Mechanism design, incentives, and getting users to care
It’s easy to create a prediction market. You just need a question, some way for people to buy “yes” or “no” shares, and a system for resolving the outcome.
But building a useful prediction market—one that attracts participants, generates meaningful signals, and avoids manipulation—is much harder.
This post breaks down what makes prediction markets succeed or fail. Whether you’re building one, using one, or embedding one into your product, this guide is for you.
1. Ask the Right Questions
A market is only as good as the question it’s trying to answer.
Good market questions are:
Clear: No ambiguity about what "yes" or "no" means.
Time-bound: There’s a definite resolution date.
Resolvable: The outcome can be judged objectively.
Relevant: People actually care about the outcome, or can act on it.
Bad market questions are:
Too subjective (“Will this proposal be good?”)
Too vague (“Will the world improve in 2025?”)
Unresolvable (“Will aliens exist?” without defining what counts)
Clarity isn’t just about fairness. It’s essential for attracting serious participation.
2. Design Incentives That Reward Useful Input
Prediction markets are powered by incentives. People trade because they want to profit (or build reputation), and that pressure encourages truth-seeking behavior.
But incentives must be carefully tuned.
Too weak: People don't bother participating.
Too strong: The market can be gamed or dominated by whales.
Too centralized: It loses trust.
Too open-ended: It fills with spam or joke markets.
Successful systems often:
Offer small creator bounties (e.g. Manifold’s creator bonus)
Run forecasting competitions with prize pools
Use play-money points to reduce regulatory friction
Create social status rewards (leaderboards, badges, prediction history)
The goal is to align incentives with signal generation, not just raw volume.
3. Choose the Right Market Mechanism
How are prices set in your market? That depends on the mechanism you use.
Here are the most common ones:
1. CLOB (Central Limit Order Book)
Used in traditional finance (e.g. stock exchanges)
Requires users to place specific bids and asks
High flexibility, but needs lots of liquidity and active users
2. AMM (Automated Market Maker)
Constant product or other formulas
Easy to bootstrap small markets
Prices adjust automatically as trades happen
3. LMSR (Logarithmic Market Scoring Rule)
Invented for prediction markets
Ensures continuous liquidity
Prices reflect belief-weighted consensus
Used by platforms like Gnosis, Omen, and Manifold (in modified form)
Each mechanism comes with trade-offs:
LMSR gives guaranteed prices but can feel artificial.
CLOB gives realistic market depth but needs active traders.
AMMs are easy to start with but can be gameable.
Your mechanism should fit your audience. For small, social prediction systems, LMSR variants usually work best.
4. Resolve Markets Reliably
You need a trusted way to decide who won.
Resolution methods include:
Manual judgment: Platform or moderator decides based on public evidence (fast, but trust-dependent)
External API or oracle: Data feed from a trusted source (good for elections, prices, sports)
Decentralized voting: Token holders or users vote on the outcome (resilient, but can be attacked or delayed)
Hybrid systems: Combine automatic resolution with manual override
If your resolution isn’t trusted, the market isn’t trusted. And once trust is lost, users won’t return.
5. Start With Liquidity, Then Decentralize
Empty markets die quickly.
Successful platforms often seed initial markets with liquidity or incentives, such as:
Subsidizing trades
Offering “free shares” to new users
Partnering with communities to create relevant markets
Once usage is stable, you can introduce more user-created markets, decentralize control, and expand features.
Open creation is powerful—but only if you also build moderation, spam filters, and clear guidelines.
6. Support Read-Only Users
Most visitors won’t trade. They’ll just look at charts.
And that’s okay.
Your market can serve as a public forecasting signal, even for passive users. To make it valuable:
Keep UI readable (yes/no probabilities, not raw share prices)
Highlight changes over time
Let users follow or subscribe to markets
Offer context or news links
Think of it not just as a trading platform, but as a new kind of information infrastructure.
7. Build for Community, Not Just Speculators
The best markets aren’t just accurate—they’re alive.
A good prediction market encourages:
Conversation: Let users comment, debate, and explain their trades
Reputation: Let accuracy and honesty accumulate over time
Discovery: Highlight interesting, trending, or surprising markets
If users feel like they’re contributing insight, not just placing bets, they’ll stick around.
Conclusion: Prediction Markets Are Products, Not Just Protocols
Building a working prediction market is not just an engineering problem. It's a product design challenge.
You’re not just building markets. You’re building:
Trust
Incentives
Social structures
User journeys
The best systems combine clean math with messy human behavior. And when it clicks, you get something special: a tool that helps communities understand the future—together.
