I. Why the “Technical Layer” Is More Critical Than the Trading Layer in Prediction Markets

For most users, prediction markets seem like an “event betting” or “probability trading” product: you buy a specific outcome, and if your prediction is correct, you earn a profit. However, in the blockchain world, the true core of prediction markets isn’t in the trades themselves, but in how outcomes are credibly determined and settled.

Unlike spot trading or perpetual contracts, prediction markets aren’t based on on-chain assets—they’re based on real-world events. These events usually occur off-chain, often involving time delays, information asymmetry, and even subjective interpretations. If outcome determination becomes contentious, the entire market’s credibility collapses.

Therefore, for on-chain prediction markets, the primary technical challenges aren’t about “how to match trades,” but rather three fundamental questions:

• How can events be precisely defined?
• How can real-world information be securely brought on-chain?
• When disputes arise, how can the system self-correct instead of relying on centralized arbitration?

This is why prediction markets are often called “price oracles for the real world,” rather than just another financial application.

II. How Events Are Defined: The Smallest Technical Unit of Prediction Markets

In on-chain prediction markets, an event is a structured data object. A well-designed event must be both unambiguous and settleable from both technical and economic perspectives.

1. Three Core Elements of Event Definition

A valid prediction event typically needs to clarify three points:

• What (What happens): Specify the event details, such as “Will a certain asset reach a specific price before a given date?”
• When (When does it end): Set a clear deadline or observation window to prevent “delayed settlement” or repeated information revisions.
• Outcome (Possible results): Define the set of outcomes, which commonly include: binary events (Yes / No); multiple-choice events (A / B / C); numerical range events (which range does it fall into).

The more ambiguous an event is, the higher the systemic risk. This is one of the main reasons early prediction markets failed.

2. Why Ambiguous Events Are the Biggest Enemy of Prediction Markets

For example, statements like “Did a certain policy succeed?” or “Was a particular project accepted by the market?” have real-world significance but are nearly impossible to settle on-chain. On-chain prediction markets naturally favor events that are verifiable, quantifiable, and confirmable by third parties.

Mature prediction market platforms often sacrifice “macro narrative appeal” for settlement certainty. This trade-off isn’t conservative—it’s technological rationality.

III. Oracle Systems: Bringing the Real World On-Chain

Once an event is clearly defined, the next critical question is: Who informs the blockchain world about what actually happened in reality? This is precisely the role of the oracle.

1. The Role of Oracles in Prediction Markets

In prediction markets, oracles don’t “predict”—they input final facts. They decide:

• Which outcome is recognized as true
• Whether settlement is triggered
• Whether challenges or disputes are allowed

Oracles are both the most crucial and most vulnerable single point in prediction markets.

2. Comparison of Mainstream Oracle Types

Centralized Oracles

Outcomes are provided directly by platforms, teams, or designated data sources.

Advantages:

• Fast and low-cost
• Superior user experience

Disadvantages:

• Strong trust assumptions
• Easily influenced by regulators or interested parties

This model is common in early-stage or semi-centralized prediction markets.

Decentralized Oracles

Consensus is reached through multiple nodes, data sources, or economic incentive mechanisms.

Advantages:

• Strong censorship resistance
• Aligned with Web3 principles

Disadvantages:

• Higher costs
• Slower response times
• Complex mechanisms

This approach is better suited for high-value events with significant dispute risk.

Social Consensus Oracles

Users can submit outcomes, with final decisions formed through staking, challenges, and voting.

Features:

• Turns “truth determination” into a game-theoretic problem
• Relies on economic incentives rather than authority

This model is widely used across various on-chain prediction markets, especially for real-world events that are difficult to verify automatically.

Arbitration and Dispute Resolution: The Safety Valve of Prediction Markets

Even with clear event definitions and robust oracle designs, disputes are inevitable. That’s why a mature prediction market must have a built-in dispute resolution mechanism.

Why Is a Dispute Window Necessary?

Most prediction markets introduce a dispute window after results are submitted:

• During this period, anyone can challenge the reported outcome
• Challenges typically require staking tokens
• If the challenge succeeds, the challenger is rewarded; if it fails, the staked tokens are lost

This design fundamentally uses economic costs to filter out baseless disputes and economic incentives to encourage genuine error correction.

The Economic Logic Behind Arbitration

Prediction markets don’t strive for “absolute truth”; they aim for “error correction costs that exceed malicious gains.” As long as the cost of manipulating results outweighs the potential rewards, the system remains economically secure.

This is also why prediction markets closely resemble governance mechanisms—both are consensus systems driven by game theory.

Settlement Mechanisms and Edge Case Handling

Once the event outcome is finalized, the system moves into the settlement phase. While this seems straightforward, it often involves handling numerous edge cases.

Automatic Settlement vs Manual Confirmation

• Automatic settlement: Suitable for price-based or on-chain data events, relying on deterministic data sources.
• Manual confirmation: Necessary for real-world events, requiring arbitration or community consensus.

Different types of events often require distinct settlement pathways.

Handling Invalid and Failed Events

Mature prediction markets typically account for special states such as:

• Event cancellation
• Data source failure
• Indeterminate outcomes

In these scenarios, the most common solution is proportional fund returns or refunds to original sources to prevent systemic trust crises.

Technical Trade-offs and Future Evolution of On-chain Prediction Markets

There is no “perfect architecture” for prediction markets—only ongoing engineering trade-offs.

Decentralization vs User Experience

• Highly decentralized: Secure but complex
• Moderately centralized: Efficient but increases trust costs

Different platforms will make different choices based on their target users.

Impact of Layer 2 and Modular Architectures

As Layer 2 costs decrease, prediction markets can:

• Create more low-cost, long-tail events
• Shorten settlement and dispute cycles
• Boost overall trading frequency

Potential Impact of ZK and AI

In the future, prediction markets may integrate:

• ZK technology: Enabling privacy-focused predictions and institutional-grade participation
• AI models: Assisting with event definition, anomaly detection, and market monitoring

Prediction markets could become a key intersection of AI, finance, and social signals.