lands out in the ocean. They need ships to be in the right place and the need planes to be in the right place and the missile in the right place. So, basically, they have to coordinate to get all these things together to make a test happen. And sometimes the test just doesn't happen as some of the parts just aren't ready. So, quite often a lot of money is wasted because tests don't happen that some people got ready for that other people didn't get ready for. So, they created a betting market on whether the test would actually happen. And this actually gave us more accurate information about which tests will actually happen and which

won't. And that was able to save money in terms of, you know, bailing on stuff that's not going to happen. But it was quickly scrapped because it made it more clear who to blame about the failures. And apparently it's just more important not to be clear who to blame than to actually know like which tests are going to happen. This is just ubiquitous in most organizations including the government everywhere. You should just notice pretty much all the numbers governments forecast, if they had a betting market on that, that forecast would in fact be more accurate and they don't create the betting market. And this is this is true for all the organizations anywhere you see in the world, anytime you see anybody forecasting things and there's not a betting market on that, you should ask yourself, why don't they make a

betting market, won't that give us a more accurate forecast? And the answer is going to be yes, it will. And they don't want it. >> Yeah. Do they have some reason why they're massaging the forecast to be the one they're showing you? >> Right. Yeah. And I think that that's, you know, it's kind of an uncomfortable truth to hear said out loud, but I think that there is some value in preserving sort of this like gray area and doubt and the ability to kind of >> let's talk about the value in organizations of inaccurate forecasts. Let's just be let's just be honest and clear about that. Let's go through a couple of those.

>> Sure. Go ahead. One, we have data where you have two levels of management and three total levels. At the bottom, there's line workers, say writing software. One level above them, there's the line managers. They manage the line workers writing software. And above them, there are bosses who manage those managers. These intermediate managers are often called to make forecasts about particular projects they might do and how long it'll take, how many resources it would take to make any particular project asked for at the higher level. And these managers differ in their accuracy at this middle level. Some of them, if they give you a forecast, it'll be, you know, right on the money. It'll be very accurate. And some of them are not so accurate in giving their

forecasts. And typically the way they're not accurate is they're optimistic. they will tell you they will can do more than they actually can. Now it turns out the higher level managers prefer the inaccurate middle level managers because when they predict they will be able to do something with so much resources and it starts to turn out that they're not going to be able to do as much as they claim they could do, they push themselves and their people to do more than they otherwise would. And the higher level managers prefer the push to the accuracy.

That is, you'd rather have subordinates who just do more even if they don't do as much as they claim than who do less because the act the managers who are accurate in their forecast, they're predicting roughly what they could do if if they only tried to a modest degree, not to an unusual degree. And then they won't have to try so hard. And then the ones who promise overpromise, they will then as they reach the crunch of not making their promise, they will push harder to get more done. So, and more generally, if you think about say a project with a deadline, if you're really sure not to make the deadline or really sure you will make it, you put in less effort than if you're right on the borderline of we might make it if only we just put in a

big push here to make the deadline. >> Right? >> So, I remember seeing this in the third Lord of the Rings movie. I that's one of the few times I ever watched the director's commentary about it. And they tell the story of how, you know, the director there just asked for a lot of CGI stuff, way more than the previous movies. And then he had this deadline. Look, the movie's coming out on this day. You need all this stuff to be done, otherwise you know, we can't release the movie. And he got a lot of work out of people by making them think they might be able to make the deadline if only if they pushed really hard to make it by the deadline. So managers know this. Managers know they can motivate and get more work out of people. if they put them on the edge of

uncertainty about whether they'll make deadlines, which means if the truth is you're pretty sure to make it or not, you don't want to tell that truth, you want to hide that truth and make them uncertain. >> Yeah, I think there's a lot of wisdom in that. And I think that a lot of people who have managers can attest to the the truth of that statement. Um, I want to get your thoughts on something that happened pretty recently. And I know like you said you're more focused on broader trends, but something around this gave me some hope. And I I want to also tell you a quick story like there there's a guy named Sergey Nazerof who's the head of chain link protocol which is an an oracle network that uses, you know, decentralized networks to create information and and store that in a verifiable way on chain and and give

that to smart contracts and so forth. And he talks a lot about transitioning the world from a system of trust to a system of truth specifically to solve this problem, right? Of like when you have to trust all this data from people, they can be massaging it, manipulating it, whatever word you want to use in order to, you know, achieve some ends that favor them or or disadvantage others. Um, and so moving on to the blockchain is a way to transition from this system of trust to truth to that end. The commerce department recently announced that they're planning to begin publishing economic data like GDP on the blockchain. Um and they're partnering with different uh different protocols, different networks to do this. Um but this is just an initial step. Obviously, there's a long way to go with this. But do you think that this is a promising

trend? Do you think that moving a lot of this economic data and just a lot of things in general on chain onto the system of of truth as opposed to trust? Do you think that's a direction that we should explore further? Like what are your thoughts on that as a solution to this like largecale social problem we have of like I don't know just a systemic >> disingenuous and false information. The chain literally mainly prevents people from editing historical records. Uh but we haven't so much had a problem with people editing historical records for a while. I mean that's a thing that has you know it's part of 1984 say uh part of the novel is the hero edits historical records and there have been times and places in the world where you can't trust historical records. They in fact get it. But I think in our world

today editing historical records isn't really the main obstacle to truth. So the question is whether the records put on the chain are true not what was on the chain. Uh so for example many organizations they have votes and then the vote is on the chain and then things happen as a result of the votes. But the question is are the votes reflecting something true or various you know politics and manipulation. So I'd more worry about what's on the chain and how you're producing that than whether it's on a chain. I mean the chain literally as you know the main thing a chain assures you of is that the record wasn't changed. A chain is a is a sequence of records that can't have been changed

because uh you couldn't change that record without changing all the records afterwards. So once you have a long enough stream of records that you trust a lot of it, you know that you trust all the records because they couldn't have changed uh very you know they couldn't have changed any of them unless they had to change all of them. >> Right. Yeah. And so what you're describing is why I think chain link is so powerful. Um not to like you know talk about one specific protocol or anything not financial advice but the way that they get their their their data network their data from their oracle network is through what you're kind of saying like this decentralized methodology to make sure that the data that they are storing is high quality verifiable and and unchanged as you say.

I wanna um well so before we move on to a new topic I want to ask one more question um just for the the joke of it really but um the markets are predicting with like a 90% likelihood a 25 basis point rate cut by the Fed in September um I think that the way that we currently make these choices about economic policy or monetary policy is very much reliant on like we said like un un like unreliable data and then just the the decision-m of a few people in Washington. How could we use prediction markets or or change the way that we're setting monetary policy to make sure that we're not chronically behind or off on on where we are with monetary policy?

>> Decision markets, which I talked to you about before, is something that many crypto organizations are now using. Um, let me just walk through an example and then tell you how it works for the for the Fed. An example of a decision market would be deciding the fire of the CEO of a company based on a conditional stock price. That is you can set up the market so that you have a value of the company, the stock price if the CEO stays and another value of the stock price if the CEO leaves. You can actually just get two different prices from special conditional markets. And then you can compare those prices to say, well, let's keep the CEO if the value of the company is higher if we keep them than if we let them go. That's a key example of a decision market in the more general

process is we need an outcome metric that we think tracks the thing we care about, in this case, the stock price. And then we need discrete decisions, keep the CEO or not. And then we need to estimate this outcome metric we care about conditional on each of the discrete decisions. And then that's enough to get a recommendation from the market about which decision to take. Okay. So now for the monetary policy, it's a nice example of this because we have a nice discrete decision. Every time we raise or lower interest rates, there's a limited number of options. There's at most five different options we can be considering of, you know, raise the interest rate a lot, a little, do nothing, lower the interest rate a little, lower it a lot. There's not very many options. And so for each option, we could ask a market to estimate some

consequence. And then the key question is, can we agree on the consequence we care about for macro policy? And it turns out we can. That is the literature on macroeconomics doesn't disagree that much on the outcome we care about. It's some simple weighted average of future unemployment and GDP growth and it doesn't really need to have much else as long as you estimate that in, you know, 5 years later or something. And then we could just estimate those two quantities, you know, some weighted average of them conditional on which interest rate option we pick. And that would be a decision market that could replace the bulk Fed board in making these decisions. And arguably it would do at least as good a job because we have all

the rest of this literature saying that when we compare prediction markets to other things they do at least as good a job in forecasting. So mo almost all the expertise of the Fed is to try to make the judgment of the connection between whether to raise or lower interest rates and these long-term consequences we all agree are the consequences we care about. And that's a perfect application of futur. Basically we could just do futur directly for that decision. Awesome. Um, just so the audience is aware and I'm clear on this too. I I looked this up before, but what is a futuri?

>> It's the same as a decision market. So, I just gave you this example of the CEO. So, the stock conditional on keeping the CEO, stock conditional on getting rid of the CEO. That is a decision market. It's just decision conditional estimates of the outcome you care about. >> Right. And that that hierarchy, monarchy, th those those words mean power within kind of Exactly. Right. Exactly. I just want to make sure >> I tried to say a future form of government was the way I named it a long time ago, but the name, you know, it's kind of too late to change it. So >> Yeah. Yeah. Yeah. No, we we've we've gone down this road a long way with that. We we'll make it work. But I just wanted the audience to understand like the idea that we're talking about is a a different way of investing power and of

decision-m such that we are all able to contribute in a more direct way and in and one that doesn't revolve around verbal communication. I wanted to ask one other thing about this which is that so what we've been discussing here are potential reformations to how we make decisions at a social level, how the Fed makes decisions. Um, right now there's been a lot of conversation around reform of the Federal Reserve from the Secretary of the Treasury, from other people in this administration and there's this kind of like worry that the Federal Reserve is going to be losing its independence. I just wanted to kind of get your thoughts on this as somebody who is a, you know, a student of history and an economist. Um, what do you see as sort of like likely outcomes from this?

Have you been paying attention to this? Do you think the Fed is going to lose their independence? Like what are your thoughts on this whole conversation? I just proposed a pretty dramatic reform of the Fed in this last few minutes. So I would therefore like if people would consider changing the Fed because I think there are ways to make it better. But the basic fact of especially America governance for the last 250 years is that we have not changed very much for a long time. So most of the rest of the world has made much more dramatic changes to their institutions of governance, their election institutions, their government agencies than has the United States. And the reason you can see and this is happening in this case

too whenever someone proposes changes then that's proposed usually by somebody who's in power and then the other side says, "Oh, they're trying to change that now because they're trying to undermine the protections that the current system has had for you. you can't trust them while they're in power. Wait till we're in power and then let us change the system. And you can see, you know, that just means it never gets never changes, right? So only if the two sides would get together and have some sort of, you know, conference committee summit where they agreed together on a change, but they don't want to do that uh because, you know, they're hoping that they're going to be in charge next time and then they would get to change it more their direction. So unfortunately, the United States just has not changed its institutions very much for a long time compared again to

most of the rest of the world. We're sort of locked in. But it's also interesting that there's just very little emotional passion there. So if you look at people's passion about politics, the things they're most passionate about are the things that align with these cultural fights where there's an us and a them. And you know, you're fighting for us side and those against those evil guys. People love those fights. So look, anything Trump's pushing the other side is against, everybody's all over that. When you start to talk about the structure that this whole thing takes place in and the larger institutions, people just get bored really fast. They they just can't be bothered with that discussion. So for example, in the last half century, most governments when they made major changes to institutions of democracy, they

picked proportional representation. And there's a literature that suggests it's just a better form of democracy than what we started with 250 years ago. But you know what? Nobody wants to make those changes here because you know first of all you know people somebody loses somebody wins and the losers don't want to allow it. But then the public just has almost no passion for any of these institutional changes. And that's a problem for the institutional changes I'm proposing too. We'll have to wait till some period where there's more openness or passion or somehow it becomes a left a you know us them thing which case one party gets in control and they have enough power to push it through and the other side hates it but somehow change happens anyway.

>> Well I hope it doesn't become an us them thing. I think we've seen people from the digital asset space and the crypto industry I think are generally familiar with this though because you know there was operation choke point 2.0 and the the Biden administration and now the Trump administration is very friendly to digital assets. Um, and when it becomes this sort of political football and like a cultural war that people are only interested in because the other side is opposed to it, I think that's like toxic and unhealthy. And sometimes you do get changes, but they're not usually in a positive or or generally constructive direction. And yeah, you know, I I appreciate the feedback that these are boring subjects that most people will tune out on. But part of what I'm interested in doing with this conversation is to to draw people's minds and attention towards what is

possible and to just make them aware that hey there's a better mousetrap that we could build and it wouldn't really be all that hard, right? So we have the tools to make these things better. Um I I want to just kind of close with this like we're we're going into the second half of this year. Obviously, you know, rate cuts haven't come yet, but a lot of people are predicting that they are. Just from like a macroeconomics perspective, you know, what should investors be watching for as we go into the end of this year? What's the the most important thing for them to pay attention to? Is it you know >> say a boring thing >> which is just on macro things like this we have a number of financial market prices there are you know interest rate futures and there are options on these

and those give a full probability distribution over all the o possibilities you're thinking about and you should just believe those probability distributions and if you think you know more than those distributions what you should really do is trade in those markets make money on your insight and now all the rest of us can benefit from your insight. But if you don't have any special insight, just look at those distributions and that's what you should believe. And you shouldn't listen to some guy on a podcast like me telling you something different than the market price out there. You should say, "Look, Robin, if you know better than the price, you go trade and fix that and make your millions. Why believe? Why make you listen to me on the podcast?

That's just irresponsible of me to pretend I know more than the market price here." >> Well, this has been a lot of fun. I've learned that I'm boring and I'm inefficient and I should shut up because too much talking is bad for everyone. Um, but no, look, I I appreciate the cander with a lot of these answers and this has been a really fun and enjoyable conversation for me. I feel like I've learned a lot. I hope the audience has too. Um, there are a lot of things I've been wanting to ask you about that I just did not get to and we're not going to have time to today. Um, so we probably >> if you're on the mood, >> we would well, you know, if if we haven't uh cancelled this show because it's too boring and a waste of time, then then we might have to do that. Uh,

one final question. Where can we send people to find more of you and your work online? >> I'm Robin Hansen. So, fortunately, I'm the most popular Robin Hansen on the internet. So, if you just search for me, you'll find me. >> Thanks, professor. Thanks for being here. I really enjoyed it. Thanks for being on Milk Road Macro. All right, great. And thank you all for joining us. Uh, until next time. Uh, stay safe, stay educated, stay bullish, and we hope we'll see you on a future episode of Milk Road Macro. Thanks for being here, everyone. Bye. Want insights on what's really moving markets and how we're trading each event? Subscribe to our channel, then join the Milk Road Macro and Macro Pro newsletters. This show is for educational purposes only. Nothing we say is financial advice. Investing is risky. Never invest more than you can

afford to lose.