Prediction Markets

Prediction markets aggregate information by enabling participants to trade on the outcomes of future events. These markets have proven remarkably accurate at forecasting elections, product launches, policy decisions, and other events. However, current prediction markets are limited to events with publicly observable outcomes. The vast majority of interesting questions involve outcomes that are not immediately public or require private information to verify.

Private Data Markets

Many valuable predictions depend on non-public information. Will a company’s next quarterly earnings exceed analyst expectations? Will a clinical trial show positive results? Will a government implement a specific policy change? These questions have clear answers that could be verified, but the verification requires access to private corporate data, research results, or confidential government information.

Swarm Network enables prediction markets that settle based on verified private data. Market operators can specify settlement conditions that reference private information, with verification occurring through zero-knowledge proofs that confirm outcomes without exposing the underlying data. A market on corporate earnings could settle based on verified financial results without revealing detailed financial statements. A market on clinical trial outcomes could settle based on verified results without exposing patient data or proprietary research details.

These private data markets create several benefits. Information aggregation improves as markets can address questions that were previously impossible to settle objectively. Insider information becomes valuable in a legitimate way, as people with special knowledge can participate in markets without violating confidentiality. Forecasting quality improves as markets can incorporate private information that affects outcomes. New market categories emerge around questions that were previously impossible to market-make.

Event-Driven Markets

Many prediction markets settle based on real-world events that are not immediately reflected in public data. Political prediction markets might settle on policy implementations that occur gradually. Technology markets might settle on product capabilities that require expert assessment. Social markets might settle on sentiment shifts that are not easily quantified.

Swarm Network’s AI agent network can detect and verify complex real-world events, enabling prediction markets that settle on nuanced outcomes. Agents monitor news sources, official announcements, social media, and other information streams to detect when specified events occur. Human verifiers provide oversight for ambiguous cases, ensuring that settlement is accurate even for complex or disputed events.

This capability expands prediction markets to address questions that require interpretation and judgment rather than simple data lookup. Markets can trade on “soft” outcomes like sentiment, adoption, or influence that are meaningful but not easily quantified. The combination of AI detection and human verification ensures accurate settlement while maintaining the speed and efficiency that makes prediction markets valuable.

Conditional Markets

Conditional prediction markets enable trading on outcomes that depend on specific conditions being met. For example, a market might trade on “Company X’s stock price if Product Y launches” or “Election outcome if Candidate Z participates.” These conditional markets provide valuable information about causal relationships and counterfactuals, but they require verifying both the condition and the outcome.

Swarm Network enables complex conditional markets by verifying both conditions and outcomes through its verification pipeline. Markets can specify arbitrary conditions that must be verified before settlement occurs, with verification happening through zero-knowledge proofs of private data or AI agent verification of real-world events. This capability enables sophisticated market structures that provide deeper insights than simple outcome markets.