Information Itself Can Be a Complete Market, This Article Is Based on Vitalik Buterin’s Article “From Prediction Markets to Info Finance,” Compiled, Translated, and Written by Odaily.
(Background: Polymarket’s Mysterious Whale Wins $50 Million, How Did He Accurately Predict the Election Outcome?)
(Additional Background: Gao Chongjian’s Special Article: I Predicted Kamala Harris’s Victory on Polymarket and Lost Two Dollars)
The U.S. election propelled the prediction market Polymarket into further popularity, attracting those seeking profit to place bets, while those seeking results used it as a news information platform. As a “breakthrough” blockchain application, Polymarket successfully combined on-chain funds with real-world predictions. Vitalik Buterin has praised Polymarket multiple times and is also a loyal fan of the early prediction market Augur.
Today, Vitalik published an article exploring “information finance” through prediction markets. Below is the full content translated by Odaily Planet Daily:
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 proposed by Robin Hanson. As early as 2015, I was an active user and supporter of the prediction market Augur, and I made $58,000 betting on the 2020 election. This year, I have been a loyal supporter and follower of Polymarket.
For many, prediction markets are just about betting on elections, and betting on elections is simply gambling — which is great if it helps the public have fun, but fundamentally, it’s no more interesting than randomly buying a meme on pump.fun. From this perspective, my excitement about prediction markets seems puzzling. So, in this article, I will explain the concept of prediction markets that interests me. In short, I believe:
1. The existing prediction markets are a very useful tool for the world;
2. Furthermore, prediction markets are just a precursor to a more popular field with potential applications in social media, science, news, governance, and other areas, which I will categorize as “information finance.”
Over the past week, Polymarket has been a very effective source of information regarding the U.S. election. Polymarket not only predicted Trump’s winning probability as 60/40 (while other sources predicted 50/50, which in itself is not too impressive) but also demonstrated other advantages: when the results came out, despite many experts and news sources attempting to sway audiences in favor of Harris, Polymarket directly revealed the truth: Trump’s winning probability was over 95%, and the probability of capturing control of all government departments was over 90%.
However, for me, this is not even the best example of Polymarket’s intrigue. So let’s look at another example: during the Venezuelan presidential election in July, the day after the election ended, I inadvertently saw someone protesting the highly manipulated Venezuelan presidential election. Initially, I didn’t pay much attention. I knew Maduro was already one of those “basically dictators,” so I thought, of course, he would rig every election result to maintain his power, there would be protests, and the protests would fail. Unfortunately, many others also failed. Later, while browsing Polymarket, I saw this:
People were willing to invest over $100,000, betting on the possibility of Maduro being overthrown in this Venezuelan election at 23%, now I noticed this matter.
Of course, we still know that the outcome of being overthrown is unlikely. Ultimately, Maduro remained in power. But the market made me realize that this time the attempt to overthrow Maduro was serious. At the time, there were large-scale protests, and the opposition surprisingly adopted a well-executed strategy, proving to the world the fraudulent nature of this election. If I hadn’t received Polymarket’s initial signal “this time, something needs attention,” I wouldn’t have started paying so much attention.
You shouldn’t completely trust the charts: if everyone believed the charts, then anyone with money could manipulate the charts, and no one would dare to bet. On the other hand, completely trusting the news is also a good way. News has sensational motives, exaggerating the consequences of anything for click rates. Sometimes a matter is reasonable, sometimes not. If you see a sensational article and then verify it in the market, finding that the probability of the related event hasn’t changed at all, then it’s reasonable to be skeptical. Additionally, if you see an event in the market with an unexpectedly high or low probability or a sudden change, it’s a signal to read the news and see what led to this conclusion.
Conclusion: By reading the news and charts, you can gain more information than by browsing either alone.
If you are a gambler, you can deposit with Polymarket; for you, it’s a gambling site. If you’re not a gambler, you can read chart data; for you, it’s a news site. You should never completely trust the charts, but I personally have made reading chart data a step in my information gathering workflow (alongside traditional media and social media), helping me acquire more information more effectively.
Predicting election outcomes is just one use case. The broader concept is that you can use finance as a way to coordinate incentive mechanisms to provide valuable information to audiences. Now, a natural response might be: Isn’t all finance fundamentally about information? Different participants make different buying and selling decisions because they have different views on what will happen in the future (besides personal needs like risk preferences and hedging desires), and you can infer a lot of knowledge about the world from reading market prices.
To me, information finance is like this, but structurally correct, similar to structurally correct concepts in software engineering. Information finance is a discipline that requires you to
1. Start from the facts you want to know;
2. Then deliberately design a market to optimally extract that information from market participants.
One example is prediction markets: you want to know something that will happen in the future, so you create a market for people to bet on it. Another example is decision markets: you want to know whether decision A or decision B will produce better results according to some metric M, so to achieve this, you set up a conditional market: You let people bet on which decision will be chosen: if decision A is chosen, then the value of M, otherwise zero; if decision B is chosen, then the value of M, otherwise zero. With these three variables, you can calculate which decision the market thinks is more likely to…The Value of Good M
I anticipate that in the next decade, artificial intelligence (whether LLMs or some future technology) will have a significant impact on the financial industry. This is because many applications of information finance deal with “micro” issues: mini-markets of millions of decisions where the impact of a single decision is relatively low. In practice, markets with low trading volumes often do not operate efficiently: for an experienced participant, it is not worthwhile to spend time conducting detailed analysis for a profit of only a few hundred dollars. Many even believe that without subsidies, such markets would not operate at all, as there are not enough novice traders for experienced traders to profit from, except for the most significant and sensational issues. Artificial intelligence completely changes this equation, meaning that we might obtain quite high-quality information even in markets with a trading volume of $10. Even if subsidies are required, the scale of subsidies for each issue is affordable.
Suppose you have a human judgment mechanism you trust, and the entire community trusts its legitimacy, but it requires a long time and high cost to make judgments. However, you wish to access a cheaper copy of this “expensive mechanism” in a low-cost and immediate way. Here’s an idea from Robin Hanson about what you can do: set up a prediction market every time you need to make a decision, predicting the outcome the expensive mechanism would produce if called. Then the prediction market starts running, and a small amount of money is invested to subsidize the market maker.
In 99.99% of cases, you don’t actually call the expensive mechanism: maybe you “reverse trade,” refunding or not refunding the money everyone invested, or you look at whether the average price is closer to “yes” or “no,” and take that as the basic fact. In 0.01% of cases, perhaps randomly, perhaps in the market with the highest trading volume, or perhaps both, you actually run an expensive mechanism and compensate participants based on it.
This provides a trusted, neutral, fast, and inexpensive “refined version,” which is a refined version of the originally highly trusted but extremely costly mechanism (using “refined” as an analogy to LLM model distillation). Over time, this refined mechanism roughly reflects the behavior of the original mechanism, as only participants who help achieve that result can make money, while others would lose money.
This applies not only to social media but also to DAOs. A major issue with DAOs is that there are too many decisions, and most people are unwilling to participate, leading to either widespread delegation with risks of centralization and agency failure common in representative democracy, or they become vulnerable to attacks. If actual voting in DAOs occurs rarely, and most things are decided by prediction markets where humans and AI combine to predict voting outcomes, then such a DAO might function well.
As we see in the example of decision markets, information finance holds many potential paths to solving important issues in decentralized governance, with the key being the balance between market and non-market: the market is the “engine,” while some other non-financialized trust mechanism is the “steering wheel.”
Personal Tokens — projects like Bitclout (now deso), friend.tech, and others that create tokens for everyone and make them easy to speculate on — are what I would call “primitive information finance.” They deliberately create a market price for a specific variable (i.e., the expected future status of a person), but the exact information revealed by the price is too vague, prone to reflexivity and bubble dynamics (as noted by Odaily Planet Daily: price surges attract buys). It is possible to build improved versions of such agreements and 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 perspective in “Future of Reputation” is a possible end state.
Advertising — the ultimate “expensive but trustworthy signal” is whether you will buy a product. Information finance based on this signal can be used to help people determine what to buy.
Scientific Peer Review — There has always been a “replication crisis” in the scientific community, where some famous results have become part of folk wisdom in certain situations but cannot be reproduced in new studies. We can attempt to determine which results need re-examination through prediction markets. Before re-examination, such markets would also allow readers to quickly estimate to what extent they should trust any particular result. Experiments with this idea have been conducted and seem successful so far.
Public Goods Funding — One main issue with public goods funding mechanisms used in Ethereum is their “popularity contest” nature. To gain recognition, every contributor needs to run their own marketing operations on social media, and those who cannot do so sufficiently or those inherently with more “background” roles find it difficult to receive significant funding. An attractive solution is to try to track the entire dependency graph: for each positive result, how much each project contributed to it, then for each project, how much each project contributed to it, and so on. The main challenge of this design is determining the weights of the edges so it can resist manipulation. After all, such manipulation is constantly happening. Refined human judgment mechanisms may help.
These ideas have been theorized for a long time: the earliest writings on prediction markets and even decision markets are from decades ago, and financial theory saying similar things is even older. However, I believe the present decade offers a great opportunity for information finance for several key reasons:
Information finance solves real trust issues people have. A common problem of this era is the lack of awareness of who is trustworthy in political, scientific, and business contexts (worse, the lack of consensus), and information finance applications can be part of the solution;
We now have scalable blockchain suites as a foundation, which until recently made these ideas infeasible due to high costs. But now, costs are no longer high.
AI as a participant, when information finance had to rely on human participation to resolve every issue, made it relatively difficult to function. AI greatly improves this situation, enabling effective markets even on small-scale issues. Many markets may have a combination of AI and human participation, especially when the volume of specific issues suddenly grows from small to large.
To fully seize this opportunity, it’s time to explore what financial information can bring us through electoral predictions.
