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Eliciting properties of probability distributions: the highlights
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Eliciting truthful answers to multiple-choice questions
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A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
Automated market-making in the large: the gates hillman prediction market
Proceedings of the 11th ACM conference on Electronic commerce
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Information elicitation for decision making
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ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
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We investigate the problem of truthfully eliciting an expert's assessment of a property of a probability distribution, where a property is any real-valued function of the distribution such as mean or variance. We show that not all properties are elicitable; for example, the mean is elicitable and the variance is not. For those that are elicitable, we provide a representation theorem characterizing all payment (or "score") functions that induce truthful revelation. We also consider the elicitation of sets of properties. We then observe that properties can always be inferred from sets of elicitable properties. This naturally suggests the concept of elicitation complexity; the elicitation complexity of property is the minimal size of such a set implying the property. Finally we discuss applications to prediction markets.