從預測市場到資訊金融

One of the Ethereum applications that has always excited me the most are prediction markets. I wrote about futarchy, a model of prediction-based governance conceived by Robin Hanson, in 2014. I was an active user and supporter of Augur back in 2015 (look, mommy, my name is in the Wikipedia article!). I earned $58,000 betting on the election in 2020. And this year, I have been a close supporter and follower of Polymarket.

To many people, prediction markets are about betting on elections, and betting on elections is gambling – nice if it helps people enjoy themselves, but fundamentally not more interesting than buying random coins on pump.fun. From this perspective, my interest in prediction markets may seem confusing. And so in this post I aim to explain what it is about the concept that excites me. In short, I believe that (i) 預測市場即使存在於今天，對世界來說也是一個非常有用的工具, but furthermore (ii) 預測市場只是一個更大、更強大的類別的一個例子，有潛力在社群媒體、科學、新聞、治理和其他領域創造更好的實施。我將把這個類別標記為“資訊金融“.

Polymarket 的兩個面孔：為參與者提供的投注網站，為其他人提供的新聞網站

In the past week, Polymarket has been a very effective source of information about the US election. Not only did Polymarket predict Trump would win with 60/40 odds while other sources predicted 50/50 (not too impressive by itself), it also showed other virtues: when the results were coming out, while many pundits and news sources kept stringing viewers along with hope of some kind of favorable news for Kamala, Polymarket showed the direct truth: Trump had a greater than 95% chance of victory, and a greater than 90% chance of seizing control of all branches of government at the same time.

 兩張螢幕截圖皆拍攝於美國東部時間 11 月 6 日凌晨 3:40But to me this is not even the best example of why Polymarket is interesting. So let us go to a different example: the elections in Venezuela in July. The day after the election happened, I remember seeing out of the corner of my eye something about people protesting a highly manipulated election result in Venezuela. At first, I thought nothing of it. I knew that Maduro was one of those “basically a dictator” figures already, and so I figured, 當然 he would fake every election outcome to keep himself in power, 當然 some people would protest, and 當然 the protest would fail – as, unfortunately, so many others do. But then I was scrolling Polymarket, and I saw this:

People were willing to put over a hundred thousand dollars on the line, betting that there is a 23% chance that 這 election would be the one where Maduro would actually get struck down. 現在 I was paying attention.

Of course, we know the unfortunate result of this situation. Ultimately, Maduro did stay in power. However, the markets clued me in to the fact that 這次，推翻馬杜羅的企圖是嚴重的. There were huge protests, and the opposition played a surprisingly well-executed strategy to prove to the world just how fraudulent the elections were. Had I not received the initial signal from Polymarket that “this time, there is something to pay attention to”, I would not have even started paying that much attention.

You should never trust the charts entirely: if 每個人 trusts the charts, then anyone with money can manipulate the charts and no one will dare to bet against them. On the other hand, trusting the news entirely is also a bad idea. News has an incentive to be sensational, and play up the consequences of anything for clicks. Sometimes, this is justified, sometimes it’s not. If you see a sensational article, but then you go to the market and you see that probabilities on relevant events have not changed at all, it makes sense to be suspicious. Alternatively, if you see an unexpectedly high or low probability on the market, or an unexpectedly sudden change, that’s a signal to read through the news and see what might have caused it. Conclusion: you can be more informed by reading the news 和 the charts, than by reading either one alone.

Let’s recap that’s going on here. 如果您是投注者，那麼您可以向 Polymarket 存款，對您來說，這是一個投注網站。如果您不是博彩玩家，那麼您可以閱讀圖表，對您來說這是一個新聞網站。你永遠不應該完全相信圖表，但我個人已經將閱讀圖表作為我的資訊收集工作流程的一個步驟（與傳統媒體和社交媒體一起），它幫助我更有效地獲得更多資訊。

更廣泛的資訊金融

Now, we get to the important part: 預測選舉只是第一個應用程序. The broader concept is that you can 使用金融作為調整激勵措施的一種方式，以便為觀眾提供有價值的訊息. Now, one natural response is: 難道所有的金融本質上不都是關於資訊的嗎？ Different actors make different buy and sell decisions because of different opinions about what will happen in the future (in addition to personal needs like risk preferences and desire to hedge), and you can read market prices to infer a lot of knowledge about the world.

To me, info finance is that, but correct by construction. Similar to the concept of correct-by-construction in software engineering, info finance is a discipline where you (i) 從您想知道的事實開始，然後 (ii) 故意設計一個市場，以最佳方式從市場參與者那裡獲取該信息.

資訊金融是一個三邊市場：投注者做出預測，讀者閱讀預測。市場輸出對未來的預測作為公共物品（因為這就是它的設計目的）。One example of this is 預測市場: you want to know a specific fact that will take place in the future, and so you set up a market for people to bet on that fact. Another example is 決策市場: you want to know whether decision A or decision B will produce a better outcome according to some metric M. To achieve this, you set up 有條件市場：您要求人們下注 (i) 將選擇哪個決策，(ii) 如果選擇決策 A，則 M 的值，否則為零，(iii) 如果選擇決策 B，則 M 的值，否則為零。給定這三個變量，您可以弄清楚市場是否認為決策 A 或決策 B 對 M 的值更看好。

我預計人工智慧將在未來十年內推動資訊金融的一項技術 (whether LLMs or some future technology). This is because many of the most interesting applications of info finance are on “micro” questions: millions of mini-markets for decisions that individually have relatively low consequence. In practice, markets with low volume often do not work effectively: it does not make sense for a sophisticated participant to spend the time to make a detailed analysis just for the sake of a few hundred dollars of profit, and many have even argued that without subsidies such markets won’t work at all because on all but the most large and sensational questions, there are not enough naive traders for sophisticated traders to take profit from. AI changes that equation completely, and means that we could potentially get reasonably high-quality info elicited even on markets with $10 of volume. Even if subsidies 是 required, the size of the subsidy per question becomes extremely affordable.

資訊金融提煉人類判斷

Suppose that you have a human judgement mechanism that you trust, and that has the legitimacy of a whole community trusting it, but which takes a long time and a high cost to make a judgement. However, you want access to at least an 近似副本 of that “costly mechanism” cheaply and in real time. Here is Robin Hanson’s idea for what you can do: every time you need to make a decision, you set up a prediction market on what outcome the costly mechanism 會 make on the decision if it was called. You let the prediction market run, and put in a small amount of money to subsidize market makers.

99.99% 的時候，你實際上並沒有調用昂貴的機制：也許你「恢復交易」並將每個人都歸還他們投入的東西，或者你只是給每個人零，或者你看看平均價格是否接近 0或1 並將其視為基本事實。 0.01% 的時間——也許是隨機的，也許是針對最大交易量的市場，也許是兩者的某種組合——你實際上運行了昂貴的機制，並據此補償參與者。

This gives you a credibly neutral fast and cheap “distilled version” of your original highly trustworthy but highly costly mechanism (using the word “distilled” as an analogy to LLM distillation). Over time, this distilled mechanism roughly mirrors the original mechanism’s behavior – because only the participants that help it have that outcome make money, and the others lose money.

可能的預測市場+社區筆記組合的模型。

這不僅適用於社交媒體，也適用於 DAO。 DAO 的一個主要問題是，決策數量如此之多，以至於大多數人不願意參與其中的大多數決策，從而導致廣泛使用授權，從而帶來我們所面臨的中心化和委託代理失敗的風險。代議制民主，或易受攻擊的弱點。實際投票很少發生，大多數事情都是由預測市場決定，並結合人類和人工智慧來預測投票，這樣的 DAO 可能會運作得很好。

Just as we saw in the decision markets example, info finance contains many potential paths to solving important problems in decentralized governance. 關鍵是市場與非市場的平衡：市場是“引擎”，其他一些非金融化的可信機制是“方向盤”.

資訊金融的其他用例

– 個人代幣 – the genre of projects such as Bitclout (now deso), friend.tech and many others that create a token for each person and make it easy to speculate on these tokens – are a category that I would call “proto info-finance”. They are deliberately creating market prices for specific variables – namely, expectations of future prominence of a person – but the exact information being uncovered by the prices is too unspecific and subject to reflexivity and bubble dynamics. There is a possibility to create improved versions of such protocols, and use them to solve important problems like talent discovery, by being more careful about the economic design of a token, particularly where its ultimate value comes from. Robin Hanson’s idea of prestige futures is one possible end state here.

- 廣告 – the ultimate “expensive but trustworthy signal” is whether or not you will buy a product. Info finance based off of that signal could be used to help people to identify what to buy.

– 科學同儕審查 – there is an ongoing “replication crisis” in science where famous results that have in some cases become part of folk wisdom end up not being reproduced at all by newer studies. We can try to identify results that need re-checking with a prediction market. Before the re-checking is done, such a market would also give readers a quick estimate of how much they should trust any specific result. Experiments of this idea have been done, and so far seem successful.

– 公共產品資助 – one of the main problems with public goods funding mechanisms used in Ethereum is the “popularity contest” nature of them. Each contributor needs to run their own marketing operation on social media in order to get recognized, and contributors who are not well-equipped to do this, or who have inherently more “background” roles, have a hard time getting significant amounts of money. An appealing solution to this is to try to track an entire 依賴圖: for each positive outcome, which projects contributed how much to it, and then for each of those projects, which projects contributed how much to 那, and so on. The main challenge in this kind of design is figuring out the weights of the edges in a way that is resistant to manipulation – after all, such manipulation happens all the time already. A distilled human judgement mechanism could potentially help.

結論

這些想法已經被理論化了很長一段時間：關於預測市場甚至決策市場的最早的著作已有數十年的歷史，而描述類似事情的金融理論則更古老。然而，我認為當前十年提供了一個獨特的機會，原因如下：

– 資訊金融解決人們實際存在的信任問題。這個時代的一個普遍擔憂是，在政治、科學和商業背景下，缺乏關於信任誰的知識（更糟的是，缺乏共識）。資訊金融應用程式可以幫助成為解決方案的一部分。

– 我們現在有可擴展的區塊鏈作為基礎。直到最近，費用太高，無法實際實施大部分想法。現在，他們已經不再太高了。

– 人工智慧作為參與者。當資訊金融必須依賴人類參與每個問題時，它的工作相對困難。人工智慧極大地改善了這種情況，即使在小規模問題上也能實現有效的市場。許多市場可能會有人工智慧和人類參與者的結合，特別是當特定問題的數量突然從小到大轉變時。

為了充分利用這個機會，是時候超越預測選舉的範圍，並探索資訊金融可以為我們帶來的其他內容了。

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