@inproceedings{82bb73ca84b4432d8b9f1f1a95e63180,
title = "A Cardinal Comparison of Experts",
abstract = "In various situations, decision makers face experts that may provide conflicting advice. This advice may be in the form of probabilistic forecasts over critical future events. We consider a setting where the two forecasters provide their advice repeatedly and ask whether the decision maker can learn to compare and rank the two forecasters based on past performance. We take an axiomatic approach and propose three natural axioms that a comparison test should comply with. We propose a test that complies with our axioms. Perhaps, not surprisingly, this test is closely related to the likelihood ratio of the two forecasts over the realized sequence of events. More surprisingly, this test is essentially unique. Furthermore, using results on the rate of convergence of supermartingales, we show that whenever the two experts{\textquoteright} advice are sufficiently distinct, the proposed test will detect the informed expert in any desired degree of precision in some fixed finite time.",
keywords = "Forecasting, Probability, Testing",
author = "Itay Kavaler and Rann Smorodinsky",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Web and Internet Economics, WINE 2020 ; Conference date: 07-12-2020 Through 11-12-2020",
year = "2020",
doi = "10.1007/978-3-030-64946-3_29",
language = "الإنجليزيّة",
isbn = "9783030649456",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "416--429",
editor = "Xujin Chen and Nikolai Gravin and Martin Hoefer and Ruta Mehta",
booktitle = "Web and Internet Economics - 16th International Conference, WINE 2020, Proceedings",
address = "ألمانيا",
}