IDEAS home Printed from https://ideas.repec.org/a/wly/fufsci/v6y2024i1ne157.html

Long‐range subjective‐probability forecasts of slow‐motion variables in world politics: Exploring limits on expert judgment

Author

Listed:
  • Philip E. Tetlock
  • Christopher Karvetski
  • Ville A. Satopää
  • Kevin Chen

Abstract

Skeptics see long‐range geopolitical forecasting as quixotic. A more nuanced view is that although predictability tends to decline over time, its rate of descent is variable. The current study gives geopolitical forecasters a sporting chance by focusing on slow‐motion variables with low base rates of change. Analyses of 5, 10, and 25‐year cumulative‐risk judgments made in 1988 and 1997 revealed: (a) specialists beat generalists at predicting nuclear proliferation but not shifting nation‐state boundaries; (b) some counterfactual interventions—for example, Iran gets the bomb before 2022—boosted experts’ edge but others—for example, nuclear war before 2022—eliminated it; (c) accuracy fell faster on topics where expertise conferred no edge in shorter‐range forecasts. To accelerate scientific progress, we propose adversarial collaborations in which clashing schools of thought negotiate Bayesian reputational bets on divisive issues and use Lakatosian scorecards to incentivize the honoring of bets.

Suggested Citation

  • Philip E. Tetlock & Christopher Karvetski & Ville A. Satopää & Kevin Chen, 2024. "Long‐range subjective‐probability forecasts of slow‐motion variables in world politics: Exploring limits on expert judgment," Futures & Foresight Science, John Wiley & Sons, vol. 6(1), March.
    • Handle: RePEc:wly:fufsci:v:6:y:2024:i:1:n:e157
    DOI: 10.1002/ffo2.157
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ffo2.157
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ffo2.157?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Keynes, John Maynard, 1919. "The Economic Consequences of the Peace," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, number keynes1919.
    2. Anil Gaba & Dana G. Popescu & Zhi Chen, 2019. "Assessing Uncertainty from Point Forecasts," Management Science, INFORMS, vol. 65(1), pages 90-106, January.
    3. Alison Hubbard Ashton & Robert H. Ashton, 1985. "Aggregating Subjective Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 31(12), pages 1499-1508, December.
    4. Franz Dietrich, 2010. "Bayesian group belief," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 35(4), pages 595-626, October.
    5. Welton Chang & Eva Chen & Barbara Mellers & Philip Tetlock, 2016. "Developing expert political judgment: The impact of training and practice on judgmental accuracy in geopolitical forecasting tournaments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(5), pages 509-526, September.
    6. Carless, Travis S. & Redus, Kenneth & Dryden, Rachel, 2021. "Estimating nuclear proliferation and security risks in emerging markets using Bayesian Belief Networks," Energy Policy, Elsevier, vol. 159(C).
    7. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    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. Barbara A. Mellers & Joshua D. Baker & Eva Chen & David R. Mandel & Philip E. Tetlock, 2017. "How generalizable is good judgment? A multi-task, multi-benchmark study," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(4), pages 369-381, July.
    10. Bas, Muhammet A. & Coe, Andrew J., 2016. "A Dynamic Theory of Nuclear Proliferation and Preventive War," International Organization, Cambridge University Press, vol. 70(4), pages 655-685, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Christopher W. Karvetski & David R. Mandel & Daniel Irwin, 2020. "Improving Probability Judgment in Intelligence Analysis: From Structured Analysis to Statistical Aggregation," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 1040-1057, May.
    3. 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.
    4. Ross Gruetzemacher & Kang Bok Lee & David Paradice, 2024. "Calibration training for improving probabilistic judgments using an interactive app," Futures & Foresight Science, John Wiley & Sons, vol. 6(2), June.
    5. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    7. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    8. David R. Mandel & Daniel Irwin, 2021. "Facilitating sender-receiver agreement in communicated probabilities: Is it best to use words, numbers or both?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(2), pages 363-393, March.
    9. 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.
    10. Petropoulos, Fotios & Spiliotis, Evangelos, 2025. "Judgmental selection of parameters for simple forecasting models," European Journal of Operational Research, Elsevier, vol. 323(3), pages 780-794.
    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. 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.
    13. Saeed Hayati & Kenji Fukumizu & Afshin Parvardeh, 2024. "Kernel mean embedding of probability measures and its applications to functional data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 447-484, June.
    14. Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
    15. Luis A Barboza & Shu-Wei Chou-Chen & Paola Vásquez & Yury E García & Juan G Calvo & Hugo G Hidalgo & Fabio Sanchez, 2023. "Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 17(1), pages 1-13, January.
    16. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    17. 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.
    18. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    19. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    20. Jörg Bibow, 2018. "How Germany’s anti-Keynesianism has brought Europe to its knees," International Review of Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 569-588, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:fufsci:v:6:y:2024:i:1:n:e157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2573-5152 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.