What Vitalik didn't elaborate on: Compliance of prediction markets hinges not on technology but on narrative.

Written by: Zhang Feng

1. Vitalik: Predictive markets as an “emotional remedy”

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

The core issue of social media lies in “the economics of emotional transmission” — content that elicits strong emotional responses is more likely to be disseminated, while rational and complex facts are often marginalized. This mechanism leads to a public discourse space filled with anger, opposition, and simplified narratives, where the truth becomes a secondary consideration. Vitalik believes that prediction markets, by introducing the mechanism of “betting with real money,” can create a fundamentally different information verification environment: participants are required 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 is only a 3% chance of it happening in the market. He believes that, compared to the irresponsibility of media lies, prediction markets involve real monetary investments, making them more genuine and rational, and the economic incentives give them a stronger spirit of “truth-seeking.”

In summary, the rationality of prediction markets is mainly reflected in three aspects: First, it provides a mechanism for aggregating collective wisdom, reflecting the consensus judgment of the group on the probability of an event through price signals; Second, it establishes an economic incentive mechanism for fact-checking, encouraging people to invest resources to verify or refute various claims; Third, it increases the “cost” of 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 surpass expert surveys and traditional polls in their predictive accuracy regarding election outcomes, economic indicators, and more.

2. The Essential Difference Between Prediction Markets and Gambling

Many people equate prediction markets with gambling, and while this analogy may seem superficially similar, it ignores the 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, healthy prediction markets have the following distinguishing characteristics:

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, enabling decision-makers, businesses, and the public to better foresee the future. During the 2016 U.S. presidential election, prediction markets captured the trend changes in Trump’s probability of winning earlier and more accurately than most polls and expert analyses.

High-quality prediction markets typically focus on events with clear validation criteria that are of significant societal importance, such as election outcomes, policy changes, and timelines for technological breakthroughs. In contrast, traditional betting often involves sports events or random occurrences, which have a lower relevance to real-world decision-making.

Market participants in prediction markets engage in trading not only for profit, but many also participate for the 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,” incorporating their non-public information or unique analyses into market prices through trading.

Well-functioning prediction markets can be seen as a decentralized intelligence analysis network that provides collective insights about the future in a distributed, censorship-resistant manner. This feature holds unique value in areas such as crisis warning and policy evaluation. Gambling, on the other hand, does not generate such positive externalities.

III. A panorama of legal risks faced by the prediction market

Despite its theoretical rationality, prediction markets face a complex web of legal risks in practice, which become the main obstacles to its compliance:

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

Most jurisdictions strictly limit monetary transactions based on uncertain events. Despite the information function defense, legal texts often do not make this distinction. U.S. federal laws such as the Professional and Amateur Sports Protection Act and the Unlawful Internet Gambling Enforcement Act directly affect the development of related prediction markets for prohibiting interstate sports betting.

Prediction markets are relatively easy to intertwine with some illegal activities. On one hand, anonymous or pseudonymous trading may make prediction markets a channel for money laundering, forcing compliant platforms to implement strict customer identification procedures, which creates tension with the privacy values 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 manipulation is more likely to occur and harder to regulate.

In addition, there are some practical operational issues. For example, taxation; countries lack a unified standard for the taxation of predictive market profits. Some may be considered ordinary income, some as capital gains, and some may even be regarded as illegal income that cannot be declared. This uncertainty hinders institutional participation. Furthermore, there is the issue of cross-border regulatory coordination; the decentralized nature of blockchain technology makes predictive markets inherently globally accessible, but this conflicts with region-based sovereign legal systems. Platforms may face accusations of “compliance arbitrage” or find themselves caught in the cracks of multi-national regulation.

4. Value 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 become more prominent.

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

Information efficiency is reflected. Research shows that unmanipulated prediction markets outperform traditional surveys and expert panels in information aggregation efficiency. Experiments from the MIT Media Lab indicate that, under appropriate incentives, the collective prediction accuracy on complex issues exceeds that of the vast majority of individual experts. This “collective intelligence” has practical applications in areas such as financial crisis warning and epidemic development forecasting.

Policy evaluation tools. 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 ideology-based debates.

Corporate decision-making assistance. Internal prediction markets have been used by companies like Google and Microsoft for project timeline forecasting, market response evaluation, etc., achieving more accurate results than traditional management forecasts. This application completely avoids legal gray areas and demonstrates the instrumental value of prediction markets.

Cognitive bias correction. Research in behavioral economics has found that economic incentives can significantly reduce cognitive biases such as confirmation bias and overconfidence. In prediction markets, participants are forced to face counterparties with 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.

Proper stratification may lead regulatory bodies to gradually accept 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 classification and regulatory approach to crypto asset services under the EU MiCA framework may provide a reference for this.

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

Regulatory sandboxes, such as the UK's FCA regulatory sandbox and Singapore's MAS fintech sandbox, provide the possibility for testing market operations in a controlled environment. By limiting the types of participants, the scope of trading targets, and the scale of funds, the information value and social benefits can be verified under controlled risk.

Technical nesting, zero-knowledge proofs, and other privacy-enhancing technologies 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 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, climate events), and then gradually expanding the scope. This path has already been evidenced in the acceptance of cryptocurrencies in some countries.

Cross-border coordination, with the improvement of the regulatory framework for virtual assets by international organizations such as the Financial Action Task Force (FATF), the prediction of multinational 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 and decentralized autonomous organizations (DAOs) may develop effective self-regulatory mechanisms through reputation systems, collective governance, and internal dispute resolution, maintaining market health without relying on centralized oversight. This bottom-up compliance attempt may provide new ideas for traditional regulation.

Vitalik's perspective of viewing prediction markets as a “social media emotional antidote” indeed provides a new moral foundation and narrative of value for its compliance. Historical experience shows that technological innovations with real social utility often find modes of coexistence with regulation. Prediction markets may not completely be “compliant” as uncontroversial mainstream financial instruments, but they are likely to gain legitimate space within specific boundaries—as a supplement to traditional information collection mechanisms, as a new method for 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 bear actual costs, and the collective wisdom 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.