从预测市场到信息金融

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) 预测市场即使存在至今，对世界来说仍然是一个非常有用的工具, but furthermore (ii) 预测市场只是更大、更强大的类别的一个例子，有潜力在社交媒体、科学、新闻、治理和其他领域创造更好的实现。我将这个类别标记为“信息金融“.

Polymarket 的两面性：为参与者提供的博彩网站，为其他所有人提供的新闻网站

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.

 两张截图均拍摄于美国东部时间 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, 当然 he would fake every election outcome to keep himself in power, 当然 some people would protest, and 当然 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 this election would be the one where Maduro would actually get struck down. 现在 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 这次推翻马杜罗政权的企图是认真的. 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 每个人 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. 如果你是博彩玩家，那么你可以向 Polymarket 存款，对你来说，这是一个博彩网站。如果你不是博彩玩家，那么你可以阅读图表，对你来说，这是一个新闻网站。你永远不应该完全相信图表，但我个人已经把阅读图表作为我信息收集工作流程中的一个步骤（与传统媒体和社交媒体一起），它帮助我更有效地获取更多信息。

更广泛意义上的信息金融

Now, we get to the important part: 预测选举只是第一个应用程序. The broader concept is that you can 使用财务作为协调激励措施的一种方式，以便为观众提供有价值的信息. Now, one natural response is: 从根本上来说，难道所有金融不是都与信息有关吗？ 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）从你想知道的事实开始，然后（ii）刻意设计一个市场，以便从市场参与者那里最佳地获取该信息.

信息金融是一个三边市场：投注者做出预测，读者阅读预测。市场将对未来的预测作为公共物品输出（因为这是它被设计的目的）。One example of this is 预测市场: 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 决策市场: 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 有条件市场：你要求人们押注 (i) 会选择哪个决策，(ii) 如果选择决策 A，则 M 的值，否则为零，(iii) 如果选择决策 B，则 M 的值，否则为零。有了这三个变量，你就可以确定市场认为决策 A 还是决策 B 对 M 的值更有利。

我预计未来十年将推动信息金融发展的一项技术是人工智能 (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.

信息金融需要人类的精炼判断

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 会 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% 的时间里，你实际上并不会调用昂贵的机制：也许你会“撤销交易”并返还每个人的投入，或者你只是给每个人零，或者你看看平均价格是否更接近 0 或 1 并将其视为基本事实。0.01% 的时间 – 可能是随机的，可能是针对交易量最大的市场，可能是两者的某种组合 – 你实际上运行昂贵的机制，并据此补偿参与者。

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.

可能的预测市场 + 社区笔记组合的模型。

这不仅适用于社交媒体，也适用于 DAO。DAO 的一个主要问题是，决策数量太多，大多数人都不愿意参与其中，这导致要么广泛使用委托，存在代议制民主中常见的那种集中化和委托代理失灵的风险，要么容易受到攻击。DAO 中实际投票很少发生，大多数事情都由预测市场决定，由人类和人工智能结合预测投票，这样可能会运行良好。

Just as we saw in the decision markets example, info finance contains many potential paths to solving important problems in decentralized governance. 关键是市场与非市场的平衡：市场是“引擎”，其他一些非金融化的信任机制是“方向盘”.

信息金融的其他用例

– 个人代币 – 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.

- 广告 – 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.

– 科学同行评审 – 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.

– 公共物品融资 – 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 依赖图: for each positive outcome, which projects contributed how much to it, and then for each of those projects, which projects contributed how much to that, 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.

结论

这些想法已经被理论化了很长时间：关于预测市场甚至决策市场的最早著作已有几十年历史，而金融理论的类似论述则更为古老。然而，我认为，当前十年提供了一个独特的机会，主要原因如下：

– 信息金融解决的是人们实际存在的信任问题。这个时代的一个普遍担忧是缺乏知识（更糟糕的是，缺乏共识），不知道在政治、科学和商业环境中应该信任谁。信息金融应用可以帮助成为解决方案的一部分。

– 我们现在拥有可扩展的区块链作为基础直到最近，费用仍然过高，无法真正实施这些想法。现在，费用不再过高。

– 人工智能作为参与者信息金融在必须依靠人类参与每个问题时，相对难以运作。人工智能极大地改善了这种情况，即使在小规模的问题上也能实现有效的市场。许多市场可能会有人工智能和人类参与者的组合，特别是当特定问题的数量突然从小变为大时。

为了充分利用这个机会，我们应该超越仅仅预测选举，探索信息金融还能为我们带来什么。

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