Original title: From prediction markets to info finance
Original author: Vitalik Buterin
Original compilation: 0xjs, Golden Finance
One of the Ethereum applications that excites me the most is prediction markets. In 2014, I wrote an article about articles by futarchya prediction-based governance model conceived by Robin Hanson. I’ve been an active user and supporter of Augur since 2015 (see, my name is in the Wikipedia article). I made $58,000 betting on the 2020 election. I have been a close supporter and follower of Polymarket this year.
For many people, prediction markets are about betting on elections, and betting on elections is about gambling – if it allows people to have fun, that’s great, but fundamentally it’s no better than betting on pump.fun It’s more fun to buy random tokens on. From this perspective, my interest in prediction markets seems confusing. So, in this article, I aim to explain why this concept excites me. In short, I believe that (i) even existing prediction markets are a very useful tool for the world, but additionally (ii) prediction markets are just one example of a larger, very powerful category that has the potential to create Better implementation in social media, science, journalism, governance and other fields. I will 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 regarding the US election. Not only did Polymarket predict a 60/40 chance of Trump winning (while other sources predicted 50/50, which in and of itself is not too impressive), it also showed other merit: When the results came in, despite many experts and news sources have been baiting viewers in the hope they’ll hear favorable news for Harris, but Polymarket sets the record straight: Trump has a greater than 95% chance of winning and a greater than 90% chance of seizing control of all government departments at the same time .
Both screenshots were taken at 3:40 a.m. ET on November 6
But to me, this isn’t even the best example of how interesting Polymarket is. So let’s look at another example: Venezuela’s elections in July. The day after the election, I remember seeing out of the corner of my eye people protesting the highly rigged election results in Venezuela. At first, I didn’t pay much attention. I know Maduro is already one of those “basically dictator” figures, so I thought, of course he will falsify every election result to keep himself in power, of course there will be protests, of course the protests will fail – unfortunately The thing is, many others have failed. But then I was scrolling through Polymarket and saw this:
People are willing to invest more than $100,000 to bet that there is a 23% chance that Maduro will be overthrown in this election. Now I’m starting to pay 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 huge 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 completely trust Polymarket betting charts: if everyone believed in 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 entirely is also a bad idea. News has a motive of sensationalism, exaggerating the consequences of anything for the sake of clicks. Sometimes this is justified, sometimes not. If you read a sensational article, but then you go to the market and find that the probabilities of the event in question have not changed at all, it’s also reasonable to be skeptical. Or, if you see an unexpected high or low probability in the market, or an unexpected sudden change, that’s a signal for you to read through the news to see what’s causing it. Conclusion: You can get more information by reading the news and betting on charts than by reading either alone.
Let’s review what’s going on here. If you are a bettor, then you can bet with Polymarket, which is a betting website for you. If you are not a bettor then you can read betting charts and for you this is a news website. You should never completely trust a bet chart, but I personally have made reading bet charts a step in my information gathering workflow (along with traditional and social media) and it helps me get more information more efficiently.
The broader significance of information finance
Now, we get to the important part: predicting election results is only 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 your audience. Now, a natural reaction is: isn’t all of finance fundamentally about information? Different participants will make different buying and selling decisions because they have different views of what will happen in the future (in addition to personal needs, such as 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 structurally correctness in software engineering, information finance is a discipline that requires you to (i) start with the facts you want to know and then (ii) deliberately design a market to best obtain information from market participants Get this information.
Information finance is a three-sided market: bettors make predictions and readers read the predictions. The market outputs predictions of the future as a public good (because that is what it was designed to do).
Prediction markets are one example: you want to know a specific fact that will happen in the future, so you set up a market for people to bet on that fact. Another example is the decision market: you want to know whether decision A or decision B will produce better results according to some metric M. To achieve this, you set up a conditional market: you ask people to bet on (i) which decision will be chosen, (ii) the value of M if decision A is chosen, and zero otherwise, (iii) if decision B is chosen, Then the value of M is obtained, otherwise it is zero. With these three variables, you can determine whether the market considers decision A or decision B to be more beneficial to the value of M.
I predict that one technology that will drive the development of information finance in the next decade is AI (whether it is a large model or a future technology). That’s because many of the most interesting applications of information finance are related to “micro” problems: millions of small markets where decisions individually have relatively small impacts. In fact, markets with low trading volumes often do not work efficiently: for experienced players, it does not make sense to spend time on detailed analysis just to get a few hundred dollars in profit, and many even believe that without subsidies, such The market simply doesn’t work because there aren’t enough naive traders for experienced traders to profit from all but the biggest and most sensational issues. AI completely changes this equation, meaning that even in a market with a trading volume of $10, it is possible to obtain reasonably high-quality information. Even if a subsidy is required, the amount of subsidy per issue becomes very affordable.
Information finance requires human distillation (distilled)
judgment
Suppose you have a human judgment mechanism that is trustworthy and has the legitimacy that the entire community trusts it, but making judgments takes a long time and is costly. However, you want to have low-cost, real-time access to at least an approximate copy of this “expensive mechanism”. Here’s Robin Hanson’s idea of what you could do: Every time you need to make a decision, you build a prediction market predicting what the outcome of the decision will be if that expensive mechanism is invoked. You let the prediction market run and invest a small amount of money to subsidize the market makers.
99.99% of the time, you don’t actually invoke the expensive mechanism: maybe you “undo the transaction” and give everyone back their input, or you just give everyone zero, or you see if the average price is closer to 0 or 1 and treat it as a basic fact. 0.01% of the time – it could be random, it could be for the market with the highest volume, it could be a combination of the two – you would actually be running an expensive mechanism and compensating participants accordingly.
This provides you with a credible, neutral, fast, and cheap “distilled version” of your original highly credible but extremely costly mechanism (using the word “distilled” as an analogy to “distilled” in LLM) . Over time, this distillation mechanism roughly mirrors the behavior of the original mechanism – because only the participants who helped achieve that outcome make money, while everyone else loses money.
A model of a possible prediction market + community note combination.
This applies not only to social media but also to DAOs. A major problem with DAOs is that there are so many decisions that most people are unwilling to participate in them, which results in either extensive use of delegation, risk of centralization and delegate-agent failure common in representative democracies, or vulnerability to attacks. A DAO might work well if actual voting rarely happens and most things are decided by prediction markets, where humans and AI combine to predict voting results.
As we saw in the example of the decision-making market, information finance contains many potential paths to solve 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-financial The trust mechanism is the “steering wheel”.
Other use cases for 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 of what I call “raw information finance.” They deliberately create market prices for specific variables (i.e., expectations about one’s future reputation), but the exact information the prices reveal is too vague and subject to reflexive and bubble dynamics. It is possible to create improved versions of such protocols and address important issues such as talent discovery by more carefully considering the economic design of the token, especially where its ultimate value comes from. Robin Hanson’s idea of prestige futures is one possible end state here.
Advertising – The ultimate “expensive but trustworthy signal” is whether you will buy the product. Information finance based on this signal can be used to help people determine what to buy.
Scientific Peer Review – There is an ongoing “replication crisis” in science, where famous results that in some cases become part of folk wisdom are ultimately unable to be reproduced in new research. We can try prediction markets to identify outcomes that need to be rechecked. Such a market would also allow readers to quickly estimate how much they should trust any particular outcome before re-examining it. Experiments with this idea have been completed and so far appear to have been successful.
Public Goods Funding – One of the main issues 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 those who don’t have the ability to do this or who naturally have a more “background” role have a hard time getting significant funding. An attractive solution is to try to track the entire dependency graph: for each positive result, which projects contributed how much to it, then for each project, which projects contributed how much to it, and so on. The main challenge in this design is to find the weight of the edges to make them resistant to manipulation. After all, this manipulation happens all the time. Distilled human judgment mechanisms may 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, while similar accounts in financial theory are even older. However, I believe that the current decade offers a unique opportunity for the following main reasons:
Information finance solves the trust problem that people actually have. A common concern in this era is a lack of knowledge (and worse, a lack of consensus) about who to trust in political, scientific, and business environments. Information finance applications can help become part of the solution.
We now have a scalable blockchain as a foundation. Until recently, the cost was too high to actually implement these ideas. Now, they’re not too high anymore.
AI as a participant. Information finance is relatively difficult to function when it must rely on human involvement in every problem. AI greatly improves this situation, enabling efficient markets even on small-scale problems. Many markets are likely to have a mix 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 offer us.
Special thanks to Robin Hanson and Alex Tabarrok for their feedback and comments
