Introduction

Prediction has always fascinated humanity.

In the past, people turned to fortune tellers or experts for them.

Today, technology has given us new tools: prediction and forecasting projects.

At first glance, many forecasting projects look similar; they all ask a question about the future and reward people based on the outcome. But beneath the surface, they are very different. Each project makes important technical choices about:

• How outcomes are decided,
• What kind of infrastructure powers the system, and
• How disputes or unclear results are resolved.

These choices matter. They determine whether a platform is fast or slow, centralized or decentralized, easy to use or complex, open to anyone or tightly regulated.

Understanding these technical choices is crucial not only for participants but also for regulators, developers, and investors deciding which models will endure. They also influence adoption, trust, and long-term sustainability.

This piece maps out the technical landscape of forecasting projects through three main lenses:

1. Outcome resolution — whether results are determined by oracles, human judgment, or automated feeds.
2. Infrastructure — fully on-chain, off-chain, or hybrid systems.
3. Other technical dimensions — such as market design and data sources.

From this lens, we build a typology of platforms, explain their trade-offs, and illustrate with real-world examples.

By the end, you’ll see why no single design is perfect and how each approach serves a different purpose in the world of forecasting.