Peerprediction

Peerprediction is a method used to incentivize honest reporting of private information. It is particularly relevant in situations where individuals have incentives to misrepresent their true beliefs or knowledge to gain an advantage. The core idea is to create a system where the accuracy of an individual's reported information is evaluated by comparing it to the reported information of their peers.

In a peerprediction system, participants are typically asked to report on an uncertain event or outcome. For example, they might be asked to predict the probability of a certain stock price increase or the outcome of a sporting event. After the event occurs, the predictions are revealed. Participants receive a reward or payoff based on how well their prediction aligns with the consensus of their peers, or with a predefined measure of accuracy. The key is that the reward function is designed such that reporting one's true belief is the optimal strategy, even if that belief is controversial or deviates from what others might say.

This mechanism can be applied in various fields, including crowdsourcing, forecasting markets, and recommender systems. By aligning individual incentives with the accuracy of their reported information, peerprediction aims to aggregate diverse private knowledge effectively and reduce strategic misreporting. The design of the reward function is crucial for the success of any peerprediction system, ensuring that honesty is indeed the best policy.