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Human and Algorithmic Predictions in Geopolitical Forecasting: Quantifying Uncertainty in Hard-to-Quantify Domains.
Mellers, Barbara A; McCoy, John P; Lu, Louise; Tetlock, Philip E.
Afiliación
  • Mellers BA; Department of Marketing, University of Pennsylvania.
  • McCoy JP; Department of Marketing, University of Pennsylvania.
  • Lu L; Department of Marketing, Stanford Business School, Stanford University.
  • Tetlock PE; Management Department of Wharton Business School, University of Pennsylvania.
Perspect Psychol Sci ; : 17456916231185339, 2023 Aug 29.
Article en En | MEDLINE | ID: mdl-37642169
Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon-nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Perspect Psychol Sci Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Perspect Psychol Sci Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos