What Vitalik didn't fully explain: The key to compliance in prediction markets lies in the narrative.

Author: Zhang Feng

1. Vitalik: Prediction Markets as “Emotional Remedies”

Ethereum co-founder Vitalik Buterin recently posted on social media, believing that in the age of rampant misinformation and emotional dissemination on social media, prediction markets based on economic incentives can become an important tool for promoting rational discussions and filtering out noise.

The core issue of social media lies in “emotional传播经济学”—content that triggers strong emotional reactions is more likely to be disseminated, while rational, complex facts are often marginalized. This mechanism leads to a public discussion space filled with anger, opposition, and simplified narratives, while the truth becomes a secondary consideration. Vitalik believes that prediction markets, by introducing the mechanism of “betting real money,” can create a drastically different information verification environment: participants need to bear the economic consequences of their predictions, which forces them to conduct more prudent research and make more balanced judgments.

He cited that Musk once posted that “the English Civil War is inevitable,” but predicted that there was only a 3% chance of it happening in the prediction market. He believes that, compared to the media that lies without accountability, prediction markets involve real investments, making them more authentic and rational, with economic incentives enhancing their spirit of “truth-seeking.”

In summary, the rationality of prediction markets is primarily reflected in three aspects: first, it provides a mechanism for aggregating collective wisdom, reflecting the group's consensus judgment on the probability of an event occurring through price signals; second, it establishes an economic incentive mechanism for fact-checking, encouraging individuals to invest resources to verify or refute various claims; third, it adds a “cost” to expressing opinions, reducing the likelihood of casually making extreme statements. Historical data supports this view: from the Iowa Electronic Markets to platforms like PredictIt, prediction markets often exceed the accuracy of expert surveys and traditional polls in predicting election outcomes, economic indicators, and more.

2. The Essential Differences Between Prediction Markets and Betting

Many people equate prediction markets simply with gambling. While this analogy may seem similar on the surface, it overlooks essential differences. The core characteristics of traditional gambling are: 1) the outcomes of events are usually unrelated to broader social values; 2) participant behavior does not affect the outcomes; 3) it primarily serves entertainment purposes. In contrast, well-functioning prediction markets have the following distinguishing features:

The main value of prediction markets lies in information aggregation and price discovery. Each price represents the collective judgment of market participants regarding the probability of an event occurring, based on the integration of different information and analytical perspectives. This informational function gives prediction markets social utility, helping decision-makers, businesses, and the public better foresee the future. During the 2016 U.S. presidential election, prediction markets assessed the probability of Trump's victory earlier and more accurately than most polls and expert analyses, capturing trend changes.

High-quality prediction markets typically focus on events with clear verification criteria that are of significant social importance, such as election outcomes, policy changes, timelines for technological breakthroughs, etc. In contrast, traditional betting often involves sports events or random occurrences, which are less related to real-world decision-making.

Market participants in prediction markets engage in trading not only for profit, but many also do so for purposes of information acquisition, hedging risks, or expressing opinions. Research shows that some of the most active traders actually participate as “information contributors” rather than “gamblers”, integrating their non-public information or unique analyses into market prices through trading.

A well-functioning prediction market can be seen as a decentralized intelligence analysis network, capable of providing collective insights about the future in a decentralized and censorship-resistant manner. This characteristic holds unique value in areas such as crisis warning and policy assessment. Gambling, on the other hand, does not generate such positive externalities.

3. Overview of Legal Risks Facing Prediction Markets

Despite having theoretical rationality, prediction markets face a complex network of legal risks in practical operation, which become the main obstacles to their compliance:

The definition of “investment contracts” in various countries often includes the expectation of profits from the efforts of others, and certain prediction market contracts may be deemed unregistered securities. The U.S. SEC has taken action against prediction market platforms multiple times, asserting that their trading contracts meet the definition of securities. Designing a market structure that neither crosses the red line of securities laws nor compromises functional integrity is a long-standing challenge for the industry.

Most jurisdictions strictly limit monetary transactions based on uncertain events. Despite the defense of informational functions, legal texts often do not make this distinction. Laws such as the United States Federal Professional and Amateur Sports Protection Act and the Unlawful Internet Gambling Enforcement Act directly impact the development of related prediction markets.

Prediction markets can easily become intertwined with certain illegal activities. On one hand, anonymous or pseudonymous transactions may make prediction markets a channel for money laundering, which forces compliant platforms to implement strict customer identification procedures, but this creates tension with the value of privacy in blockchain culture. On the other hand, similar to financial markets, prediction markets may face issues such as the spread of false information and manipulation of large positions. Due to the typically smaller market size, such manipulations are more likely to occur and harder to regulate.

There are also some practical issues that exist. For example, taxation, countries lack a unified standard for the tax treatment of earnings from prediction markets, with some potentially seen as ordinary income, some as capital gains, and some even potentially regarded as illegal income that cannot be declared. This uncertainty hinders institutional participation. Additionally, there is cross-border regulatory coordination, the decentralized nature of blockchain technology inherently provides global accessibility for prediction markets, but this conflicts with region-based sovereign legal systems. Platforms may face accusations of “regulatory arbitrage” or fall into the cracks of multiple national regulations.

4. Valuation Confirmation of Prediction Markets Excluding Manipulation

When we envision a prediction market that excludes human manipulation and operates ideally, its rationality and social value will be further highlighted.

Manipulation Protection Mechanism. Through technical and management measures such as identity verification, position limits, and abnormal transaction monitoring, it becomes difficult for large participants to manipulate prices through false trading or information. The development of decentralized oracles (such as Chainlink) and dispute resolution mechanisms (such as Kleros) provides new ideas for addressing the trust issues in outcome adjudication.

Information Efficiency Demonstration. Research shows that unmanipulated prediction markets outperform traditional surveys and expert panels in terms of information aggregation efficiency. Experiments from the MIT Media Lab indicate that, with appropriate incentives, the collective predictions on complex issues surpass the accuracy of the vast majority of individual experts. This “collective wisdom” has practical applications in areas such as financial crisis warnings and pandemic development forecasts.

Policy Assessment Tool. Political scientists have proposed using prediction markets as “policy analysis markets” to assess the potential outcomes of different policies through trading prices. This economically incentivized evaluation may be closer to actual effects than ideologically based debates.

Corporate Decision Support. Internal prediction markets have been utilized by companies such as Google and Microsoft for project timeline forecasting, market response evaluation, etc., achieving more accurate results than traditional managerial forecasts. This application completely avoids legal gray areas and demonstrates the instrumental value of prediction markets.

Cognitive Bias Correction. Behavioral economics research has found that economic incentives can significantly reduce cognitive biases such as confirmation bias and overconfidence. In prediction markets, participants are forced to confront trading counterparts who hold opposing views, and this mandatory clash of opinions helps to form more balanced judgments.

V. Future Compliance Path: Seeking Balance Between Innovation and Regulation

Combining Vitalik's views and other positive factors, the prediction for the market's compliance may need to develop along the following paths.

Appropriate stratification, regulators may gradually recognize the distinction between “information markets with social value” and “purely entertainment gambling.” The former may obtain special licenses but must meet stricter requirements for information transparency, manipulation protection, and public interest. The EU MiCA framework for the classification and regulation of crypto asset services may provide a reference for this.

Internal applications, internal prediction markets for enterprises, governments, and research institutions may become a breakthrough. These applications do not involve public trading and are entirely based on instrumental purposes, making them more likely to gain legal recognition. The accumulation of successful cases may gradually change regulators' perception of the nature of prediction markets.

Regulatory Sandbox, mechanisms such as the UK FCA regulatory sandbox and the Singapore MAS fintech sandbox provide the possibility to test market operations in a controlled environment. By limiting the types of participants, the scope of trading targets, and the scale of funds, the informational value and social benefits can be verified under controllable risks.

Technical nesting, privacy-enhancing technologies such as zero-knowledge proofs can meet regulatory audit requirements while protecting user privacy; the transparency and automated execution of smart contracts can reduce manipulation risks; decentralized identity systems can balance anonymity with KYC requirements. Technological innovation may unlock established regulatory challenges.

From point to surface, certain jurisdictions may adopt a gradual strategy of “from niche to mainstream,” initially allowing prediction markets based on specific themes (such as technological advancements and climate events), and then gradually expanding the scope. This path has been evident in the process of cryptocurrency acceptance in some countries.

Cross-border coordination, as international organizations such as the Financial Action Task Force (FATF) improve the regulatory framework for virtual assets, it is predicted that cross-national regulatory coordination in the market may become possible. Unified classification standards, anti-money laundering requirements, and information-sharing mechanisms can reduce compliance conflicts and regulatory arbitrage.

Community Autonomy, Decentralized Autonomous Organizations (DAOs) may develop effective self-discipline mechanisms within the community, maintaining market health through reputation systems, collective governance, and internal dispute resolution, without relying on centralized regulation. This bottom-up compliance attempt may provide new ideas for traditional regulation.

Vitalik views prediction markets as a “social media emotional antidote,” which indeed provides a new ethical foundation and value narrative for their compliance. Historical experience shows that technological innovations with real social utility often find a way to coexist with regulation. Prediction markets may not be completely “compliant” as uncontroversial mainstream financial instruments, but they are likely to gain a legitimate existence within certain boundaries—as a complement to traditional information gathering mechanisms, as a new method of policy analysis, and as an auxiliary system for corporate decision-making.

The future form of prediction markets may not be to replace social media as the mainstream information platform, but rather to coexist as a special “reality verification layer”—emotional claims need to face economic scrutiny, extreme predictions must incur actual costs, and the wisdom of the crowd has the opportunity to be presented in more precise numbers. The degree to which this balance is achieved will determine whether prediction markets can truly move from the legal margins to a compliant future.