0:00 Completed technology to Bitcoin, and do several other amazing things as well. So, Paul Sztorc, thanks. 0:12 This talk actually is not about Drivechain, although I'm sure it'll come up in the panel at noon. 0:23 Okay, so hi, my name is Paul Sztorc. I have a relatively mainstream background. 0:28 I went to public school in Connecticut for 12 years, and then I went to a private engineering college in Ohio, 0:34 where I studied economics and statistics. 0:37 I then worked a kind of pre-doctoral research statistician job at Yale University for two years for a man named 0:44 William Nordhaus, who just won the Nobel 0:47 Prize in Economics a few months ago, or the Swedish Bank Prize if you prefer, and so my economic training is relatively mainstream. 0:55 But despite all of that, I fell in love with Bitcoin in 2012, and I published Bitcoin research on my blog 1:01 Truthcoin.info since 2014, and I presented at many Bitcoin conferences, 1:07 especially the scaling Bitcoin conferences, as well as building on Bitcoin last summer and TabConf 1:13 earlier this year. 1:15 The topic of this presentation is an idea for getting the public sector, and I put it there, 1:21 I put it there in a kind of scary red blob, to substantially inherit most of the reasonableness of the private sector. 1:29 And so this presentation is not about 1:32 replacement. It is not about replacing the public sector with the private sector via some mysterious process that no one will explain. 1:39 It is just about improving the public sector. So you have to decide how anarchist you think it is. 1:45 It's up to you, but it does involve markets. It's very Hayekian, so we'll see. 1:51 Here is the entire talk in one slide. The problem, politicians are installing bad policies and getting away with it. 1:58 Solution, make it easy to find the bad politicians and get rid of them, and 2:04 specifically, we're going to create some assets that whose value reflects the politicians' competence, 2:10 and then we'll make the pricing data available to the public, and they'll just vote for 2:14 whoever has the better numbers. 2:17 So 2:20 people might have this information available to them just before they vote, and so for each person running, 2:25 there's a column here, and it just gives forecasts about how life will be if the person's elected. 2:31 The first row guesses how much the government will cost. The second row guesses how economically prosperous the citizens will be, and 2:38 the third row just maybe would cover things like 2:42 invasion, war, health care, mental health, 2:44 gun safety, whatever. It just kind of catches all these things, and people will just look at the two numbers and decide which one they like 2:50 more. So where do these numbers come from, and why would anyone take them seriously, and that is what this talk is about. 2:59 So first, I'm going to give some background knowledge, and then second, I'm going to talk a little bit about 3:04 democracy and what I call electoral feedback, and 3:08 then I'm going to talk about these event derivatives, which are usually called prediction markets, and 3:13 fourth, I'm going to combine these ideas, and fifth, I'll explain why I think this idea actually will work, unlike other ideas that don't work. 3:21 Those last two are super short, take about 60 seconds, I hope. 3:26 So 3:27 the background knowledge that you need to have in a nutshell is that this idea is very old, and a lot of super smart people 3:33 like it, including many cypherpunks. 3:36 The second is that I've already basically implemented the idea, so it's not vaporware, and the third, there is no like scam 3:42 ICO, or any kind of like weird scamming thing in this project anywhere, which I'm going to explain. 3:47 So the first name I'm going to drop is Hal Finney. A lot of people think that Hal Finney is Satoshi Nakamoto. 3:54 He received the first Bitcoin transaction. He liked this idea a lot. 3:59 He's calling them conditional prediction markets. I'm going to call them multi-dimensional 4:03 event derivatives later. 4:06 The second name is Robin Hanson, who Ralph Merkle is name-dropping him as an important guy. 4:13 This is basically Robin Hanson's ideas just put on a blockchain, and 4:18 Ralph Merkle built most of the technology that Bitcoin uses, so 4:23 this is more name-dropping, which is that this was a paper that was put out 4:27 in order to try to convince the government to stop interfering with prediction market development and research. 4:33 It's signed by a giant list of people. All these people either like will win the Nobel Prize, or have, or should. 4:40 They're super, super well respected, and they all signed this paper, 4:44 but it's kind of going nowhere, hence the blockchain emphasis that I'm taking it sort of more 4:50 getting further away from the high road, because it's been shown to be impractical. 4:56 So the second thing is that this talk assumes that someone will solve the so-called 5:02 blockchain oracle problem, which is a technical problem. In order to make these assets, you need it to be solved. Fortunately, 5:08 I personally have solved it, 5:09 so there's nothing for you to worry about, and I wrote this paper about it a long time ago, and this paper has some pretty 5:14 cool endorsements. We've got Roger Ver up here. 5:18 We also have Andrew Poelstra, who I think is the brightest technical mind in the space, and 5:23 we have also Adam Back, CEO of Blockstream, and we even have a smiley face from Peter Todd. 5:29 So it's like basically as good as it gets, I think. 5:33 This paper that I wrote spawned virtually all the projects in this area, 5:38 some of which I'm more proud of than others, but the only one I really like is this one at the top. I saw it's 5:45 oversaw its development. Personally, it's a fork of Bitcoin Core, and it was assembled by two different 5:50 Bitcoin Core developers over a period of about 24 months, 5:54 and I run the project website where I put content like papers and this presentation, and 6:00 here's some screenshots. 6:02 There we go. 6:04 Some screenshots, which hopefully by the end of the talk will kind of make some sense to you. 6:08 So we have some events here. They're like in a big list, and then we have some markets. They have prices, 6:14 and then we have like a trading view, where you're like buying and selling things. So that's just kind of to show it's not paperware. 6:23 This project could theoretically be live on the Bitcoin BTC mainnet as early as this autumn, 6:29 but I don't, you know, you never know about that type of thing. It depends on a lot of 6:34 variables, so we'll have to see about that. 6:36 So finally, I just have to mention there's no ICO and no like weird utility token going on here. 6:41 There is a second token, which leads to misunderstanding, so I'm going to explain it in about three sentences. 6:47 The second token is more 6:49 analogous to mining. So in Bitcoin, the people who transact, they don't need to mine, and 6:54 in this project, the users, people who trade in these markets and create them, they don't need to know anything about the second token, 7:02 nor do they need to own any of the second token. 7:04 So if you only own that token, if you want to go into that line of work, 7:07 same way you'd only buy an ASIC if you wanted to go into that line of work. 7:12 Okay, so now the talk can really get started actually, for real. 7:16 So the question is, why is the public sector so disappointing? Because it's really everyone that hates it. 7:22 It's not just people who come to an anarchist 7:25 conference 7:26 in Mexico. It's just kind of universally, culturally, 7:29 it's just disappointing for people. And the question is why, and the argument I'm going to put forward has nothing to do with 7:35 morality or with lofty abstractions such as freedom or whatever. It won't even have that much to do with economics. 7:43 Instead, it has more of like a physics or biochemistry flavor, because it rests on this idea of feedback. 7:50 And this is an idea of feedback in the free market, where you have 7:54 bad products being sold and 7:57 consumers don't like the products, and they buy fewer of them, 8:00 which punishes the merchant who's selling them. So a free market doesn't mean instant utopia, right? 8:06 But it does mean that things improve, and they improve basically as fast as possible. 8:10 And 8:11 so the modern democracies that most of us live in, for better or for worse, also have feedback called voting. And 8:19 it kind of goes a little like this, where if the government is too unsatisfying in some way, 8:25 it can be dismissed with a majority vote. And it's a little more complicated than that. There's like some other weird stuff 8:31 that's not really important. But basically, this is this gear that 8:34 controls the machine 8:36 very slowly. Now, the free market feedback differs from electoral feedback. 8:40 We know they're different, because one works pretty well, and the other one is always disappointing. 8:46 So what are the differences? Frequency is one difference. 8:49 The market feedback fires every time a merchant loses a sale. 8:54 So that's many millions of times per day. 8:56 The electoral feedback fires once every four or two or six years or something like that or whatever, 9:02 whatever the election is. So that's one big difference. 9:05 But I think that difference is actually relatively trivial in comparison to something that I call research. 9:11 And so you see there's actually even more feedback loops than you originally anticipated, because 9:18 when a disappointing sale is completed, the consumer suffers. And the desire to avoid suffering 9:26 stimulates the consumer's search for alternatives. And I call that search effort research, for lack of a better word. 9:32 I don't know what else to call it. 9:34 But so, okay. 9:36 Now, in both sectors, public and private, you have to do your own research, 9:41 so to speak, and you have to pay for your research. Research consumes your scarce resources, especially your time. 9:49 Important things in your life, you research a lot. Buying a house, you research it a lot. 9:54 But picking out a hotel room, on the other hand, you do not do as much research. You only do a little bit. 10:00 Cosmetic surgery, you would do lots of research. Buying a new pair of flip-flops, very little research. 10:06 So the problem with elections is that your single vote will almost never affect the outcome, because there's so many voters. 10:13 And people pretend that their vote matters in order to feel important, but really, they know it doesn't matter. 10:20 So if you master the issues and the candidates and do a lot of research, 10:25 it won't change your life in the slightest, because other people are not doing any research. 10:29 So there's never any reason for you to do any. 10:32 Now, of course, this is a big oversimplification, 10:34 but I think it's basically the explanation for what's going on here. 10:38 Voters usually admit that they don't know what they're doing, and I have here 71% of Americans cannot even name their congressional representative, 10:45 let alone know what they've been up to or whatever. 10:48 And why bother learning who the representative is? Because it basically makes no difference. 10:53 So at breakfast on Election Day, 10:56 voters will carefully weigh the costs and benefits of different breakfast foods that they could eat. 11:01 But when they go in to vote, they won't really know anything about the costs and benefits of their choices, 11:06 so hence this idea to kind of give them that information. 11:10 Now, an implication of this theory is that the idea of educating people in order to make them libertarians, 11:17 or make them experts in economics, 11:19 will never be able to actually solve this problem and improve society. 11:23 It's very noble and worth doing, in the same way that chemistry and music are worth doing, 11:28 and like bird-watching is like a real skill, and mastering the skill of bird-watching could be fun, 11:33 but ultimately, if persuasion were effective, then the elections themselves wouldn't be so problematic. 11:39 Friedrich Hayek would have been wrong about information diffusion, 11:43 and a centrally planned economy would be more efficient than laissez-faire. 11:47 So the research incentives are the whole problem in the first place. 11:50 So now, what should we do instead? 11:52 As I said, the plan is to have people show up on Election Day, open their cell phones, and check some prices, 11:58 and it should be as easy as checking an NFL score, or the S&P 500, or something like that. 12:02 Then they just vote for the candidate with better prices, and this is just supposed to be a huge improvement of what we do right now. 12:08 So prices of what, though? What's going on here? 12:11 This is an example of an event derivative, which is usually called a prediction market. 12:16 It's defined as an asset that pays you money if something happens. 12:20 If the thing happens, you get paid. If it doesn't happen, you do not get paid. 12:24 This is an example from a website called Intrade. 12:27 The asset was created in 2011, and it would have been worth $1 if the year 2012 had been the warmest year on record, 12:33 as reported by these NASA satellites that measure surface temperature and put it on a website where anyone can find it. 12:40 Otherwise, the asset is worth zero. 12:42 So the asset traded at $0.40 in January 2011, but by mid-2012, it was worth just $0.10, 12:49 and in December of 2012, it was worth basically nothing. 12:53 So event derivatives are similar to bets or wagers. 12:57 The difference is that with event derivatives, the prices are constantly changing, 13:02 and this variation in prices produces socially useful information. 13:05 So in a bet, the odds are fixed. 13:08 And the odds are usually just fixed at 50-50. 13:10 I bet you $20 if this happens. 13:13 And where's the fun in that? 13:14 You know, with these assets, the market price is the objective likelihood of the event that the market is about. 13:21 So as 2012 went on, it was becoming clear that it wasn't going to be the warmest year, at least according to these satellites, 13:28 and so the price collapsed over time. 13:31 The topic of prediction markets usually reminds people of election betting, which is a good start. 13:38 These are assets that pay out if the candidate wins. 13:41 This right here is an example if a candidate won the 2016 Republican presidential nomination. 13:47 So after the Iowa straw poll, Trump's price in blue plummeted because he didn't do as well as people thought, 13:54 and then it surged after he won South Carolina. 13:57 Now, is this what I suggest that we do? 13:59 A lot of people think that this is what I want us to do, but it is absolutely not at all what I want us to do. 14:04 This is lame. 14:06 It is certainly entertaining, and it makes election years much more fun, especially for me, because I like this type of thing. 14:12 But these type of markets only tell us who is probably going to win. 14:17 Instead, we want to change who wins, and we want to sabotage the incompetent candidates so that they cannot win. 14:24 And how do we do that? 14:26 This is what Hal Finney was excited about. 14:29 Although, yeah, they could never really do it the right way, but they were excited about it back in, whenever that was, 2006 or something. 14:37 Okay, so, oh yeah, sorry. 14:40 I'm going to try to explain this idea of multivariate betting in nine slides. 14:45 And hopefully after this, the whole presentation will make sense. 14:49 And the first two are super easy, and four of them are really just one slide broken into four pieces. 14:54 So, here we go. 14:56 Okay, here on the left, I have a series of coin flip events. 14:59 In this table, we're describing the behavior of the 303rd coin flip. 15:04 We don't know what will happen on that flip, but we can nonetheless fill in the probabilities in this table. 15:08 Heads and tails are equally likely, so it's 50-50. 15:12 This is just a basic statement of probability. 15:14 And this next one is also easy. 15:16 It's just a dice. 15:17 It's the same as the previous slide, but with a dice. 15:19 Since each face of the dice is equally likely to come up, they all have one sixth. 15:25 Now, here's where you need to turn on your brain, unfortunately. 15:28 Sorry about that. 15:29 But this is where it starts to get complicated, which is that this slide is just the previous two slides kind of added together. 15:35 And we have the coin flip in green along the left vertical edge of the table. 15:40 And we have the dice roll across the top horizontal edge. 15:43 And I've circled some probabilities in red. 15:46 The probability that a two will be rolled is still one sixth. 15:50 It's at the bottom. 15:51 The probability that a tail will be flipped is still one half. 15:54 Those are called marginal probabilities because they're written in the margin, so to speak. 15:59 The probability that both a two will be rolled and that a tail will be flipped simultaneously, that's shown here as one twelfth. 16:06 And okay, so with that out of the way, we can talk about relationships between events. 16:13 And I'm going to draw a contrast between these two tables here. 16:16 Both are about unknown future coin flips. 16:19 The top table considers coin flip 304 in blue across the top. 16:27 And it considers flip number 305 in red across the left side. 16:31 Each cell occurs with probability .25, which is what I have indicated here. 16:35 Now, the second table does something extremely unrealistic that is only for teaching purposes. 16:40 It plots coin flip 304 against itself. 16:44 Now, in the real world, this would never happen. 16:46 No one would do this because it's just the same thing twice. 16:49 But the point here is that if it did happen, we could write certain numbers in this table. 16:54 And the numbers we would write would have zeros for things that are impossible. 16:58 So we zero out everything that's impossible. 17:00 And we see there's a kind of clumping here. 17:03 And I'm going to explain that again with this dice, which is also the same idea here, 17:08 that if it's the same dice roll against itself, you have this kind of clumping up in a line. 17:14 If they're perfectly related, they clump up in a line. 17:16 If they're just a little related, then they only clump up a little bit. 17:19 And if they are not related at all, they don't clump. 17:23 So that's really the synthesis here is that instead of doing things that we know are related, 17:29 we check and see if other things are related. 17:33 Here I have the—we just make four—okay. 17:38 Since clumping equals relatedness, all we do is measure these two things at once, 17:42 and we look for the clumping. 17:44 And if there's clumping, that means the events are related. 17:46 So we make four different event-derivative markets, one for each of the grids here, 17:51 and we get a market price for each, and then we just look for this clumping. 17:54 And that's basically the synthesis here, which is the short part, 17:57 which is that with some arithmetic you can just add two numbers and divide them, 18:01 and you can get out of this something that's very, very easy for the layperson to understand. 18:06 They'll never have to see anything complex. 18:08 They just see a bunch of numbers that go up when the candidate is better 18:11 and go down when the candidate is much worse. 18:14 And that is how you get these numbers to show up that I mentioned at the beginning of the talk. 18:19 And so now I have two minutes left, I think, and I'm going to talk about this last part, 18:25 which is why anyone will care about this. 18:28 So why would laypeople check their phones on election day? 18:31 Well, if they're already in line, then actually this is the first Google result for election line, 18:35 and there's already people who are just on there. 18:37 These people will be glued to their phones the whole time. 18:39 So that's a small ask. 18:41 People are basically on their phone all the time. 18:43 But why would they navigate their phone to these particular prices and not just ignore them, 18:47 the same way they ignore every other piece of information that falls under this category of research that I mentioned before? 18:54 Well, basically, let's see here. 18:59 In a nutshell, the voter turnout rate is super low, 19:02 and it's so low that the people who don't vote could basically form their own political party 19:07 that would defeat both of the other parties combined. 19:10 So you really aim for these people who are just totally overwhelmed and apathetic and don't know what to do. 19:15 And I think that really describes most people, 19:17 especially when you consider that fact about 71% of Americans not even knowing who their congressional representative is, 19:25 even though everyone hates the government. 19:27 So I think that's the only way to explain all those things. 19:30 The victory margins are very narrow. 19:32 So even if you don't get every single nonvoter to form a party, you could still do very well. 19:38 And finally, you can ramp this up by betting on other stuff that people like 19:42 until they get more comfortable with this institution, and then there you go. 19:46 And that's basically—oh, yes, one final thing, and I think I have like 30 seconds left, so it's perfect, perfect timing, 19:53 which is that this idea makes rhetoric irrelevant. 19:57 So, for example, let me skip to bullet point number three. 20:01 If a politician announces that they want everyone to die and that they plan to kill everyone, that's fine, 20:06 but if the traitors don't actually believe that they will follow through or they will or can follow through with this, 20:10 then the number won't actually budge an inch because you're speculating on stuff that will happen. 20:15 The derivatives only pay if it actually happens. 20:17 So everything that politicians say can just be safely ignored. 20:21 And laypeople already know that politicians are liars, so I think they will actually warm up to this idea pretty quickly. 20:27 Okay, that's the talk. 20:29 Thank you.