# Public epistemics and futarchy

- Speakers: [Robin Hanson](https://streameth.org/speakers/robin-hanson)
- Channel: [Devcon 7 SEA](https://streameth.org/devcon_7_sea)
- Date: 2024-11-16
- Duration: 15:34
- Watch: https://streameth.org/watch/67381fca1b0f83434d0dfd4b

## Description

35 years ago I began outlining a vision of how betting markets could offer informed credibly-neutral estimates on far more disputed topics. I elaborated 25 years ago on how decision markets could support neutral governance, and 21 years ago on how combinatorial markets allow estimates on all possible combinations for existing topics. Now in the last year, we are seeing substantial crypto-based trials, especially re governance. In this talk, I’ll paint a picture of where all this could go.

## About the speakers

### Robin Hanson

http://hanson.gmu.edu/bio.html


## Transcript

Leonardo Silva Reviewer:" Peter van de Ven Long ago, I was part of a group of people who imagined the World Wide Web before there was a web. Many people didn't think it was very plausible. I left grad school to go off to join people working on that. But then within a few years, I started to have doubts about some of the visions people had about the web and what it could achieve. And I tried to think about other ways to solving that problem. And I came up with the idea, because I was hanging around a lot of rabid libertarians, that betting markets would be a powerful way to address many key questions in society, and I started to write about that. And within a few years, I helped develop some exchanges and experiments. And then about ten years later, i.e., I started writing about this stuff 35 years ago. 25 years ago, I realized that the highest value of information aggregation is when you can inform decisions closely. So I focused on what I call decision markets, advising markets that advise decisions. I wrote on that about 25 years ago, and one of the concepts I described was futarki. And then in the decade after that, I was working on combinatorial markets, which I'm going to tell you a bit about. And then in the last year, we've seen a number of concrete experiments on this concept of futarki or using conditional markets for governance. I'll tell you a bit about. And that's exciting and why I'm here and why you should just wait, if necessary, till you're old. Have ideas when you're young and wait till you're old because then, by then, something might have happened. So, I'm an economics professor and we social scientists have a number of lenses by which we understand society and human behavior. In economics, the most straightforward standard one is exchange of goods and services, but we also have game theory where we understand power and strategy, and political science is big on that. Sociology and anthropology are big on understanding things in terms of norms and prestige, which I've been focused on for the last year and catching up on things I didn't understand. But another lens for understanding human behavior is the idea that we reveal, infer, and aggregate information by our behaviors. And we have a lot of social institutions that do that. And in some sense, the activity of economists in the last half century has mostly been focused on modeling this aspect of human behavior, how we are signaling, showing, inferring, and aggregating information. And when you look at the many institutions we use for that, one small institution now stands out for its exceptional abilities and that is speculative markets. In comparison after comparison they just beat all the other institutions in terms of their ability to produce accurate estimates, timely and using modest amounts of resources compared to other things. So that creates this vision I have which is could we use speculative markets a lot more elsewhere in society to produce the information aggregation that we are now producing in worse ways through other institutions, like gossip, news, voting, academia, etc. Now, these four lenses probably aren't everything. There's a lot of social dark matter, and that should make us a little cautious about any claims we make about how to change society. But we should be trying this vision and seeing how far we can get with it. Now, one way to see my vision here is just to, for the moment, set aside your doubts about how feasible this is and think about all the different things we could apply this sort of thing to. So, for example, we could have markets on if X dated Y, how long would their relationship last? When you have a new hire, what would their employee evaluation be in a few years? If you have a project with a deadline, will it make the deadline? How would that change if you had changed resources, requirements, personnel? You could admit students to universities and other places based on expectation of graduating or GPA, conditional on admission. You could fire the CEO based on whether that was expected to change the stock price. You could do security analysis based on whether what the chance of red teams penetrating security is, given that you changed some approach. You could do various kinds of policy in terms of changing crime policy. How does that change the crime rate? The Fed changing interest rates. How does that predict inflation? Even things like antitrust. You could ask whether an industry will be healthier or not many years later if you make a certain antitrust move. I think once you start to see these examples and look around you, you'll see this everywhere. All over your world are decisions that could be improved by speculative markets. Just like even this conference, we could have had markets about, well, on what date do we have it, what location, what kind of speakers. We could have outcomes like how many people attend or other sorts of things. All of those are possible. Now you, hopefully, I presume at this point, you have doubts. Many, most of you probably think, yeah, but this isn't feasible. You just couldn't do this. There must be something in the way because we're not doing it now. This is, I'm a teacher. Most of my students are basically, always assume anything that isn't the status quo just must not work. And even you probably have doubts. But let me go farther with the vision a little bit before we get into how these things work. The grandest vision perhaps, I tried to pick what would be the most aspirational grand applications of this idea of conditional markets, and that would be governance. So at the moment, you can think of our democracy as a system where you have a bunch of citizens, they elect representatives who pass bills, and if the bills meet constitutional constraints, they become laws. And along this process, we have various information institutions that inform both the legislatures and the public about their beliefs about what bills have what consequences. My alternative, which I've called futarkey, long before I knew the other association of the word, sorry, is that we would still have legislatures to decide our values, to decide what outcomes we want, and they would basically vote on and create a measure of national welfare. But in addition, they would no longer decide what bills passed. Instead, people could propose new bills, say, through an auction agenda mechanism, and then we'd ask the markets, is national welfare going to be higher if we adopt this proposed bill versus not? And we'd just adopt the bills that do. So that would be a way to do governance, full governance, using betting markets. So markets just have a lot of advantages over other institutions. They're numerically precise. They're consistent across many issues. They're numerically precise. They're consistent across many issues. They're frequently updated. It turns out to be hard to manipulate, actually. And the key thing is when we do experimental tests comparing their accuracy to other institutions, they're either about the same or substantially better. That's the key thing that excites me and should excite you about using this in more places. And the vision isn't to just make betting markets and see who has fun betting there and only have the markets that people find fun. The idea is that if you want to know the answer to a question, you subsidize a market on your question, and then that attracts people to trade because of the subsidies you put there, and then you get the answer to your question, but you paid for it. So the idea is this is a market for information. You offer to pay for the information you want. People who have or could get the information, they generate it, and then you get better estimates. So we know lots of tricks about how to make this stuff work better. Market makers allow a lot of advantages. You can limit sabotage. It actually works better to have winners from one market, only winners allowed in the next market, that's more accuracy, and you can have problems that are hard to judge, be judged only randomly, and bet on what would happen if they were judged. We can do things like just having any sort of distribution of a curve, and basically estimate the curve, and you can make an edit, and and move the curve and then that's the new thing everybody else sees or you could have a big hierarchy like who's what the field of a Nobel Prize is gonna be in and you could edit anywhere in that hierarchy at a very fine-grained level or at a high level this is all quite feasible and to show you just how far we can go with the technology I'm going to tell you about combinatorial markets, which have been implemented, I'll tell you. The idea is not only do we have a set of claims, but we let people bet on all possible combinations of those claims. And the straightforward way is you have an edit-based interface. That is, you see a bunch of estimates, you're allowed to go pick any estimate you want and change it. That becomes a bet. And then the next person who sees it, they'll see the number you put there. But in addition, you can assume any number you see, assume any particular value. And now everything else you see is conditional on the assumption you made. And you can make several assumptions. So that's a general way to browse and edit a full combinatorial distribution. And it turns out that the subsidy you require to subsidize a combinatorial distribution is actually no more than subsidy for each of the individual claims. Now, of course, a full combinatorial distribution has a vast number of parameters, too many to be explicit. But there's a standard way to deal with that called a Bayes net. And a Bayesian network cuts the explosion by making key independence assumptions. And I was actually part of a project 10 years ago where we had a thousand questions we could have had more where we had a full base network connecting all these questions so that you could edit any part of this network and your updates would propagate through the whole network and we did the exact updates and exact management of people's assets so this is actually Yeah, it might be a little harder on the blockchain. I haven't worked on that. So do people actually do this stuff? Well, for a long time, we've had conditional markets on elections because we've had markets on who's going to be nominated, who's going to win. So the ratio of those two is, in fact, the chance of winning if you're nominated. And markets have long been giving the parties advice about who to nominate that mostly they've ignored, but it's there. But this year, we actually had two websites, Metaculous and Manifold, which are not full money prediction markets, but they have some incentives, offering conditional estimates on the election and a number of policy things. So this is a real thing that's now implemented and live for people to trade this election and no doubt will be even more popular next time around. Many years ago, actually, on this combinatorial site I talked about, we actually had conditional markets on changing the blockchain size for Bitcoin, at least, in order to estimate the consequence of policy there. So this happened. In the last few years, as I mentioned, we've had a number of efforts to apply this concept of conditional markets to governance. First, there is a secretive India-based government medical organization that's done roughly a half a dozen markets where they've asked hundreds of thousands of their customers to bet on various health policies that they've adopted or not. I think they adopted five out of six. A little bit later, we have the MetaDAO, which has apparently now done 23 proposals for governance of the MetaDAO, and only six of those were rejected. They have some customers now. 14 proposals, three rejected. You know, a lot of volume in trade. MetaDAO is now apparently focusing more on using governance to advise, say, MetaDAO grant proposals. Estimating the consequence of a grant proposal and seeing which ones to fund. There are some other organizations in this space. And I apologize if I don't know about yours and not mentioning them, but one that I do know about is called Buttery, and they're also focusing on this idea of funding projects in terms of estimating the outcomes for the projects. And I'm advising lots of these efforts, and in particular, I am chief scientist here at Futarki.fi, which was previously named Quiver. We have at least one customer, which is Gnosis, to do governance for them. We'll do it for ourselves. And we are interested in the question of how to do governance for organizations without bothering the bosses who don't want to be replaced. And one promising application there is the relationship between investors and managers. Often they're in dispute about when to raise more money and maybe a future arching mechanism can sit as an intermediary there to help them make that decision. All right, now, but I warned you about social dark matter. Key thing is that I've been describing a simple, elegant idea. There are a bunch of issues that we've worked through in a talk yesterday. I went through a number of the technical issues and problems. But just in general, all real-world things are not just simple, elegant ideas. They have a lot of messy details. And so we just need real- world experiments to work out those details because in some sense we don't fully understand human behavior. There's a lot of social dark matter. There's a lot of ways in which we make simple theories that should work and they don't. So we just need to try things. Let me elaborate a bit. Imagine we put an autistic person in the C suite of a typical corporation. They are a person who knows the business very well. They are very knowledgeable and informed about key decisions, but they have no social savvy. Whenever a topic comes up, they just blurt out whatever they think the truth is without having any idea whose agendas might be squashed by that. Such a person will just not last in the C-suite. they might become a trusted advisor of somebody else nearby but they won't be sitting there but that's in essence what prediction markets are they are very smart and knowledgeable things that speak the truth without knowing who's bothered by that and that highlights the fact that modern organizations a lot of what they do is politics and massaging and messages and so we need to search in the space of how to place these things in organizations. And 20 years ago, there was a wave of prediction market applications, and hundreds of firms tried them. But even though they typically had more accurate estimates, satisfied users, the experiments didn't continue, largely because of objections from managements who really got pissed when the market disagreed with them and the market was right. And so we need to deal with that. So a more concrete example, even, think of a project manager who has a deadline. They want to know what the chance of making the deadline, but they also want to have a good excuse if they fail. How will they do that? Well, their favorite excuse if they fail is to say, well, we were going along just fine until the last minute when something came out of left field, some weird thing, it's so rare and it'll never happen again, so let's just forget about it. And the problem is the prediction market gets in the way of that because it tells you a long time before, you were going to fail, you're going to fail, and you can't say we didn't know that until the last minute. So you can see we need to overcome this sort of a problem in organizations in order to be able to field them. And that's why we need to be doing these experiments, which I'm excited to be doing. And that's my talk.