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