The future form of the market may not be to replace social media as the mainstream information platform, but rather to coexist with it as a special "reality verification layer".
Article by Zhang Feng
Text source: WeChat Official Account "Digital New Financial Report"
I. Vitalik: Predicting Markets as an "Emotional Relief"
Ethereum co-founder Vitalik Buterin recently posted on social media, arguing that in an era of rampant misinformation and emotional outbursts on social media, prediction markets based on economic incentives can be an important tool for promoting rational discussion and filtering out noise.
The core problem with social media lies in the "economics of emotional dissemination"—content that evokes strong emotional reactions is more likely to spread, while rational and complex facts are often marginalized. This mechanism leads to public discourse rife with anger, antagonism, and simplistic narratives, while truth becomes a secondary consideration. Vitalik argues that prediction markets, by introducing a "real money bet" mechanism, can create a radically different information verification environment: participants must bear the economic consequences of their predictions, forcing them to conduct more prudent research and make more balanced judgments.
He cited an example: Musk once posted that "a civil war in Britain is inevitable," but the market predicted only a 3% chance of it happening. He argued that compared to the media lying without accountability, the prediction market involves real money investment, making it more authentic and rational, and the economic incentives make it more "truth-seeking."
In summary, the rationale for prediction markets lies primarily in three aspects: First, they provide a mechanism for aggregating collective wisdom, reflecting the group's consensus on the probability of events through price signals; second, they establish an economic incentive mechanism for fact-checking, encouraging people to invest resources in verifying or refuting various claims; and third, they add a "cost" to the expression of opinions, reducing the likelihood of arbitrarily making extreme statements. Historical data supports this view: from the Iowa Electronic Marketplace to platforms like PredictIt, prediction markets often outperform expert surveys and traditional polls in their accuracy in predicting election results, economic indicators, and other areas.
II. The essential difference between prediction markets and gambling
Many people equate prediction markets simply with gambling, an analogy that, while superficially similar, overlooks the fundamental differences. The core characteristics of traditional gambling are: 1) the outcome of an event is usually unrelated to broader social value; 2) participant behavior does not affect the outcome; and 3) it primarily serves entertainment purposes. In contrast, a well-functioning prediction market possesses the following distinguishing characteristics:
The primary value of prediction markets lies in information aggregation and price discovery. Each price represents a collective judgment by 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 policymakers, businesses, and the public to better anticipate the future. During the 2016 US presidential election, prediction markets captured the trend shift earlier and more accurately than most polls and expert analyses.
High-quality prediction markets typically focus on events with clear validation criteria and significant social impact, such as election results, policy changes, and timelines for technological breakthroughs. In contrast, traditional betting often involves sporting events or random events, with less correlation to real-world decision-making.
Market participants are not only driven by profit; many also engage in trading for information gathering, risk hedging, or to express their opinions. Research indicates that some of the most active traders actually participate as "information contributors" rather than "gamblers," incorporating their non-public information or unique analysis into market prices through trading.
A well-functioning prediction market can be viewed as a decentralized intelligence analysis network, capable of providing collective insights into the future in a distributed and 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 generate such positive externalities.
III. A Panoramic View of Legal Risks Facing the Predictive Market
Despite its theoretical rationale, the prediction market faces a complex network of legal risks in its actual operation, which become a major obstacle to its compliance:
The definitions of "investment contracts" in different countries often include the expectation of profit from the efforts of others, and some prediction market contracts may be considered unregistered securities. The U.S. SEC has taken action against prediction market platforms multiple times, believing that their trading contracts meet the definition of securities. How to design a market structure that neither crosses the line of securities law nor compromises its functionality remains a long-standing challenge for the industry.
Most jurisdictions strictly limit monetary transactions based on uncertain events. While there is an information function defense, legal provisions often do not make this distinction. The prohibition of interstate sports betting by U.S. federal laws such as the Professional and Amateur Sports Protection Act (PSPA) and the Illegal Internet Gambling Enforcement Act directly impacts the development of related prediction markets.
Prediction markets are prone to being intertwined with illegal activities. On the one hand, anonymous or pseudo-anonymous transactions could turn prediction markets into money laundering channels, forcing compliant platforms to implement strict customer verification procedures, which clashes with the privacy values inherent in blockchain culture. On the other hand, similar to financial markets, prediction markets may face issues such as the spread of misinformation and manipulation of large positions. Due to their typically small market size, such manipulation is easier to occur and more difficult to regulate.
In addition, there are some practical operational issues. For example, taxation: there is a lack of unified standards for the tax treatment of prediction market returns across countries. Some may be considered ordinary income, some capital gains, and some may even be considered illegal income and cannot be declared. This uncertainty hinders institutional participation. Another issue is cross-border regulatory coordination. The decentralized nature of blockchain technology makes prediction markets inherently globally accessible, but this conflicts with geographically based sovereign legal systems. Platforms may face accusations of "compliance arbitrage" or find themselves caught in the regulatory gaps of multiple countries.
IV. Value Confirmation in Prediction Markets Excluding Manipulation
When we envision a predictive market that operates ideally and excludes human manipulation, its rationality and social value become even more apparent.
Manipulation prevention mechanisms. Through technologies and management measures such as identity verification, position limits, and abnormal transaction monitoring, it becomes difficult for large participants to manipulate prices through fraudulent transactions or information. The development of decentralized oracles (such as Chainlink) and dispute resolution mechanisms (such as Kleros) has provided new approaches to resolving trust issues in outcome adjudication.
Information efficiency is evident. Research shows that unmanipulated prediction markets outperform traditional surveys and expert panels in information aggregation efficiency. Experiments at the MIT Media Lab demonstrate that, with appropriate incentives, the accuracy of group predictions on complex issues surpasses that of the vast majority of individual experts. This "collective wisdom" has practical applications in areas such as financial crisis early warning and pandemic development forecasting.
Policy evaluation tools. Political scientists have proposed using forecasting markets as "policy analysis markets," assessing the potential outcomes of different policies through transaction prices. This evaluation, based on economic incentives, may be closer to actual effects than debates based on ideology.
Corporate decision support. Internal forecasting markets have been used by companies such as Google and Microsoft for project timeline forecasting and market response assessment, achieving more accurate results than traditional management forecasts. This application completely avoids legal gray areas, demonstrating the instrumental value of forecasting 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; this mandatory clash of opinions helps to form a more balanced judgment.
V. Future Compliance Path: Finding a Balance Between Innovation and Regulation
Combining Vitalik's perspective with other positive factors, the market's compliance process is likely to evolve along the following path.
With appropriate stratification, regulators may gradually accept the distinction between "information markets with social value" and "purely recreational gambling." The former may receive special licenses, but will need to meet stricter requirements regarding information transparency, manipulation prevention, and public interest. The EU's MiCA framework's approach to the categorization and regulation of crypto-asset services may offer a useful reference for this.
Internal applications, such as prediction markets within enterprises, governments, and research institutions, may be a breakthrough. These applications do not involve public trading, are purely instrumental in purpose, and are more likely to gain legal approval. The accumulation of successful cases may gradually change regulators' understanding 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 and operating prediction markets in a controlled environment. By limiting the types of participants, the scope of trading instruments, and the size of funds, their informational value and social benefits can be verified under controllable risks.
Technological integration and privacy-enhancing technologies such as zero-knowledge proofs can protect user privacy while meeting regulatory audit requirements; the transparency and automated execution of smart contracts can reduce the risk of manipulation; decentralized identity systems can balance anonymity and KYC requirements. Technological innovation may solve the established regulatory challenges.
Starting with specific examples and gradually expanding outwards, some jurisdictions may adopt a "niche to mainstream" strategy, first allowing prediction markets based on specific themes (such as technological advancements or climate events) and then gradually broadening their scope. This approach has already been demonstrated in the acceptance of cryptocurrencies in some countries.
Cross-border coordination is becoming increasingly possible as international organizations such as the Financial Action Task Force (FATF) refine their regulatory frameworks for virtual assets. Unified classification standards, anti-money laundering requirements, and information-sharing mechanisms can reduce compliance conflicts and regulatory arbitrage.
Community self-governance, particularly through decentralized autonomous organizations (DAOs), can develop effective community self-regulation mechanisms to maintain market health without relying on centralized regulation, through reputation systems, collaborative governance, and internal dispute resolution. This bottom-up approach to compliance may offer new insights into traditional regulation.
Vitalik's perspective of viewing prediction markets as a "social media sentiment remedy" does indeed provide a new ethical foundation and value narrative for their compliance. Historical experience shows that technological innovations with real social utility often find a model for coexisting with regulation. Prediction markets may not be completely "compliant" into an undisputed mainstream financial instrument, but they are likely to gain a legitimate space within certain boundaries—as a supplement 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 with it as a special "reality check layer"—emotional claims need to face economic scrutiny, extreme predictions will incur real costs, and collective wisdom has the opportunity to be represented by more accurate figures. The degree to which this balance is achieved will determine whether prediction markets can truly move from the legal periphery to a compliant future.
