The principal–agent problem appears when the person responsible for taking action has different incentives than the person relying on those actions. In prediction markets, this issue arises when someone with influence over an outcome is also able to trade on it. Their decisions may serve their personal interests rather than the interests of those depending on accurate forecasting.
On platforms like Polymarket, Kalshi, Myriad, and Manifold, principal–agent issues occur in markets tied to governance decisions, organizational votes, or protocol changes. An agent—such as a DAO delegate, policymaker, team lead, or insider—may have private incentives that don’t align with what the principal (the broader community, tokenholders, or decision-makers) wants. These conflicts show up in prediction markets data as probability shifts that reflect strategic behavior rather than unbiased expectations.
Recognizing principal–agent dynamics helps analysts understand when markets are forecasting genuine outcomes versus anticipating strategic, incentive-driven actions.
Principal–agent problems can reduce forecast quality and distort prediction markets data, especially when insiders or influential actors can change the outcome directly.
It arises when the person making decisions about an outcome also trades on the market tied to that outcome. Their private incentives may guide their behavior more than the principal’s goals or the event’s true probability. This introduces strategic distortions into prediction markets data.
Markets may price outcomes based on expected strategic moves rather than real-world likelihoods. Probabilities can shift suddenly because insiders act in their own interest, not because of new external information. This weakens the predictive value of prediction markets data and makes interpretation more complex.
Analysts can identify when influential actors have incentives that diverge from the broader group. They can recognize patterns where market movements anticipate strategic decisions rather than factual updates. This awareness helps analysts interpret prediction markets data with more precision and caution.
A DAO market on Myriad predicts whether a funding proposal will pass. A major delegate, who also trades actively, buys shares favoring rejection before signaling their intention to vote against the proposal. The market reacts, revealing a principal–agent conflict influencing the forecast.
Understanding principal–agent behavior requires detailed, time-aligned market data. FinFeed's Prediction Markets API provides structured prediction markets data that analysts can use to spot incentive-driven distortions and evaluate how insider actions influence forecasts.
