Science and technology prediction markets focus on uncertain outcomes in research, innovation timelines, and real-world tech milestones. They can help translate expert beliefs into probabilities and highlight where consensus is weak.
Typical Questions
Examples of markets in this category:
- Will a clinical trial meet its primary endpoint?
- Will a specific paper replicate successfully?
- Will a major model benchmark be surpassed by date Y?
- Will a spacecraft launch happen in a given window?
- Will a new regulation for AI pass this year?
Why They Are Interesting
These markets can help because:
- Experts disagree, and a market can quantify that disagreement
- A single probability is easier to act on than conflicting essays
- Markets can expose hidden assumptions by forcing traders to price timelines and feasibility
Hard Problems to Solve
Science and tech markets have specific obstacles:
- Resolution can be complex (what counts as a replication, which benchmark, what measurement?)
- Participation is thin unless you attract domain experts
- Information asymmetry is real, and in some contexts insider knowledge is ethically sensitive
- Some topics create perverse incentives (for example, betting on disasters or failures)
What Good Looks Like
A high-quality science or tech market usually has:
- Clear definitions and accepted sources for resolution
- A good mix of expert traders and motivated generalists
- Transparent updates when assumptions change (methods, datasets, timelines)
Key Takeaways
- These markets are most useful when outcomes are measurable and definitions are strict.
- Expert participation is the limiting factor.
- The signal is strongest when resolution is objective and incentives reward accuracy.