Author: Zhang Feng

1. Vitalik: Prediction Markets as an "Antidote to Emotionalism"

Ethereum co-founder Vitalik Buterin recently posted on social media, suggesting that in an era of rampant misinformation and emotional communication on social media, economically incentivized prediction markets can serve as important tools for promoting rational discussion and filtering out noise.

The core issue with social media lies in the "economics of emotional communication"—content that elicits strong emotional reactions is more likely to be shared, while rational, complex facts are often marginalized. This mechanism results in public discourse being filled with anger, division, and simplified narratives, with the truth becoming a secondary consideration. Vitalik believes that prediction markets, by introducing a mechanism of "betting with real money," can create a distinctly different environment for information validation: participants must bear the economic consequences of their predictions, which compels them to conduct more careful research and make more balanced judgments.

He cited an example where Musk tweeted that "the English Civil War is inevitable," but the prediction market indicated only a 3% probability of it happening. He argues that unlike media that can lie without accountability, prediction markets involve real monetary stakes, making them more truthful and rational, with economic incentives fostering a spirit of "truth-seeking."

In summary, the rationality of prediction markets is primarily reflected in three aspects: first, they provide a mechanism for aggregating collective wisdom, reflecting the consensus judgment of the group on the probability of an event through price signals; second, they establish an economic incentive mechanism for fact-checking, encouraging people to invest resources to verify or refute various claims; third, they add a "cost" to expressing opinions, reducing the likelihood of casually voicing extreme statements. Historical data supports this view: from the Iowa Electronic Markets to platforms like PredictIt, prediction markets often outperform expert surveys and traditional polls in predicting election outcomes and economic indicators.

2. The Essential Difference Between Prediction Markets and Gambling

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

The primary 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 winning more accurately and earlier than most polls and expert analyses.

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

Participants in prediction markets are not solely motivated by profit; many also engage in trading for information acquisition, risk hedging, 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.

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

3. The Legal Risk Landscape Facing Prediction Markets

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

Countries often define "investment contracts" to include profit expectations 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 does not cross the legal boundaries of securities law while maintaining functional integrity is a long-standing challenge for the industry.

Most jurisdictions strictly limit monetary transactions based on uncertain events. Despite defenses based on informational functions, 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 impact the development of related prediction markets by prohibiting interstate sports betting.

Prediction markets can easily intertwine with certain 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 inherent in blockchain culture. On the other hand, similar to financial markets, prediction markets may face issues such as misinformation dissemination and large position manipulation. Due to the typically smaller market size, such manipulations are more likely to occur and harder to regulate.

There are also practical operational issues. For example, taxation, countries lack a unified standard for the tax treatment of prediction market earnings; some may be viewed as ordinary income, some as capital gains, and some may even be considered illegal income that cannot be reported. This uncertainty hinders institutional participation. Additionally, there is the issue of cross-border regulatory coordination; the decentralized nature of blockchain technology gives prediction markets global accessibility, but this conflicts with region-based sovereign legal systems. Platforms may face accusations of "regulatory arbitrage" or find themselves caught in the crossfire of multiple national regulations.

4. Value Confirmation of Prediction Markets Excluding Manipulation

When we envision an ideal prediction market that excludes human manipulation, its rationality and social value become even more pronounced.

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

Information efficiency. Research indicates that non-manipulated prediction markets outperform traditional surveys and expert panels in terms of information aggregation efficiency. Experiments from MIT's Media Lab show that, under appropriate incentives, groups can predict complex issues more accurately than the vast majority of individual experts. This "collective wisdom" has practical applications in areas such as financial crisis warning and pandemic development forecasting.

Policy evaluation tools. Political scientists have proposed using prediction markets as "policy analysis markets," assessing 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 used by companies like Google and Microsoft for project timeline forecasting and market response evaluation, achieving more accurate results than traditional management forecasts. This application completely avoids the legal gray area, demonstrating 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 with opposing views, and this forced clash of opinions helps form more balanced judgments.

5. Future Compliance Pathways: Seeking Balance Between Innovation and Regulation

Combining Vitalik's perspective with other positive factors, the compliance of prediction markets may need to develop along the following pathways.

Appropriate stratification; regulatory agencies may 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 EU's MiCA framework for the classification of crypto asset services may provide a reference for this.

Internal applications; internal prediction markets within companies, governments, and research institutions may become a breakthrough point. These applications do not involve public trading and are entirely based on instrumental purposes, making them easier to gain legal recognition. The accumulation of successful cases may gradually change regulatory agencies' perceptions of the nature of prediction markets.

Regulatory sandboxes; mechanisms like the UK's FCA regulatory sandbox and Singapore's MAS fintech sandbox provide opportunities for prediction markets to test operations in a controlled environment. By limiting participant types, trading subject ranges, and funding scales, the information value and social benefits can be verified under controlled risks.

Technological embedding; privacy-enhancing technologies like 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 resolve established regulatory challenges.

From point to surface; certain jurisdictions may adopt a "from niche to mass" gradual strategy, first allowing prediction markets based on specific themes (such as technological advancements or climate events) and then gradually expanding the scope. This pathway has already been evident in some countries' acceptance of cryptocurrencies.

Cross-border coordination; as international organizations like the Financial Action Task Force (FATF) improve regulatory frameworks for virtual assets, cross-border regulatory coordination for prediction markets may become feasible. Unified classification standards, anti-money laundering requirements, and information-sharing mechanisms can reduce compliance conflicts and regulatory arbitrage.

Community self-governance; decentralized autonomous organizations (DAOs) may develop effective community self-regulatory mechanisms, 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's view of prediction markets as an "antidote to emotionalism on social media" indeed provides a new moral foundation and narrative for their compliance. Historical experience shows that technological innovations with genuine social utility often find ways to coexist with regulation. Prediction markets may not completely "comply" to become uncontroversial mainstream financial tools, 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 must face economic scrutiny, extreme predictions must bear actual costs, and 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.

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