Machine-Learning Aided Peer Prediction
Yang Liu, Yiling Chen · 2017
Information Elicitation without Verification (IEWV) is a classic problem where a principal wants to truthfully elicit high-quality answers of some tasks from strategic agents despite that she cannot evaluate the quality of agents' contributions. The established solution to this problem is a class of peer prediction mechanisms, where each agent is rewarded based on how his answers compare with those of his peer agents. These peer prediction mechanisms are designed by exploring the stochastic correlation of agents' answers. The prior distribution of agents' true answers is often assumed to be known to the principal or at least to the agents.