One of the Ethereum applications that excites me the most is prediction markets. In 2014, I wrote an article about futarchy, a governance model based on predictions conceived by Robin Hanson. As early as 2015, I was an active user and supporter of Augur (look, my name is in the Wikipedia article). I made $58,000 betting in the 2020 election. This year, I have been a close supporter and follower of Polymarket.
For many, prediction markets are about betting on elections, and betting on elections is gambling - if it can be fun for people, that’s great, but fundamentally, it is no more interesting than buying random tokens on pump.fun. From this perspective, my interest in prediction markets may seem puzzling. Therefore, in this article, I aim to explain why this concept excites me. In short, I believe (i) even existing prediction markets are a very useful tool for the world, but moreover (ii) prediction markets are merely an example of a larger, very powerful category that has the potential to create better implementations in social media, science, news, governance, and other fields. I will call this category 'information finance'.
The dual nature of Polymarket: a betting site for participants, a news site for everyone else.
In the past week, Polymarket has been a very effective source of information regarding the US elections. Polymarket not only predicted a 60/40 chance of Trump winning (while other sources predicted 50/50, which is not that impressive), but also showcased other advantages: when the results came out, despite many experts and news sources trying to entice viewers with hopeful news for Harris, Polymarket directly revealed the truth: the chance of Trump winning was over 95%, while the chance of taking control of all government departments was over 90%.
Both screenshots were taken at 3:40 AM EST on November 6.
But for me, this is not even the best example of why Polymarket is interesting. So let’s look at another example: the elections in Venezuela in July. The day after the election, I remember catching a glimpse of people protesting the highly manipulated election results in Venezuela. At first, I didn’t pay much attention. I knew Maduro was one of those 'basically dictators', so I thought, he would certainly falsify every election result to stay in power, and of course, there would be protests, and of course, the protests would fail - unfortunately, many others did fail. But then while scrolling through Polymarket, I saw this:
People were willing to stake over $100,000 on the chance of Maduro being overthrown in this election at 23%. Now I started to pay attention.
Of course, we know the unfortunate outcome of this situation. Ultimately, Maduro did continue to hold power. However, the market made me realize that this time, the attempt to overthrow Maduro was serious. The scale of the protests was huge, and the opposition executed a surprisingly well-performing strategy to demonstrate to the world how fraudulent the elections were. If I hadn't received the initial signal from Polymarket 'this time, something is worth paying attention to', I would not have even started to pay attention.
You should never fully trust the Polymarket betting charts: if everyone believes the betting charts, then any wealthy person can manipulate the betting charts, and no one would dare to bet against them. On the other hand, fully trusting the news is also a bad idea. News has sensational motivations, exaggerating the consequences of anything for clicks. Sometimes that is reasonable, and sometimes it is not. If you see a sensational article but then go to the market and find that the probabilities of the relevant events haven't changed at all, then skepticism is warranted. Or if you see unexpectedly high or low probabilities in the market, or unexpected sudden changes, that is a signal for you to read the news and see what caused it. Conclusion: by reading both the news and the betting charts, you can get more information than by reading either one alone.
Let’s recap what’s happening here. If you are a bettor, then you can bet on Polymarket, which is a betting site for you. If you are not a bettor, then you can read the betting charts, which is a news site for you. You should never fully trust the betting charts, but I personally have made reading the betting charts a step in my information-gathering workflow (alongside traditional media and social media), which helps me obtain more information more effectively.
Information finance in a broader sense
Now, we enter the important part: predicting election outcomes is just the first application. The broader concept is that you can use finance as a way to coordinate incentive mechanisms to provide valuable information to the audience. Now, a natural reaction is: isn’t all finance fundamentally about information? Different participants will make different buying and selling decisions because they have different views on what will happen in the future (aside from individual needs such as risk preferences and hedging desires), and you can infer a lot about the world by reading market prices.
For me, information finance is like this, but structurally correct. Similar to the concept of structural correctness in software engineering, information finance is a discipline that requires you to (i) start from the facts you want to know, and then (ii) deliberately design a market to obtain that information from market participants in the best way.
Information finance is a three-sided market: bettors make predictions, readers read predictions. The market outputs predictions about the future as a public good (because that is what it is designed to do).
Prediction markets are one example: you want to know a specific fact about what will happen in the future, so you set up a market for people to bet on that fact. Another example is decision markets: you want to know which yields better results based on some metric M, decision A or decision B. To do this, you set up conditional markets: you ask people to bet (i) which decision will be chosen, (ii) if decision A is chosen, what the value of M will be, otherwise zero, (iii) if decision B is chosen, what the value of M will be, otherwise zero. With these three variables, you can determine whether the market believes decision A or decision B is more favorable for obtaining the value of M.
I expect that a technology driving the development of information finance over the next decade will be AI (whether large models or future technologies). This is because many of the most interesting applications of information finance relate to 'micro' issues: millions of small markets where decisions have relatively small impacts individually. In practice, low-volume markets often cannot operate effectively: for experienced participants, spending time on detailed analysis just to gain a few hundred dollars in profit makes no sense, and many believe such markets cannot operate at all without subsidies, as there are not enough naive traders to allow experienced traders to profit from anything other than the largest and most sensational issues. AI completely changes this equation, meaning that even in a market with a trading volume of $10, we can potentially obtain fairly high-quality information. Even if subsidies are needed, the amounts for each question become very affordable.
Information finance requires human distillation
Judgment
Suppose you have a trusted human judgment mechanism, and that mechanism has the legitimacy of being trusted by the entire community, but making judgments takes a long time and is costly. However, you want to access at least one approximate copy of that 'expensive mechanism' in real-time at a low cost. Here is an idea proposed by Robin Hanson: every time you need to make a decision, you establish a prediction market that forecasts what result that expensive mechanism would produce if called upon for the decision. You let the prediction market run and invest a small amount of funds to subsidize the market makers.
99.99% of the time, you actually won’t invoke expensive mechanisms: you might 'reverse the trade' and refund everyone’s contributions, or you might just give everyone zero, or you might look at whether the average price is closer to 0 or 1 and treat that as a basic fact. 0.01% of the time - possibly random, possibly focused on the markets with the highest trading volume, possibly a combination of both - you will actually run the expensive mechanism and compensate participants accordingly.
This gives you a trustworthy, neutral, quick, and cheap 'distilled version' of your original highly trustworthy but costly mechanism (using the term 'distilled' analogous to 'distilled' in LLMs). Over time, this distilled mechanism roughly reflects the behavior of the original mechanism - because only participants who help achieve that outcome can make money, while others will lose money.
Possible model of combining prediction markets + community notes.
This applies not only to social media but also to DAOs. One of the main issues with DAOs is that there are too many decisions, and most people are unwilling to engage, leading to either widespread delegation, with the risks of centralization and agency failures common in representative democracy, or vulnerability to attacks. If actual voting in a DAO happens infrequently and most decisions are determined by prediction markets, with human and AI combined to predict voting outcomes, then such a DAO could operate well.
As we saw in the example of decision markets, information finance contains many potential pathways to solve important issues in decentralized governance, with the key being the balance between market and non-market: the market is the 'engine', while other non-financial trust mechanisms are the 'steering wheel'.
Other use cases of information finance
Personal tokens - Many projects such as Bitclout (now deso), friend.tech, etc., which create tokens for everyone and make them easy to speculate on, are a category I call 'primitive information finance'. They intentionally create market prices for specific variables (i.e., the expectation of a person's future reputation), but the exact information revealed by the prices is too ambiguous and subject to reflexivity and bubble dynamics. It is possible to create improved versions of such protocols and address important issues like talent discovery by more carefully considering the economic design of the tokens (especially where their ultimate value comes from). Robin Hanson's idea of reputation futures is a possible ultimate state here.
Advertising - The ultimate 'expensive but trustworthy signal' is whether you would buy the product. Information finance based on that signal can be used to help people determine what to buy.
Scientific peer review - The scientific community has long faced a 'replication crisis', where certain famous results have become part of common wisdom but ultimately cannot be replicated in new research. We can attempt to identify results that need re-examination through prediction markets. Before re-examination, such markets also allow readers to quickly estimate how much they should trust any specific result. Experiments of this idea have been conducted and so far seem to have succeeded.
Public goods funding - One of the main issues with the public goods funding mechanisms used by Ethereum is its 'popularity contest' nature. Each contributor needs to conduct their own marketing campaign on social media to gain recognition, while those who lack the ability to do so or have more 'background' roles find it difficult to secure significant funding. An attractive solution is to attempt to track the entire dependency graph: for each positive outcome, which projects contributed how much to it, and then for each project, which projects contributed how much, and so on. The main challenge of this design is figuring out the weights of the edges to resist manipulation. After all, such manipulation has been occurring. Distilling human judgment mechanisms may help.
Conclusion
These ideas have been theorized for a long time: the earliest writings about prediction markets, even decision markets, date back decades, and similar discourses in financial theory are even older. However, I believe the current decade offers a unique opportunity, primarily for the following reasons:
Information finance solves the trust issues that people actually face. A common concern of this era is the lack of knowledge (even worse, the lack of consensus) about whom to trust in political, scientific, and business environments. Information finance applications can help be part of the solution.
We now have scalable blockchains as a foundation. Until recently, the fees were too high to truly realize these ideas. Now, they are no longer too high.
AI as participants. When information finance must rely on human participation for every issue, it is relatively difficult to operate effectively. AI greatly improves this situation, enabling effective markets even on small-scale issues. Many markets may have a combination of AI and human participants, especially when the number of specific issues suddenly shifts from small to large.
