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Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation
 Journal of Prediction Markets
, 2002
"... In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals. ..."
Abstract

Cited by 72 (5 self)
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In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals.
Eliciting Properties of Probability Distributions
 In Proceedings of the ninth ACM conference on electronic commerce
, 2008
"... We investigate the problem of incentivizing an expert to truthfully reveal probabilistic information about a random event. Probabilistic information consists of one or more properties, which are any realvalued functions of the distribution, such as the mean and variance. Not all properties can be e ..."
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Cited by 18 (4 self)
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We investigate the problem of incentivizing an expert to truthfully reveal probabilistic information about a random event. Probabilistic information consists of one or more properties, which are any realvalued functions of the distribution, such as the mean and variance. Not all properties can be elicited truthfully. We provide a simple characterization of elicitable properties, and describe the general form of the associated payment functions that induce truthful revelation. We then consider sets of properties, and observe that all properties can be inferred from sets of elicitable properties. This suggests the concept of elicitation complexity for a property, the size of the smallest set implying the property.
An initial implementation of the turing tournament to learning in two person games
 Games and Economic Behavior
, 2006
"... and of the National Science Foundation (Grant #SES0079301) for help in funding the experiments. We ..."
Abstract

Cited by 7 (0 self)
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and of the National Science Foundation (Grant #SES0079301) for help in funding the experiments. We
Eliciting Objective Probabilities via Lottery Insurance Games
 Computational Mathematics Laboratory, Rice University
, 1993
"... Since utilities and probabilities jointly determine choices, eventdependent utilities complicate the elicitation of subjective event probabilities. However, for the usual purpose of obtaining the information embodied in agent beliefs, it is su#cient to elicit objective probabilities, i.e., proba ..."
Abstract

Cited by 1 (1 self)
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Since utilities and probabilities jointly determine choices, eventdependent utilities complicate the elicitation of subjective event probabilities. However, for the usual purpose of obtaining the information embodied in agent beliefs, it is su#cient to elicit objective probabilities, i.e., probabilities obtained by updating a known common prior with that agent's further information. Bayesians who play a Nash equilibrium of a certain insurance game before they obtain relevant information will afterward act regarding lottery ticket payments as if they had eventindependent riskneutral utility and a known common prior. Proper scoring rules paid in lottery tickets can then elicit objective probabilities.
[Extended Abstract]
"... We investigate the problem of truthfully eliciting an expert’s assessment of a property of a probability distribution, where a property is any realvalued function of the distribution such as mean or variance. We show that not all properties are elicitable; for example, the mean is elicitable and th ..."
Abstract
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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 realvalued 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.