Vitalik full text: From prediction markets to 'information finance'

Information itself can become a complete market, this article is from Vitalik Buterin's article 'From prediction markets to info finance', compiled, translated and written by Odaily. (Background brief: Polymarket's mysterious Whale won 50 million U.S. dollars, how did he correctly predict the election result?) (Background supplement: High Reconstruction Specialist》I bet on Polymarket to win the election, and lost two dollars) The U.S. election has further heated up the prediction market Polymarket, with people seeking profits starting to bet, and those seeking results using it as a news data platform to obtain information. As an 'out-of-the-box' Blockchain application, Polymarket combines on-chain funds and real-world predictions well. Vitalik has repeatedly praised Polymarket in his articles, and he himself is also an early supporter of the prediction market Augur. Today, Vitalik discussed 'information finance' through prediction markets. The following is the full text, translated by Odaily Star Daily: One of the most exciting Ethereum applications for me is the prediction market. I wrote an article about futarchy as early as 2014, which is a governance model based on prediction 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 people, prediction markets are just gambling on elections, and gambling on elections is gambling. If it can help the public enjoy the fun, that's great, but fundamentally, it's no more interesting than randomly buying MEME on pump.fun. From this perspective, my excitement about prediction markets seems incomprehensible. So in this article, I will explain what concepts of prediction markets interest me. In short, I believe: 1. The prediction market that exists now is a very useful tool for the world; 2. Furthermore, the prediction market is just a precursor to a more popular field, with the potential to be applied to social media, science, news, governance, and other areas, which I will label as 'information finance'. Polymarket's duality: a betting website for participants, a news website for everyone else. In the past week, Polymarket has been a very effective source of information about the U.S. election. Polymarket not only predicted Trump's chances of winning as 60/40 (while other sources predicted 50/50, which is not very impressive in itself), but also showed other advantages: when the results came out, despite many experts and news sources constantly leading the audience, hoping they would hear favorable news about Harris, Polymarket directly revealed the truth: Trump's chances of winning were over 95%, and the probability of taking control of all government departments was over 90%. But for me, this isn't even the most interesting example of Polymarket. So let's look at another example: in July, the day after the Venezuelan presidential election, I inadvertently saw someone protesting the highly manipulated presidential election in Venezuela. Initially, I didn't pay much attention. I knew Maduro was already one of those 'basically dictators', so I thought, of course he would forge every election result to maintain his power, and there would be protests, which would fail. Unfortunately, many others failed too. Later, when I browsed Polymarket, I saw this: People were willing to bet over $100,000 that there was a 23% chance that Maduro would be overthrown in the Venezuelan election, and now I noticed it. Of course, we still know that the overthrow is unlikely. In the end, Maduro continued to hold power. But the market made me realize that this attempt to overthrow Maduro was serious. There were large-scale protests at the time, and the opposition unexpectedly took a well-executed strategy, proving to the world that this election was fraudulent. If I hadn't received the initial signal from Polymarket 'this time, something to pay attention to', I wouldn't even have started paying so much attention. You shouldn't completely trust charts: if everyone believes the chart, then anyone with money can manipulate the chart, and no one dares to bet. On the other hand, completely trusting the news is also a good way. The news has a sensational motive, exaggerating the consequences of anything for click-through rates. Sometimes things are reasonable, sometimes not. If you see a sensational article and then check the market and find that the probability of the relevant event has not changed at all, then it is reasonable to be skeptical. In addition, if you see an unexpected high or low probability of an event happening on the market, or a sudden change, this is a signal, read the news and see what caused this conclusion. Conclusion: By reading the news and charts, you can get more information than just browsing one of them. If you are a gambler, you can deposit with Polymarket, for you, this is a betting website. If you are not a gambler, you can read chart data, for you, this is a news website. You should never fully trust the chart, but I have personally made reading chart data a step in my information gathering process (along with traditional media and social media), which helps me get more information more effectively. A broader meaning of information finance Predicting election results is just one use case. A more general concept is that you can use finance as a way of coordinating incentives to provide valuable information to the audience. Now, a natural reaction is: isn't all finance fundamentally related to information? Different participants 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 preference and hedging). You can infer a lot about the world by reading market prices. For me, this is what information finance is, but in a structural sense, it is similar to the concept of being structurally correct in software engineering. Information finance is a discipline that requires you to 1. Start with the facts you want to know; 2. Then deliberately design a market to obtain that information in the best way possible from market participants. One example is the prediction market: you want to know something about the future, so you create a market for people to bet on it. Another example is a decision market: you want to know whether decision A or decision B will produce better results based on a certain indicator M, to achieve this, you create a conditional market: you let people bet on which decision will be made: if decision A is chosen, it is the value of M, otherwise it is zero; if decision B is chosen, it is the value of M, otherwise it is zero. With these three variables, you can calculate whether the market thinks decision A or decision B is more favorable to the value of M. I expect that in the next ten years, artificial intelligence (whether LLMs or some future technology) will have a huge impact on the financial industry. This is because many applications of information finance are related...