Skew-Adjusted Extremized-Mean: A Simple Method for Identifying and Learning From Contrarian Minorities in Groups of Forecasters

Ben Powell, Ville Satopaa, Niall MacKay, Philip Tetlock

Research output: Contribution to journalArticlepeer-review


Recent work in forecast aggregation has demonstrated that paying attention to contrarian minorities among larger groups of forecasters can improve aggregated probabilistic forecasts. In those papers, the minorities are identified using `meta-questions' that ask forecasters about their forecasting abilities or those of others. In the current paper, we explain how contrarian minorities can be identified without the meta-questions by inspecting the skewness of the distribution of the forecasts. Inspired by this observation, we introduce a new forecast aggregation tool called \textit{Skew-Adjusted Extremized-Mean} and demonstrate its superior predictive power on a large set of geopolitical and general knowledge forecasting data.
Original languageEnglish
Publication statusAccepted/In press - 11 Jun 2022


  • Forecasting
  • crowd-wisdom

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