The strongest breakout application of this round? V God talks about predicting the market and information finance: this project excites me.

One of the most exciting Ethereum applications for me is prediction market. In 2014, I wrote an article about futarchy, a governance model based on prediction proposed by Robin Hanson. As early as 2015, I was an active user and supporter of Augur (see my name in the Wikipedia article). I made $58,000 from betting on the 2020 election. This year, I have been a close supporter and follower of Polymarket.

For many, the prediction market is simply betting on elections, and betting on elections is gambling - if it can bring people enjoyment, that's great, but fundamentally, it's no more interesting than buying random Tokens on pump.fun. From this perspective, my interest in the prediction market seems confusing. Therefore, in this article, I aim to explain what excites me about this concept. In short, I believe that (i) even the existing prediction market is a very useful tool for the world, but also (ii) the prediction market is just an example of a larger, very powerful category that has the potential to create better social media, science, news, governance, and other industries. I will refer to this category as 'info finance'.

The dual nature of Polymarket: a betting site for participants, a news site for everyone else

Polymarket has been a very effective source of information about the US election in the past week. Polymarket not only predicted a 60/40 chance of Trump winning (compared to other sources' 50/50 prediction, which is not particularly impressive in itself), but also demonstrated other advantages: when the results came out, despite many experts and news sources trying to entice the audience to hear favorable news for Joe Biden, Polymarket directly revealed the truth: the probability of Trump winning is over 95%, and the probability of taking control of all government departments is over 90%.

Image Source: Golden Finance

Source: Two screenshots from Jinse Caijing were both taken at 3:40 am Eastern Time on November 6th.

But for me, this is not even the best example of what makes Polymarket interesting. So let's look at another example: the elections in Venezuela in July. The day after the elections, I remember seeing someone protesting the highly manipulated election results in Venezuela out of the corner of my eye. At first, I didn't pay much attention. I knew Maduro was already one of those "basically dictators," so I thought, of course he would falsify every election result to hold onto power, of course there would be protests, and of course the protests would fail - unfortunately, many others failed as well. But then as I scrolled through Polymarket, I saw this:

Image source: Golden Finance. People are willing to invest over 100,000 dollars, betting that there is a 23% chance of Maduro being overthrown in this election. Now I'm starting to follow.

Of course, we are aware of the unfortunate outcome of this situation. In the end, Maduro did remain in power. However, the market made me realize that this time, the attempt to overthrow Maduro was serious. The protests were massive and the opposition, with a surprisingly well-executed strategy, proved to the world how fraudulent the elections were. If I hadn't received Polymarket's initial signal, "This time, something is worth following," I wouldn't even have started following.

You should never fully trust Polymarket betting charts: if everyone believes in the betting charts, then anyone with money can manipulate them, and no one dares to bet against them. On the other hand, fully trusting the news is also a bad idea. The news has the motive of sensationalism, exaggerating the consequences of anything for the sake of clicks. Sometimes this is reasonable, and sometimes not. If you see a sensational article, but then you go to the market and find that the probability of the related event has not changed at all, then it is reasonable to be suspicious. 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: Compared to reading either one alone, by reading the news and betting charts, you can get more information.

Let's review what happened here. If you are a gambler, you can bet on Polymarket, which is a gambling website for you. If you are not a gambler, you can read the betting charts, which is a news website for you. You should never completely trust the betting charts, but personally, I have incorporated reading the betting charts as a step in my information gathering process (along with traditional media and social media), which helps me obtain more information more effectively.

The broader meaning of information finance

Now, we are entering the important part: predicting election results is just the first application. The broader concept is that you can use finance as a mechanism for coordinating incentives 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 (apart 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, this is what information finance is, but structurally correct. Similar to the concept of structural correctness in software engineering, information finance is a discipline that requires you (i) to start with the facts you want to know, and then (ii) deliberately design a market to obtain that information in the best way from market participants.

Image Source: Golden Finance

Information finance is a tripartite market: investors make predictions, readers read predictions, and the market outputs predictions about the future as public goods (because that is what it was designed to do).

Prediction markets are an 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 this fact. Another example is a decision market: you want to know which decision, A or B, will produce better results based on a certain indicator M. To achieve this, you set up a conditional market: you ask people to bet on (i) which decision they will choose, (ii) if they choose decision A, they will get the value of M, otherwise it will be zero, (iii) if they choose decision B, they will get the value of M, otherwise it will be zero. With these three variables, you can determine whether the market believes that decision A or decision B is more favorable for obtaining the value of M.

Image Source: Golden Finance

I expect that AI (either large models or future technologies) will be the technology that drives the development of information finance in the next decade. This is because many of the most interesting applications of information finance are related to 'micro' problems: millions of small markets, where individual decisions have relatively small impacts. In fact, low-volume markets often cannot operate effectively: for experienced participants, spending time on detailed analysis to earn a few hundred dollars in profit is meaningless, and many people even believe that such markets cannot operate without subsidies, because there are not enough naive traders, apart from the largest and most sensational issues, to allow experienced traders to profit from them. AI completely changes this equation, which means that even in markets with a volume of $10, we may be able to obtain high-quality information. Even if subsidies are needed, the subsidy amount for each problem becomes very affordable.

Information finance needs human distillation.

Judgment

Suppose you have a trustworthy mechanism for human judgment that has the legitimacy of the entire community trusting it, but making judgments requires a long time and high cost. However, you want to access at least one approximate copy of the "expensive mechanism" in real time at low cost. Here are Robin Hanson's ideas on what you can do: Every time you need to make a decision, you create a prediction market that predicts what results the expensive mechanism will make if called. You let the prediction market run and put a small amount of capital to subsidize the market makers.

99.99% of the time, you actually won't invoke expensive mechanisms: maybe you'll 'undo the transaction' and return everyone's investment, or you'll just give everyone zero, or you'll see if the average price is closer to 0 or 1 and treat it as a basic fact. 0.01% of the time - maybe random, maybe for the largest volume market, maybe a combination of both - you'll actually run expensive mechanisms and compensate participants accordingly.

This provides you with a trustworthy, neutral, fast, and cost-effective 'distilled version', which is a mechanism that is highly trustworthy but extremely expensive in its original form (using the term 'distilled' analogously to 'distilled' in LLM). Over time, this distilled mechanism roughly reflects the behavior of the original mechanism - because only participants who help achieve the result can make money, while others will lose money.

Image source: Possible prediction market + community note combination model from Golden Finance.

This applies not only to social media, but also to DAO. One of the main issues with DAO is that there are too many decision-making processes, and most people are unwilling to participate, which leads to either widespread use of delegation, the risk of centralization and delegation failure common in representative democracy, or vulnerability to attacks. If actual voting in DAO rarely occurs, and most things are decided by prediction markets, with a combination of human and AI predicting the voting results, then such a DAO may operate well.

As we have seen in the example of decision-making in the market, information finance contains many potential paths to solve important issues in Decentralization governance. The key lies in the balance between the market and non-market: the market is the 'engine', and some non-financial trust mechanisms are the 'steering wheel'.

Other Applications of Financial Information

Personal Tokens - projects like Bitclout (now known as DESO), friend.tech, and many others that create tokens for individuals and make them easy to speculate on - are a type of 'primitive information finance'. They intentionally create market prices for specific variables (i.e. expectations for a person's future reputation), but the exact information revealed by the price is too vague and subject to reflexivity and bubble dynamics. There may be improved versions of such protocols created that address important issues like talent discovery by more carefully considering the economic design of the token, particularly where its ultimate value comes from. Robin Hanson's concept of futarchy is one possible end state here.

Advertising - The ultimate 'expensive but reliable signal' is whether you will buy the product. Information finance based on this signal can help people determine what to buy.

Peer Review in Science - There has been a 'reproducibility crisis' in the scientific community, where certain prominent findings have become part of folk wisdom in some cases, but ultimately cannot be reproduced in new research. We can try to identify results that need to be re-examined through prediction markets. Before the re-examination, such markets will also allow readers to quickly assess how much they should trust any specific results. Experiments on this idea have been completed and seem to have been successful so far.

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 engage in their own marketing activities on social media to gain recognition, making it difficult for those who are unable to do so or have more 'background' roles to receive significant funding. One attractive solution is to attempt to track the entire dependency graph: for each positive outcome, which projects contributed to it, and then for each project, which projects contributed to it, and so on. The main challenge of this design is to find the weights of the edges that make it resistant to manipulation. After all, such manipulation has been happening. Distillation of human judgment mechanisms may be helpful.

Conclusion

These ideas have been theorized for a long time: the earliest works on predicting markets or even decision-making markets have been around for decades, while similar discourses in financial theory are even older. However, I believe that the past decade has provided a unique opportunity for the following main reasons:

Information finance solves the actual trust issues that people face. A common concern in this era is the lack of knowledge (or even worse, the lack of Consensus), not knowing whom to trust in political, scientific, and business environments. Information finance applications can help be part of the solution.

We now have a scalable Blockchain as the foundation. Until recently, the cost was too high to truly realize these ideas. Now, they are no longer too high.

AI as a participant. When information finance must rely on human participation for every issue, it is relatively difficult to play a role. AI greatly improves this situation, even on a small scale, it can achieve an effective market. Many markets may have a combination of AI and human participants, especially when the number of specific issues suddenly changes from small to large.

To make the most of this opportunity, we should go beyond just predicting elections and explore what information finance can bring us.

Special thanks to Robin Hanson and Alex Tabarrok for their feedback and comments

[Disclaimer] The market is risky, and investment needs to be cautious. This article does not constitute investment advice. Users should consider whether any opinions, views or conclusions in this article are in line with their specific circumstances. The responsibility for investment is self-assumed.

This article is authorized to be reproduced from: "Foresight News"

Original author: Vitalik, founder of Ether Square