Written by: Zhang Feng
1. Vitalik: Prediction markets as 'the antidote to emotionality'
Vitalik Buterin, co-founder of Ethereum, recently posted on social media, stating that in an era of rampant misinformation and emotional transmission on social media, prediction markets based on economic incentives can become an important tool for promoting rational discussion and filtering noise.
The core issue of social media lies in the 'emotional transmission economy'—content that triggers strong emotional responses is more likely to be disseminated, while rational and complex facts are often marginalized. This mechanism leads to public discourse being 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 distinctly 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 an example where Musk once posted that 'a civil war in the UK is inevitable', but the prediction market had only a 3% probability of occurrence. He believes that compared to the media lying without responsibility, prediction markets represent real monetary investment, making them more truthful and rational, with economic incentives providing a stronger 'truth-seeking' spirit.
In summary, the rationality of prediction markets is primarily reflected in three aspects: First, it provides a mechanism for aggregating collective intelligence, reflecting the consensus judgment of the group on the probability of an event occurring 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 adds a 'cost' to expressing opinions, reducing the likelihood of casually expressing extreme views. Historical data supports this view: from the Iowa Electronic Markets to platforms like PredictIt, the predictive accuracy of prediction markets often surpasses expert surveys and traditional polls in areas such as election outcomes and economic indicators.
II. The essential difference between prediction markets and gambling.
Many people simplify prediction markets as gambling, which, although superficially similar, ignores essential differences. The core characteristics of traditional gambling are: 1) event outcomes are usually unrelated to broader social values; 2) participant behavior does not affect the outcomes; 3) it primarily serves entertainment purposes. A well-functioning prediction market has 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 on 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. election, the prediction market's assessment of Trump's winning probability captured trends earlier and more accurately than most polls and expert analyses.
High-quality prediction markets typically focus on events with clear verification standards that are socially significant, 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 engage not only for profit; many also participate for 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 the non-public information or unique analyses they possess into market prices through trading.
A well-functioning prediction market can be seen as a decentralized intelligence analysis network that provides collective insights about the future in a distributed, censorship-resistant manner. This characteristic has unique value in areas such as crisis early warning and policy assessment. Gambling, on the other hand, generally does not produce such positive externalities.
III. The legal risks faced by prediction markets.
Despite its theoretical rationality, prediction markets face a complex network of legal risks in actual operation, which become the main obstacles to their compliance.
Many countries define 'investment contracts' as including profit expectations based on the efforts of others; some prediction market contracts may be deemed unregistered securities. The U.S. SEC has taken action against prediction market platforms multiple times, considering their trading contracts to meet the definition of securities. Designing a market structure that neither crosses the securities law red line nor compromises functional integrity is a long-term challenge for the industry.
Most jurisdictions strictly restrict monetary transactions based on uncertain events. Despite defenses for the informational function, legal texts often do not make this distinction. Laws such as the U.S. Federal (Professional and Amateur Sports Protection Act) and (Unlawful Internet Gambling Enforcement Act) directly impact the development of related prediction markets by prohibiting interstate sports betting.
Prediction markets can easily 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 identity verification 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 dissemination of false information and manipulation of large positions. Due to the typically small market size, such manipulations are easier to occur and harder to regulate.
Additionally, there are some practical operational issues. For instance, taxation, countries lack a unified standard for tax treatment of prediction market earnings, with some possibly being considered ordinary income, some as capital gains, and some even possibly regarded as illegal income that cannot be declared. This uncertainty hinders institutional participation. There is also cross-border regulatory coordination; the decentralized nature of blockchain technology gives prediction markets natural global accessibility, but this conflicts with the geographically-based sovereign legal systems. Platforms may face 'regulatory arbitrage' accusations or find themselves caught between multiple regulatory frameworks.
IV. Value confirmation of prediction markets excluding manipulation.
When we envision an ideal prediction market that excludes human manipulation, its rationality and social value will become more prominent.
Manipulation protection mechanisms. Through identity verification, position limits, abnormal transaction monitoring, and other technical and managerial means, 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 resolving trust issues in outcome adjudication.
Information efficiency embodiment. Studies show that unmanipulated prediction markets outperform traditional surveys and expert panels in information aggregation efficiency. Experiments at MIT Media Lab indicate that, with appropriate incentives, groups predict complex issues more accurately than most individual experts. This 'collective wisdom' has practical application value in areas like financial crisis early warning and pandemic development forecasting.
Policy assessment tools. Political scientists have proposed using prediction markets as 'policy analysis markets' to assess the outcomes of different policies through trading prices. This evaluation based on economic incentives may be closer to actual effects than ideology-based debates.
Corporate decision support. Internal prediction markets have been used by companies like Google and Microsoft for project timeline forecasting, market response assessment, and have achieved 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 with opposing views, and this compulsory clash of opinions helps form more balanced judgments.
V. Future compliance paths: seeking a balance between innovation and regulation.
Combining Vitalik's views and other positive factors, the compliance of prediction markets may need to develop along the following paths.
Appropriate layering. 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 MiCA framework for the classification of crypto asset services may provide a reference for this.
Internal applications, internal prediction markets in enterprises, governments, and research institutions may become breakthrough points. Such 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 regulators' perceptions 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 for prediction markets to test operations in a controlled environment. By limiting participant types, trading subjects, and funding scales, the information 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 demands. Technological innovation may unlock regulatory challenges.
From niche to mass, certain jurisdictions may adopt a gradual strategy of 'from niche to mass', first allowing prediction markets based on specific themes (such as technological progress and climate events), and then gradually expanding the scope. This path has already been evident in the acceptance of cryptocurrencies in some countries.
Cross-border coordination. As international organizations like the Financial Action Task Force (FATF) improve regulatory frameworks for virtual assets, cross-border regulatory coordination of prediction markets may become possible. 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, joint governance, and internal dispute resolution without relying on centralized regulation. Such bottom-up compliance attempts may provide new ideas for traditional regulation.
Vitalik views prediction markets as a 'social media antidote', which indeed provides a new moral foundation and narrative for its compliance. Historical experience shows that technologies with genuine social utility often find patterns of coexistence with regulation. Prediction markets may not completely 'comply' to become uncontested mainstream financial instruments but may likely gain legitimate space within specific boundaries—as a supplement to traditional information collection mechanisms, as a new approach to 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 to coexist as a special 'reality-check layer'—emotional claims need to face economic scrutiny, extreme predictions require real 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.
