Add to ORKGWe consider the design of proper scoring rules, equivalently proper losses, when the goal is to elicit some function, known as a property, of the underlying distribution. We provide a full characterization of the class of proper scoring rules when the property is linear as a function of the input distribution. A key conclusion is that any such scoring rule can be written in the form of a Bregman divergence for some convex function. We also apply our results to the design of prediction market mechanisms, showing a strong equivalence between scoring rules for linear properties and automated prediction market makers. 1
For some applications, prediction markets that rely entirely on voluntary transactions between indiv...
We give a new example for a proper scoring rule motivated by the form of Anderson-Darling distance o...
Strictly proper scoring rules continue to play an important role in probability assessment. Although...
In this paper, we introduce a novel objective prior distribution levering on the connections between...
Proper and strictly proper scoring rules provide a rigorous method for evaluating the accuracy of a ...
If a decision maker whose behavior conforms to the max-min expected utility model is faced with a sc...
Most of the methods nowadays employed in forecast problems are based on scoring rules. There is a di...
A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When d...
Ascoring rule S(x; q) provides away of judging the quality of a quoted probability density q for a r...
Strictly proper scoring rules, including the Brier score and the logarithmic score, are standard met...
If a decision maker whose behavior conforms to the max-min expected utility model (Gilboa and Schmei...
A scoring rule is a loss function measuring the quality of a quoted probability distribution $Q$ for...
Scoring rules are an important tool for evaluating the performance of probabilistic forecasting sche...
Scoring rules promote rational and good decision making and predictions by models, this is increasin...
For some applications, prediction markets that rely entirely on voluntary transactions between indiv...
We give a new example for a proper scoring rule motivated by the form of Anderson-Darling distance o...
Strictly proper scoring rules continue to play an important role in probability assessment. Although...
In this paper, we introduce a novel objective prior distribution levering on the connections between...
Proper and strictly proper scoring rules provide a rigorous method for evaluating the accuracy of a ...
If a decision maker whose behavior conforms to the max-min expected utility model is faced with a sc...
Most of the methods nowadays employed in forecast problems are based on scoring rules. There is a di...
A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When d...
Ascoring rule S(x; q) provides away of judging the quality of a quoted probability density q for a r...
Strictly proper scoring rules, including the Brier score and the logarithmic score, are standard met...
If a decision maker whose behavior conforms to the max-min expected utility model (Gilboa and Schmei...
A scoring rule is a loss function measuring the quality of a quoted probability distribution $Q$ for...
Scoring rules are an important tool for evaluating the performance of probabilistic forecasting sche...
Scoring rules promote rational and good decision making and predictions by models, this is increasin...
For some applications, prediction markets that rely entirely on voluntary transactions between indiv...
We give a new example for a proper scoring rule motivated by the form of Anderson-Darling distance o...
Strictly proper scoring rules continue to play an important role in probability assessment. Although...