IDEAS home Printed from https://ideas.repec.org/r/inm/ordeca/v11y2014i2p133-145.html

Two Reasons to Make Aggregated Probability Forecasts More Extreme

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. David R. Mandel & Daniel Irwin, 2021. "Tracking accuracy of strategic intelligence forecasts: Findings from a long‐term Canadian study," Futures & Foresight Science, John Wiley & Sons, vol. 3(3-4), September.
  2. Don A. Moore & Samuel A. Swift & Angela Minster & Barbara Mellers & Lyle Ungar & Philip Tetlock & Heather H. J. Yang & Elizabeth R. Tenney, 2017. "Confidence Calibration in a Multiyear Geopolitical Forecasting Competition," Management Science, INFORMS, vol. 63(11), pages 3552-3565, November.
  3. Tao Lin & Yiling Chen, 2022. "Sample Complexity of Forecast Aggregation," Papers 2207.13126, arXiv.org, revised Oct 2023.
  4. Marcellin Martinie & Tom Wilkening & Piers D L Howe, 2020. "Using meta-predictions to identify experts in the crowd when past performance is unknown," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-11, April.
  5. Ying Han & David V. Budescu, 2022. "Recalibrating probabilistic forecasts to improve their accuracy," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(1), pages 91-123, January.
  6. Andrew Gelman & Jessica Hullman & Christopher Wlezien & George Elliott Morris, 2020. "Information, incentives, and goals in election forecasts," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 863-880, September.
  7. 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.
  8. 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.
  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. Spyros Galanis & Sergei Mikhalishchev, 2024. "Information Aggregation with Costly Information Acquisition," Papers 2406.07186, arXiv.org, revised Apr 2026.
  11. David R. Mandel & Christopher W. Karvetski & Mandeep K. Dhami, 2018. "Boosting intelligence analysts’ judgment accuracy: What works, what fails?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(6), pages 607-621, November.
  12. Pavel Atanasov & Phillip Rescober & Eric Stone & Samuel A. Swift & Emile Servan-Schreiber & Philip Tetlock & Lyle Ungar & Barbara Mellers, 2017. "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls," Management Science, INFORMS, vol. 63(3), pages 691-706, March.
  13. 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.
  14. Gunnar P. Epping & Andrew Caplin & Erik Duhaime & William R. Holmes & Daniel Martin & Jennifer S. Trueblood, 2026. "Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment," Papers 2603.11511, arXiv.org.
  15. 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.
  16. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.
  17. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
  18. Asa B. Palley & Jack B. Soll, 2019. "Extracting the Wisdom of Crowds When Information Is Shared," Management Science, INFORMS, vol. 67(5), pages 2291-2309, May.
  19. 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.
  20. 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.
  21. Hanea, A.M. & McBride, M.F. & Burgman, M.A. & Wintle, B.C. & Fidler, F. & Flander, L. & Twardy, C.R. & Manning, B. & Mascaro, S., 2017. "I nvestigate D iscuss E stimate A ggregate for structured expert judgement," International Journal of Forecasting, Elsevier, vol. 33(1), pages 267-279.
  22. Karimi Motahhar, Vahid & Gruca, Thomas S., 2025. "How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts," International Journal of Forecasting, Elsevier, vol. 41(2), pages 487-498.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. Robert Mislavsky & Celia Gaertig, 2022. "Combining Probability Forecasts: 60% and 60% Is 60%, but Likely and Likely Is Very Likely," Management Science, INFORMS, vol. 68(1), pages 541-563, January.
  30. Joshua D. Kertzer, 2017. "Microfoundations in international relations," Conflict Management and Peace Science, Peace Science Society (International), vol. 34(1), pages 81-97, January.
  31. Michael D. Lee & Irina Danileiko, 2014. "Using cognitive models to combine probability estimates," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(3), pages 259-273, May.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.