Trick, Susanne ; Rothkopf, Constantin A. ; Jäkel, Frank (2023)
A Normative Model for Bayesian Combination of Subjective Probability Estimates.
In: Judgment and Decision Making, 2023 (18)
doi: 10.1017/jdm.2023.39
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Trick, Susanne ; Rothkopf, Constantin A. ; Jäkel, Frank |
| Art des Eintrags: | Bibliographie |
| Titel: | A Normative Model for Bayesian Combination of Subjective Probability Estimates |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Cambridge |
| Verlag: | Cambridge University Press |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Judgment and Decision Making |
| Jahrgang/Volume einer Zeitschrift: | 2023 |
| (Heft-)Nummer: | 18 |
| DOI: | 10.1017/jdm.2023.39 |
| URL / URN: | https://www.cambridge.org/core/journals/judgment-and-decisio... |
| Kurzbeschreibung (Abstract): | Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error. |
| Freie Schlagworte: | forecast aggregation, normative model, Bayesian inference, calibration, confidence, Projekt IKIDA 01IS20045 |
| Zusätzliche Informationen: | Article number: e40 |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Psychologie der Informationsverarbeitung Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 27 Nov 2023 13:34 |
| Letzte Änderung: | 20 Feb 2024 08:50 |
| PPN: | 513497455 |
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| Suche nach Titel in: | TUfind oder in Google |
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