One of the Ethereum applications that I’m most excited about is prediction markets.
Author: Vitalik, Founder of Ethereum
Compiled by: 0xjs@Golden Finance
One of the Ethereum applications that excites me the most is prediction markets. In 2014, I wrote an article about futarchy, a prediction-based governance model conceived by Robin Hanson. I was an active user and supporter of Augur back in 2015 (see, my name is in the Wikipedia article). I made $58,000 betting on the 2020 election. This year, I have been a close supporter and follower of Polymarket.
For many people, prediction markets are about betting on elections, and betting on elections is about gambling — if it allows people to have fun, then great, but it’s fundamentally no more fun than buying random tokens on pump.fun. Viewed from this perspective, my interest in prediction markets can seem confusing. So in this post, I aim to explain why the concept excites me. In short, I believe that (i) even existing prediction markets are a very useful tool for the world, but also that (ii) prediction markets are just one example of a much larger, very powerful category that has the potential to create better implementations of social media, science, journalism, governance, and other fields. I’m going to call this category “info finance.”
The two sides of Polymarket: a betting site for participants, a news site for everyone else
Over the past week, Polymarket has been a very effective source of information about the US election. Not only did Polymarket predict a 60/40 chance of Trump winning (while other sources predicted 50/50, which is not too impressive in itself), but it also demonstrated other advantages: when the results came in, while many pundits and news sources had been baiting viewers into hearing favorable news for Harris, Polymarket directly revealed the truth: Trump had a better than 95% chance of winning and a better than 90% chance of taking control of all branches of government.
Both screenshots were taken at 3:40 a.m. EST on November 6.
But to me, that’s not even the best example of why Polymarkets is interesting. So let’s look at another example: the elections in Venezuela in July. The day after the elections, I remember seeing out of the corner of my eye some people protesting the highly rigged election results in Venezuela. At first, I didn’t think much of it. I knew Maduro was already one of those “basically a dictator” figures, so I thought, of course he’s going to fake every election result to keep himself in power, of course there will be protests, and of course the protests will fail — as many others have, unfortunately. But then I was scrolling on Polymarket and saw this:
People were willing to put over a hundred thousand dollars on a 23% chance that Maduro would be overthrown in this election. Now I was paying attention.
Of course, we know the unfortunate consequences of this situation. Ultimately, Maduro did stay in power. However, the market made me realize that this time, the attempt to overthrow Maduro is serious. The protests were massive and the opposition came up with a surprisingly well-executed strategy that proved to the world how fraudulent the election was. If I hadn't gotten the initial signal from Polymarket that "this time, there's something worth paying attention to," I wouldn't have even started paying attention.
You should never trust the Polymarket betting charts completely: if everyone believed the betting charts, then anyone with money could manipulate the betting charts and no one would dare to bet against them. On the other hand, trusting the news completely is also a bad idea. The news has a motivation to sensationalize, to exaggerate the consequences of anything for the sake of clicks. Sometimes, this is justified, sometimes not. If you see a sensational article, but then you go to the market and find that the probability of the relevant event has not changed at all, then it is also reasonable to be skeptical. Or, if you see unexpectedly high or low probabilities in the market, or unexpected sudden changes, that is a signal for you to read through the news and see what caused it. Conclusion: You can get more information by reading the news and the betting charts than by reading either one alone.
Let’s review what’s going on here. If you’re a bettor, then you place bets with Polymarket, and to you, it’s a betting site. If you’re not a bettor, then you read the betting charts, and to you, it’s a news site. You should never trust the betting charts completely, but I’ve personally made reading the betting charts a step in my information gathering workflow (alongside traditional and social media) and it helps me get more information more efficiently.
The broader meaning of information finance
Now, we get to the important part: predicting election results is just the first application. The broader concept is that you can use finance as a way to align incentives in order to provide valuable information to an audience. Now, a natural reaction is: isn’t all of finance fundamentally about information? Different actors will make different buy and sell decisions because they have different views of what the future will hold (besides personal needs, like risk appetite and desire to hedge), and you can infer a lot about the world by reading market prices.
To me, information finance is just that, but structurally correct. Similar to the concept of structural correctness in software engineering, information finance is a discipline that requires you to (i) start with facts you want to know and then (ii) deliberately design a market to optimally extract that information from market participants.
Information finance is a three-sided market: bettors make predictions, and readers read them. The market outputs predictions about the future as a public good (because that is what it was designed to do).
Prediction markets are one example: you want to know if a specific fact will happen in the future, so you set up a market for people to bet on that fact. Another example is a decision market: you want to know if decision A or decision B will produce a better outcome based on some metric M. To do this, you set up a conditional market: you ask people to bet on (i) which decision will be chosen, (ii) if decision A is chosen, the value of M is obtained, otherwise zero, and (iii) if decision B is chosen, the value of M is obtained, otherwise zero. With these three variables, you can determine whether the market thinks decision A or decision B is more favorable to obtaining the value of M.
One technology I expect will drive the growth of information finance over the next decade is AI (both in large models and in future technologies). This is because many of the most interesting applications of information finance are related to “micro” problems: millions of small markets where decisions taken individually have relatively small impacts. In fact, markets with low volumes often do not work efficiently: it does not make sense for experienced participants to spend time on detailed analysis just to make a few hundred dollars in profit, and many even argue that such markets would not work at all without subsidies, because there would not be enough naive traders on all but the largest and most sensational problems for experienced traders to profit from. AI completely changes this equation, meaning that it is possible to get reasonably high-quality information even on markets with $10 in volume. Even if subsidies are needed, the amount of subsidy per problem becomes very affordable.
Information finance needs to be distilled by humans
Judgment
Suppose you have a trustworthy human judgment mechanism, and that mechanism has the legitimacy of the entire community trusting it, but it takes a long time and is costly to make judgments. However, you want to have access to at least an approximate copy of that "expensive mechanism" in real time at low cost. Here's an idea from Robin Hanson about what you could do: Every time you need to make a decision, you set up a prediction market predicting what the expensive mechanism would do about the decision if it were called. You let the prediction market run, and invest a small amount of money to subsidize the market maker.
99.99% of the time, you don’t actually call the expensive mechanism: maybe you “reverse the trade” and give everyone back their input, or you just give everyone zero, or you look at whether the average price is closer to 0 or 1 and treat that as ground truth. 0.01% of the time — maybe randomly, maybe for the most heavily traded markets, maybe a combination of both — you actually run the expensive mechanism, and compensate participants accordingly.
This gives you a trusted, neutral, fast, and cheap "distilled version" of your original highly trusted but extremely expensive mechanism (using the word "distilled" in analogy to the LLM). Over time, this distilled mechanism roughly mirrors the behavior of the original mechanism - because only the actors who helped achieve that outcome make money, while everyone else loses money.
Model of a possible prediction market + community notes combination.
This applies not only to social media, but also to DAOs. A major problem with DAOs is that there are too many decisions to make for most people to be willing to participate, which results in either extensive use of delegation, with the risk of centralization and principal-agent failure common in representative democracies, or vulnerability to attack. If actual voting rarely occurs in a DAO, and most things are determined by prediction markets, where a combination of humans and AI predict voting outcomes, then such a DAO may work well.
As we have seen in the example of decision-making markets, information finance contains many potential paths to solving important problems in decentralized governance. The key lies in the balance between market and non-market: the market is the "engine" and some other non-financialized trust mechanism is the "steering wheel."
Other Use Cases of Information Finance
Personal tokens - projects like Bitclout (now deso), friend.tech, and many others that create tokens for everyone and make them easy to speculate on - are a category I call "raw information finance". They intentionally create market prices for specific variables (i.e. expectations of a person's future reputation), but the exact information revealed by the price is too vague and subject to reflexivity and bubble dynamics. It may be possible to create improved versions of such protocols that address important issues like talent discovery by more carefully considering the economic design of the token (especially where its ultimate value comes from). Robin Hanson's idea of reputation futures is one possible end state here.
Advertising - The ultimate "expensive but trustworthy signal" is whether you will buy a product. Information based on this signal can be used to help people decide what to buy.
Scientific peer review - There has been a "replication crisis" in science, whereby some famous results that have become part of folk wisdom in some contexts end up not being reproduced in new research. We could try to identify results that need to be re-examined through prediction markets. Such markets would also give readers a quick estimate of how much they should trust any particular result before it is re-examined. Experiments with this idea have been done and appear to be successful so far.
Public Goods Funding — One of the main problems with the public goods funding mechanism used by Ethereum is its “popularity contest” nature. Each contributor needs to run their own marketing campaign on social media to gain recognition, and contributors who are not able to do this or who naturally have more “background” roles have a hard time getting significant funding. An attractive solution is to try to track the entire dependency graph: for each positive outcome, which projects contributed how much to it, then for each project, which projects contributed how much to it, and so on. The main challenge of such a design is to figure out the weights of the edges so that they are resistant to manipulation. After all, such manipulation happens all the time. A distilled human judgment mechanism might help.
in conclusion
These ideas have been theorized for a long time: the earliest writings on prediction markets and even decision markets are decades old, and similar discussions in financial theory are even older. However, I believe that the current decade presents a unique opportunity for the following main reasons:
Infofinance solves a real trust problem. A common concern of this era is the lack of knowledge (or worse, lack of consensus) about who to trust in politics, science, and business. Infofinance applications can help be part of the solution.
We now have scalable blockchains as a foundation. Until recently, fees were too high to really make these ideas a reality. Now, they are no longer too high.
AI as a participant. When information finance had to rely on human participation in every problem, it was relatively difficult to function. AI greatly improves this situation, enabling efficient markets even on small-scale problems. Many markets may have a combination of AI and human participants, especially when the volume of a particular problem suddenly changes from small to large.
To make the most of this opportunity, we should go beyond just predicting elections and explore what else information finance can do for us.
Special thanks to Robin Hanson and Alex Tabarrok for their feedback and comments
