Improving geopolitical forecasts with 100 brains and one computer

Hilla Shinitzky, Yhonatan Shemesh, David Leiser, Michael Gilead

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The ability to accurately predict future events is critical in numerous areas of human life. Past research has shown that human reasoning can usefully predict geopolitical outcomes, but such forecasts are still far from perfect. In the current work, we investigate whether machine learning can help predict whether people's forecasts are likely to be correct. We rely on data from a geopolitical forecasting contest where participants provided a total of 1530 predictions accompanied by written rationales. We extracted various features (e.g., forecasters’ psychological traits, the linguistic aspects of the rationales, and peer evaluations), trained a machine learning model to predict the accuracy of prediction, and validated it on held-out data. The results showed that the model was able to predict the accuracy of a prediction with excellent accuracy. A theoretical simulation shows that aggregating predictions based on the output of our prediction model can yield highly accurate forecasts. We conclude that combining human intelligence with machine learning algorithms can make the future more predictable.

Original languageEnglish
JournalInternational Journal of Forecasting
DOIs
StateAccepted/In press - 1 Jan 2023

Keywords

  • Aggregation
  • Forecasting
  • Judgment
  • Language
  • Machine learning

ASJC Scopus subject areas

  • Business and International Management

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