  Suppose a decision maker wants to predict weather tomorrow by eliciting and
aggregating information from crowd. How can the decision maker incentivize the
crowds to report their information truthfully? Many truthful peer prediction
mechanisms have been proposed for homogeneous agents, whose types are drawn
from the same distribution. However, in many situations, the population is a
hybrid crowd of different types of agents with different forms of information,
and the decision maker has neither the identity of any individual nor the
proportion of each types of agents in the crowd. Ignoring the heterogeneity
among the agent may lead to inefficient of biased information, which would in
turn lead to suboptimal decisions. In this paper, we propose the first
framework for information elicitation from hybrid crowds, and two mechanisms to
motivate agents to report their information truthfully. The first mechanism
combines two mechanisms via linear transformations and the second is based on
mutual information. With two mechanisms, the decision maker can collect high
quality information from hybrid crowds, and learns the expertise of agents.
