  In the setting where information cannot be verified, we propose a simple yet
powerful information theoretical framework---the Mutual Information
Paradigm---for information elicitation mechanisms. Our framework pays every
agent a measure of mutual information between her signal and a peer's signal.
We require that the mutual information measurement has the key property that
any "data processing" on the two random variables will decrease the mutual
information between them. We identify such information measures that generalize
Shannon mutual information.
  Our Mutual Information Paradigm overcomes the two main challenges in
information elicitation without verification: (1) how to incentivize effort and
avoid agents colluding to report random or identical responses (2) how to
motivate agents who believe they are in the minority to report truthfully.
  Aided by the information measures we found, (1) we use the paradigm to design
a family of novel mechanisms where truth-telling is a dominant strategy and any
other strategy will decrease every agent's expected payment (in the
multi-question, detail free, minimal setting where the number of questions is
large); (2) we show the versatility of our framework by providing a unified
theoretical understanding of existing mechanisms---Peer Prediction [Miller
2005], Bayesian Truth Serum [Prelec 2004], and Dasgupta and Ghosh [2013]---by
mapping them into our framework such that theoretical results of those existing
mechanisms can be reconstructed easily.
  We also give an impossibility result which illustrates, in a certain sense,
the the optimality of our framework.
