Combining multiple probability predictions using a simple logit model

Citations

Cited by:

1. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org, revised May 2025.
2. Siddarth Srinivasan & Ezra Karger & Michiel Bakker & Yiling Chen, 2025. "Tell Me Why: Incentivizing Explanations," Papers 2502.13410, arXiv.org.
3. Hassoun, Zane & MacKay, Niall & Powell, Ben, 2026. "Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change-point detection," International Journal of Forecasting, Elsevier, vol. 42(1), pages 112-125.
4. Jonathan Baron & Barbara A. Mellers & Philip E. Tetlock & Eric Stone & Lyle H. Ungar, 2014. "Two Reasons to Make Aggregated Probability Forecasts More Extreme," Decision Analysis, INFORMS, vol. 11(2), pages 133-145, June.
5. Ville A. Satopää & Robin Pemantle & Lyle H. Ungar, 2016. "Modeling Probability Forecasts via Information Diversity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1623-1633, October.
6. Eric Neyman & Tim Roughgarden, 2023. "From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation," Operations Research, INFORMS, vol. 71(6), pages 2175-2195, November.
7. Satopää, Ville A. & Salikhov, Marat & Tetlock, Philip E. & Mellers, Barbara, 2023. "Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model," International Journal of Forecasting, Elsevier, vol. 39(1), pages 470-485.
8. Ville A. Satopää & Marat Salikhov & Philip E. Tetlock & Barbara Mellers, 2021. "Bias, Information, Noise: The BIN Model of Forecasting," Management Science, INFORMS, vol. 67(12), pages 7599-7618, December.
9. Hanea, Anca & Wilkinson, David Peter & McBride, Marissa & Lyon, Aidan & van Ravenzwaaij, Don & Singleton Thorn, Felix & Gray, Charles T. & Mandel, David R. & Willcox, Aaron & Gould, Elliot, 2021. "Mathematically aggregating experts' predictions of possible futures," MetaArXiv rxmh7, Center for Open Science.
10. repec:osf:metaar:rxmh7_v1 is not listed on IDEAS
11. Satopää, Ville A., 2021. "Improving the wisdom of crowds with analysis of variance of predictions of related outcomes," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1728-1747.
12. Ville A. Satopää, 2022. "Regularized Aggregation of One-Off Probability Predictions," Operations Research, INFORMS, vol. 70(6), pages 3558-3580, November.
13. Schuler, Benedikt Alexander & Murmann, Johann Peter & Beisemann, Marie & Satopää, Ville, 2025. "Individual foresight: Concept, operationalization, and correlates," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1521-1538.
14. John McCoy & Drazen Prelec, 2024. "A Bayesian Hierarchical Model of Crowd Wisdom Based on Predicting Opinions of Others," Management Science, INFORMS, vol. 70(9), pages 5931-5948, September.
15. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
16. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Victor Richmond Jose & Robert L. Winkler, 2022. "Extremizing and Antiextremizing in Bayesian Ensembles of Binary-Event Forecasts," Operations Research, INFORMS, vol. 70(5), pages 2998-3014, September.
17. Jared A. Beekman & Ronald F. A. Woodaman & Dennis M. Buede, 2020. "A Review of Probabilistic Opinion Pooling Algorithms with Application to Insider Threat Detection," Decision Analysis, INFORMS, vol. 17(1), pages 39-55, March.
18. Atanasov, Pavel & Witkowski, Jens & Mellers, Barbara & Tetlock, Philip, 2025. "Crowd prediction systems: Markets, polls, and elite forecasters," International Journal of Forecasting, Elsevier, vol. 41(2), pages 580-595.
19. Jens Witkowski & Rupert Freeman & Jennifer Wortman Vaughan & David M. Pennock & Andreas Krause, 2023. "Incentive-Compatible Forecasting Competitions," Management Science, INFORMS, vol. 69(3), pages 1354-1374, March.
20. Jason Dana & Pavel Atanasov & Philip Tetlock & Barbara Mellers, 2019. "Are markets more accurate than polls? The surprising informational value of “just askingâ€," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(2), pages 135-147, March.
21. Tao Lin & Yiling Chen, 2022. "Sample Complexity of Forecast Aggregation," Papers 2207.13126, arXiv.org, revised Oct 2023.
22. Edgar C. Merkle & Robert Hartman, 2018. "Weighted Brier score decompositions for topically heterogenous forecasting tournaments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(2), pages 185-201, March.
23. Peker, Cem & Wilkening, Tom, 2025. "Robust recalibration of aggregate probability forecasts using meta-beliefs," International Journal of Forecasting, Elsevier, vol. 41(2), pages 613-630.
24. Aurélien Baillon & Benjamin Tereick & Tong V. Wang, 2025. "Follow the money, not the majority: A mechanism predicting unresolvable events," Journal of Risk and Uncertainty, Springer, vol. 71(2), pages 111-137, October.
25. Karvetski, Christopher W. & Meinel, Carolyn & Maxwell, Daniel T. & Lu, Yunzi & Mellers, Barbara A. & Tetlock, Philip E., 2022. "What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 688-704.
26. Onesun Steve Yoo & Dongyuan Zhan, 2023. "Economic Behavior of Information Acquisition: Impact on Peer Grading in Massive Open Online Courses," Operations Research, INFORMS, vol. 71(4), pages 1277-1297, July.
27. Ying Han & David Budescu, 2019. "A universal method for evaluating the quality of aggregators," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 395-411, July.
28. Yakov Babichenko & Dan Garber, 2021. "Learning Optimal Forecast Aggregation in Partial Evidence Environments," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 628-641, May.