Radanovic, GoranFaltings, Boi2016-02-092016-02-092016-02-09201510.1609/aaai.v29i1.9311https://infoscience.epfl.ch/handle/20.500.14299/123341The modern web critically depends on aggregation of information from self-interested agents, for example opinion polls, product ratings, or crowdsourcing. We consider a setting where multiple objects (questions, products, tasks) are evaluated by a group of agents. We first construct a minimal peer prediction mechanism that elicits honest evaluations from a homogeneous population of agents with different private beliefs. Second, we show that it is impossible to strictly elicit honest evaluations from a heterogeneous group of agents with different private beliefs. Nevertheless, we provide a modified version of a divergence-based Bayesian Truth Serum that incentivizes agents to report consistently, making truthful reporting a weak equilibrium of the mechanism.ml-aitext::conference output::conference proceedings::conference paper