From prediction markets to info finance

Special thanks to Robin Hanson and Alex Tabarrok for feedback and review

特别感谢 Robin Hanson 和 Alex Tabarrok 的反馈和审查

One of the Ethereum applications that has always excited me the most are prediction markets.

最让我兴奋的以太坊应用之一是预测市场。

I wrote about futarchy , a model of prediction-based governance conceived by Robin Hanson , in 2014.

我在 2014 年写了一篇关于 futarchy 的文章，这是 Robin Hanson 提出的一种基于预测的治理模型。

I was an active user and supporter of Augur back in 2015 (look, mommy, my name is in the Wikipedia article !).

早在 2015 年，我就是 Augur 的活跃用户和支持者（看，妈妈，我的名字出现在维基百科文章中！）。

I earned $58,000 betting on the election in 2020.

我通过 2020 年大选投注赚取了 58,000 美元。

And this year, I have been a close supporter and follower of Polymarket.

今年，我一直是 Polymarket 的密切支持者和追随者。

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.

对很多人来说，预测市场就是对选举进行投注，而对选举进行投注就是赌博——如果它能帮助人们享受生活就好了，但从根本上来说并不比在 Pump.fun 上购买随机硬币更有趣。

From this perspective, my interest in prediction markets may seem confusing.

从这个角度来看，我对预测市场的兴趣似乎令人困惑。

And so in this post I aim to explain what it is about the concept that excites me.

因此，在这篇文章中，我的目的是解释这个让我兴奋的概念是什么。

In short, I believe that (i) prediction markets even as they exist today are a very useful tool for the world , but furthermore (ii) prediction markets are only one example of a much larger incredibly powerful category , with potential to create better implementations of social media, science, news, governance, and other fields.

简而言之，我相信（i）预测市场即使存在于今天，对世界来说也是一个非常有用的工具，但此外（ii）预测市场只是一个更大且极其强大的类别的一个例子，有潜力为社交媒体、科学、新闻、治理和其他领域创造更好的实施。

I shall label this category " info finance ".

我将把这个类别标记为“信息金融”。

In the past week, Polymarket has been a very effective source of information about the US election.

过去一周，Polymarket 一直是有关美国大选的非常有效的信息来源。

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.

Polymarket 不仅预测特朗普将以 60/40 的赔率获胜，而其他消息来源预测为 50/50（本身并不算太令人印象深刻），它还显示了其他优点：当结果公布时，尽管许多专家和新闻来源不断向观众串扰，希望有某种对卡马拉有利的消息，但 Polymarket 展示了直接的事实：特朗普有超过 95% 的获胜机会，并且有超过 90% 的机会夺取政府所有部门的控制权。同时。

But to me this is not even the best example of why Polymarket is interesting.

但对我来说，这甚至不是 Polymarket 有趣的最好例子。

So let us go to a different example: the elections in Venezuela in July .

让我们看一个不同的例子：委内瑞拉七月的选举。

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.

选举发生的第二天，我记得用眼角的余光看到了人们抗议委内瑞拉高度操纵的选举结果。

At first, I thought nothing of it.

起初我并没有多想。

I knew that Maduro was one of those "basically a dictator" figures already, and so I figured, of course he would fake every election outcome to keep himself in power, of course some people would protest, and of course the protest would fail - as, unfortunately, so many others do.

我知道马杜罗已经是那些“基本上是独裁者”的人物之一，所以我想，他当然会伪造每一次选举结果以保持自己的权力，当然有些人会抗议，当然抗议会失败 - 不幸的是，正如许多其他人所做的那样。

But then I was scrolling Polymarket, and I saw this:

但后来我浏览 Polymarket，看到了这个：

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.

人们愿意投入超过十万美元，押注这次选举有 23% 的可能性让马杜罗真正被推翻。

Now I was paying attention.

现在我开始注意了。

Of course, we know the unfortunate result of this situation.

当然，我们知道这种情况的不幸结果。

Ultimately, Maduro did stay in power.

最终，马杜罗确实继续掌权。

However, the markets clued me in to the fact that this time , the attempt to unseat Maduro was serious .

然而，市场让我意识到，这一次推翻马杜罗的企图是认真的。

There were huge protests, and the opposition played a surprisingly well-executed strategy to prove to the world just how fraudulent the elections were.

出现了大规模的抗议活动，反对派采取了令人惊讶的执行良好的策略，向世界证明选举有多么欺诈。

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.

如果不是从 Polymarket 收到“这次有事情要注意”的最初信号，我根本不会开始那么关注。

You should never trust the charts entirely: if everyone trusts the charts, then anyone with money can manipulate the charts and no one will dare to bet against them.

你永远不应该完全相信图表：如果每个人都相信图表，那么任何有钱的人都可以操纵图表，没有人敢与它们下注。

On the other hand, trusting the news entirely is also a bad idea.

另一方面，完全相信新闻也是一个坏主意。

News has an incentive to be sensational, and play up the consequences of anything for clicks.

新闻有耸人听闻的动机，并为了点击而夸大任何事情的后果。

Sometimes, this is justified, sometimes it's not.

有时，这是合理的，有时则不然。

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.

如果你看到一篇耸人听闻的文章，但后来你进入市场，发现相关事件的概率根本没有改变，那么你就有理由怀疑。

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.

或者，如果您看到市场上出现意外的高或低概率，或者意外的突然变化，那么这是一个信号，需要仔细阅读新闻并看看可能是什么导致了这种情况。

Conclusion: you can be more informed by reading the news and the charts, than by reading either one alone .

结论：通过阅读新闻和图表，您可以比单独阅读其中任何一种获得更多信息。

Let's recap that's going on here.

让我们回顾一下这里发生的事情。

If you are a bettor, then you can deposit to Polymarket, and for you it's a betting site.

如果您是投注者，那么您可以向 Polymarket 存款，对您来说，这是一个投注网站。

If you are not a bettor, then you can read the charts, and for you it's a news site .

如果您不是博彩玩家，那么您可以阅读图表，对您来说这是一个新闻网站。

You should never trust the charts entirely, but I personally have already incorporated reading the charts as one step in my information-gathering workflow (alongside traditional media and social media), and it has helped me become more informed more efficiently.

你永远不应该完全相信图表，但我个人已经将阅读图表作为我的信息收集工作流程的一个步骤（与传统媒体和社交媒体一起），它帮助我更有效地获得更多信息。

Now, we get to the important part: predicting the election is just the first app .

现在，我们进入重要的部分：预测选举只是第一个应用程序。

The broader concept is that you can use finance as a way to align incentives in order to provide viewers with valuable information .

更广泛的概念是，您可以使用金融作为调整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: isn't all finance fundamentally about information?

现在，一种自然的反应是：难道所有的金融从根本上来说不都是关于信息的吗？

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.

不同的参与者会因为对未来发生的事情有不同的看法而做出不同的买卖决定（除了风险偏好和对冲愿望等个人需求），而且你可以通过阅读市场价格来推断出很多关于世界的知识。

To me, info finance is that, but correct by construction .

对我来说，信息金融就是这样，但通过构建是正确的。

Similar to the concept of correct-by-construction in software engineering, info finance is a discipline where you (i) start from a fact that you want to know, and then (ii) deliberately design a market to optimally elicit that information from market participants .

与软件工程中的构建正确的概念类似，信息金融是一门学科，您（i）从您想知道的事实开始，然后（ii）故意设计一个市场，以最佳方式从市场参与者那里获取该信息。

Info finance as a three-sided market: bettors make predictions, readers read predictions.

信息金融是一个三边市场：投注者做出预测，读者阅读预测。

The market outputs predictions about the future as a public good (because that's what it was designed to do).

市场输出对未来的预测作为公共物品（因为这就是它的设计目的）。

One example of this is prediction markets : 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.

其中一个例子是预测市场：您想知道未来将发生的一个具体事实，因此您建立了一个市场，供人们对该事实进行押注。

Another example is decision markets : you want to know whether decision A or decision B will produce a better outcome according to some metric M.

另一个例子是决策市场：您想知道根据某个指标 M，决策 A 或决策 B 是否会产生更好的结果。

To achieve this, you set up conditional markets : you ask people to bet on (i) which decision will be chosen, (ii) value of M if decision A is chosen, otherwise zero, (iii) value of M if decision B is chosen, otherwise zero.

为了实现这一目标，您设置了条件市场：您要求人们下注 (i) 将选择哪个决策，(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。

Given these three variables, you can figure out if the market thinks decision A or decision B is more bullish for the value of M.

给定这三个变量，您可以弄清楚市场是否认为决策 A 或决策 B 对 M 的值更看好。

One technology that I expect will turbocharge info finance in the next decade is AI (whether LLMs or some future technology).

我预计人工智能（无论是法学硕士还是某些未来技术）将在未来十年推动信息金融的发展。

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.

这是因为信息金融的许多最有趣的应用都在“微观”问题上：数以百万计的迷你市场，这些决策单独产生的后果相对较低。

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.

在实践中，交易量低的市场往往无法有效运作：对于经验丰富的参与者来说，仅仅为了几百美元的利润而花时间进行详细分析是没有意义的，许多人甚至认为，如果没有补贴，这样的市场根本无法运作，因为除了最大和最耸人听闻的问题之外，没有足够的天真交易者让经验丰富的交易者从中获利。

AI changes that equation completely, and means that we could potentially get reasonably high-quality info elicited even on markets with $10 of volume.

人工智能完全改变了这个等式，这意味着即使在交易量为 10 美元的市场上，我们也有可能获得相当高质量的信息。

Even if subsidies are required, the size of the subsidy per question becomes extremely affordable.

即使需要补贴，每个问题的补贴规模也变得非常实惠。

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.

假设你有一个你信任的人类判断机制，并且具有整个社区信任它的合法性，但是做出判断需要很长时间和很高的成本。

However, you want access to at least an approximate copy of that "costly mechanism" cheaply and in real time.

然而，您希望以廉价且实时的方式至少访问该“昂贵机制”的近似副本。

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 would make on the decision if it was called.

罗宾·汉森 (Robin Hanson) 的想法是：每次你需要做出决定时，你都可以建立一个预测市场，预测如果调用该决定，成本高昂的机制会产生什么结果。

You let the prediction market run, and put in a small amount of money to subsidize market makers .

你让预测市场运行起来，并投入少量资金来补贴做市商。

99.99% of the time, you don't actually call the costly mechanism: perhaps you "revert the trades" and give everyone back what they put in, or you just give everyone zero, or you see if the average price was closer to 0 or 1 and treat that as the ground truth.

99.99% 的情况下，您实际上并没有调用成本高昂的机制：也许您“恢复交易”并将每个人都归还他们投入的内容，或者您​​只是给每个人零，或者您查看平均价格是否接近 0 或 1 并将其视为基本事实。

0.01% of the time - perhaps randomly, perhaps for the highest-volume markets, perhaps some combination of both - you actually run the costly mechanism, and compensate participants based on that.

0.01% 的时间——也许是随机的，也许是针对交易量最大的市场，也许是两者的某种组合——你实际上运行了昂贵的机制，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 ).

这为您提供了一个可信的中立快速且廉价的“蒸馏版本”，是您原来高度值得信赖但成本高昂的机制（使用“蒸馏”一词来类比法学硕士蒸馏）。

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.

随着时间的推移，这种提炼出来的机制大致反映了原始机制的行为——因为只有帮助其实现这一结果的参与者才能赚钱，而其他人则赔钱。

Mockup of possible prediction markets + Community Notes combo.

可能的预测市场+社区笔记组合的模型。

This has applications not just in social media, but also for DAOs .

这不仅适用于社交媒体，也适用于 DAO。

A major problem of DAOs is that there is such a large number of decisions that most people are not willing to participate in most of them, leading to either widespread use of delegation, with risk of the same kinds of centralization and principal-agent failures we see in representative democracy, or vulnerability to attack.

DAO 的一个主要问题是，决策数量如此之多，以至于大多数人不愿意参与其中的大多数决策，从而导致要么广泛使用授权，从而面临与代议制民主中类似的集权和委托代理失败的风险，要么容易受到攻击。

A DAO where actual votes only happen very rarely, and most things are decided by prediction markets with some combination of humans and AI predicting the votes, could work well.

实际投票很少发生，大多数事情都是由预测市场决定，并结合人类和人工智能来预测投票，这样的 DAO 可能会运作良好。

Just as we saw in the decision markets example, info finance contains many potential paths to solving important problems in decentralized governance.

正如我们在决策市场示例中看到的那样，信息金融包含许多解决去中心化治理中重要问题的潜在途径。

The key is the balance between market and non-market: the market is the "engine", and some other non-financialized trustworthy mechanism is the "steering wheel" .

关键是市场与非市场的平衡：市场是“发动机”，其他一些非金融化的可信机制是“方向盘”。

Personal tokens - 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".

个人代币——诸如 Bitclout（现在为 deso）、friend.tech 和许多其他为每个人创建代币并使得对这些代币进行投机变得容易的项目类型——是我称之为“原始信息金融”的类别。

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.

他们故意为特定变量（即对一个人未来地位的预期）创造市场价格，但价格所揭示的确切信息太不具体，并且容易受到反身性和泡沫动态的影响。

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.

通过更加谨慎地对待代币的经济设计，特别是其最终价值的来源，有可能创建此类协议的改进版本，并使用它们来解决人才发现等重要问题。

Robin Hanson's idea of prestige futures is one possible end state here.

罗宾·汉森（Robin Hanson）关于声望未来的想法是一种可能的最终状态。

Advertising - the ultimate "expensive but trustworthy signal" is whether or not you will buy a product.

广告——最终的“昂贵但值得信赖的信号”是你是否会购买产品。

Info finance based off of that signal could be used to help people to identify what to buy.

基于该信号的信息金融可用于帮助人们确定要购买什么。

Scientific peer review - 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.

科学同行评审——科学界存在着一场持续的“复制危机”，在某些情况下成为民间智慧一部分的著名结果最终根本无法被新的研究复制。

We can try to identify results that need re-checking with a prediction market.

我们可以尝试确定需要通过预测市场重新检查的结果。

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.

在重新检查完成之前，这样的市场还可以让读者快速估计他们应该相信任何特定结果的程度。

Experiments of this idea have been done, and so far seem successful .

这个想法的实验已经完成，到目前为止似乎很成功。

Public goods funding - one of the main problems with public goods funding mechanisms used in Ethereum is the "popularity contest" nature of them.

公共物品融资——以太坊中使用的公共物品融资机制的主要问题之一是其“人气竞赛”性质。

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.

每个贡献者都需要在社交媒体上开展自己的营销业务才能获得认可，而没有足够能力做到这一点的贡献者，或者本质上具有更多“背景”角色的贡献者，很难获得大量资金。

An appealing solution to this is to try to track an entire dependency graph : 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.

一个有吸引力的解决方案是尝试跟踪整个依赖关系图：对于每个积极的结果，哪些项目对其贡献了多少，然后对于每个项目，哪些项目对其贡献了多少，等等。

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 .

这种设计的主要挑战是以一种难以操纵的方式计算出边缘的权重——毕竟，这种操纵已经一直在发生。

A distilled human judgement mechanism could potentially help.

精炼的人类判断机制可能会有所帮助。

These ideas have been theorized about for a long time: the earliest writings about prediction markets and even decision markets are decades old, and theory of finance saying similar things is even older.

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的著作已有数十年的历史，而金融理论说类似的事情则更古老。

However, I would argue that the current decade presents a unique opportunity, for several key reasons:

然而，我认为当前十年提供了一个独特的机会，原因如下：

To take full advantage of this opportunity, it's time to go beyond just predicting elections, and explore the rest of what info finance can bring us.

为了充分利用这个机会，是时候超越预测选举的范围，并探索信息金融可以为我们带来的其他内容了。