Evaluating Quantile Assessments
Operations Research
Scoring Rules and Decision Analysis Education
Decision Analysis
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Probability forecasters who are rewarded via a proper scoring rule may care not only about the score, but also about their performance relative to other forecasters. We model this type of preference and show that a competitive forecaster who wants to do better than another forecaster typically should report more extreme probabilities, exaggerating toward zero or one. We consider a competitive forecaster's best response to truthful reporting and also investigate equilibrium reporting functions in the case where another forecaster also cares about relative performance. We show how a decision maker can revise probabilities of an event after receiving reported probabilities from competitive forecasters and note that the strategy of exaggerating probabilities can make well-calibrated forecasters (and a decision maker who takes their reported probabilities at face value) appear to be overconfident. However, a decision maker who adjusts appropriately for the misrepresentation of probabilities by one or more forecasters can still be well calibrated. Finally, to try to overcome the forecasters' competitive instincts and induce cooperative behavior, we develop the notion of joint scoring rules based on business sharing and show that these scoring rules are strictly proper.