Published: Apr 26, 2023
Academic article
  • Academic article

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

Long‐range subjective‐probability forecasts of slow-motion variables in world politics: Exploring limits on expert judgment
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.
Philip E. Tetlock*,1,2, Christopher Karvetski3, Ville A. Satopää4, Kevin Chen1 ,
1 University of Pennsylvania
2 Forecasting Research Institute
3 Good Judgment Inc
4 INSEAD
* Corresponding author. Contact: tetlock@wharton.upenn.edu
Published: Apr 26, 2023
Philip E. Tetlock*,1,2, Christopher Karvetski3, Ville A. Satopää4, Kevin Chen1

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.

1 University of Pennsylvania
2 Forecasting Research Institute
3 Good Judgment Inc
4 INSEAD
* Corresponding author. Contact: tetlock@wharton.upenn.edu
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