Combining multiple probability predictions using a simple logit model

Citations

Cited by:

1. Tao Lin & Yiling Chen, 2022. "Sample Complexity of Forecast Aggregation," Papers 2207.13126, arXiv.org, revised Oct 2023.
2. repec:osf:metaar:rxmh7_v1 is not listed on IDEAS
3. 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.
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. 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.
6. 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.
7. 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.
8. Siddarth Srinivasan & Ezra Karger & Michiel Bakker & Yiling Chen, 2025. "Tell Me Why: Incentivizing Explanations," Papers 2502.13410, arXiv.org.
9. 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.
10. 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.
11. 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.
12. 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.
13. Ville A. Satopää, 2022. "Regularized Aggregation of One-Off Probability Predictions," Operations Research, INFORMS, vol. 70(6), pages 3558-3580, November.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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).
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. 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.