Prediction markets are incentive machines. They reward certain behaviors (honest information, fast reaction, arbitrage) and punish others (wishful thinking, slow updates). Game theory helps explain why the mechanism can work and where it breaks.
Incentives That Improve Accuracy
Key incentive effects:
- Informed traders profit by correcting mispricing.
- Arbitrage links related markets and reduces contradictions.
- Traders have a reason to reveal information through trading.
This is why markets can be better than committees or polls in some contexts.
Strategic Behavior
Traders can act strategically, not just honestly:
- Bluffing: trying to move price to trigger others.
- Liquidity hunting: exploiting thin books or AMM slippage.
- Timing games: waiting to trade until the moment of maximum impact.
- Reputation games: using market odds as propaganda.
Strategic behavior does not always reduce accuracy, but it changes short-term dynamics.
Mechanism Design Problems
A market’s rules determine incentives:
- Fee design can discourage small trades or high-frequency strategies.
- Position limits can reduce manipulation but also reduce information flow.
- Settlement rules create incentives to dispute, especially in ambiguous markets.
What Designers Try to Achieve
Good mechanism design aims for:
- Truthful revelation of information
- Low cost of correcting mispricing
- Clear resolution and minimal dispute surface
- Resistance to manipulation in thin markets
Key Takeaways
- Prediction markets work because incentives align money with accuracy.
- Strategic behavior can distort short-term signals.
- Rules, fees, and settlement design are game-theoretic choices.