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In the setting where information cannot be verified, we propose a simple yetpowerful information theoretical framework---the Mutual InformationParadigm---for information elicitation mechanisms. Our framework pays everyagent a measure of mutual information between her signal and a peer's signal.We require that the mutual information measurement has the key property thatany "data processing" on the two random variables will decrease the mutualinformation between them. We identify such information measures that generalizeShannon mutual information.  Our Mutual Information Paradigm overcomes the two main challenges ininformation elicitation without verification: (1) how to incentivize effort andavoid agents colluding to report random or identical responses (2) how tomotivate agents who believe they are in the minority to report truthfully.  Aided by the information measures we found, (1) we use the paradigm to designa family of novel mechanisms where truth-telling is a dominant strategy and anyother strategy will decrease every agent's expected payment (in themulti-question, detail free, minimal setting where the number of questions islarge); (2) we show the versatility of our framework by providing a unifiedtheoretical understanding of existing mechanisms---Peer Prediction [Miller2005], Bayesian Truth Serum [Prelec 2004], and Dasgupta and Ghosh [2013]---bymapping them into our framework such that theoretical results of those existingmechanisms can be reconstructed easily.  We also give an impossibility result which illustrates, in a certain sense,the the optimality of our framework.

An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling