Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386885243> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W4386885243 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." @default.
- W4386885243 created "2023-09-21" @default.
- W4386885243 creator A5010774023 @default.
- W4386885243 creator A5048733472 @default.
- W4386885243 creator A5076930431 @default.
- W4386885243 creator A5079662533 @default.
- W4386885243 date "2023-09-01" @default.
- W4386885243 modified "2023-10-01" @default.
- W4386885243 title "Improving geopolitical forecasts with 100 brains and one computer" @default.
- W4386885243 cites W1216568 @default.
- W4386885243 cites W1969404663 @default.
- W4386885243 cites W1969794118 @default.
- W4386885243 cites W2007423920 @default.
- W4386885243 cites W2012259509 @default.
- W4386885243 cites W2054047387 @default.
- W4386885243 cites W2070493638 @default.
- W4386885243 cites W2073241381 @default.
- W4386885243 cites W2155653793 @default.
- W4386885243 cites W2239326923 @default.
- W4386885243 cites W2580299352 @default.
- W4386885243 cites W2600746833 @default.
- W4386885243 cites W2908201961 @default.
- W4386885243 cites W2963082927 @default.
- W4386885243 cites W2963305465 @default.
- W4386885243 cites W2963929932 @default.
- W4386885243 cites W2965169245 @default.
- W4386885243 cites W2970376406 @default.
- W4386885243 cites W2984028244 @default.
- W4386885243 cites W3018833570 @default.
- W4386885243 cites W3034448674 @default.
- W4386885243 cites W3163040772 @default.
- W4386885243 cites W3210620004 @default.
- W4386885243 cites W4224566676 @default.
- W4386885243 cites W4236213745 @default.
- W4386885243 cites W4313425124 @default.
- W4386885243 cites W4313429089 @default.
- W4386885243 doi "https://doi.org/10.1016/j.ijforecast.2023.08.004" @default.
- W4386885243 hasPublicationYear "2023" @default.
- W4386885243 type Work @default.
- W4386885243 citedByCount "0" @default.
- W4386885243 crossrefType "journal-article" @default.
- W4386885243 hasAuthorship W4386885243A5010774023 @default.
- W4386885243 hasAuthorship W4386885243A5048733472 @default.
- W4386885243 hasAuthorship W4386885243A5076930431 @default.
- W4386885243 hasAuthorship W4386885243A5079662533 @default.
- W4386885243 hasConcept C105409693 @default.
- W4386885243 hasConcept C119857082 @default.
- W4386885243 hasConcept C127413603 @default.
- W4386885243 hasConcept C149782125 @default.
- W4386885243 hasConcept C154945302 @default.
- W4386885243 hasConcept C162324750 @default.
- W4386885243 hasConcept C17744445 @default.
- W4386885243 hasConcept C18762648 @default.
- W4386885243 hasConcept C199539241 @default.
- W4386885243 hasConcept C201960208 @default.
- W4386885243 hasConcept C2777582232 @default.
- W4386885243 hasConcept C41008148 @default.
- W4386885243 hasConcept C78519656 @default.
- W4386885243 hasConcept C94625758 @default.
- W4386885243 hasConceptScore W4386885243C105409693 @default.
- W4386885243 hasConceptScore W4386885243C119857082 @default.
- W4386885243 hasConceptScore W4386885243C127413603 @default.
- W4386885243 hasConceptScore W4386885243C149782125 @default.
- W4386885243 hasConceptScore W4386885243C154945302 @default.
- W4386885243 hasConceptScore W4386885243C162324750 @default.
- W4386885243 hasConceptScore W4386885243C17744445 @default.
- W4386885243 hasConceptScore W4386885243C18762648 @default.
- W4386885243 hasConceptScore W4386885243C199539241 @default.
- W4386885243 hasConceptScore W4386885243C201960208 @default.
- W4386885243 hasConceptScore W4386885243C2777582232 @default.
- W4386885243 hasConceptScore W4386885243C41008148 @default.
- W4386885243 hasConceptScore W4386885243C78519656 @default.
- W4386885243 hasConceptScore W4386885243C94625758 @default.
- W4386885243 hasFunder F4320322252 @default.
- W4386885243 hasFunder F4320323051 @default.
- W4386885243 hasLocation W43868852431 @default.
- W4386885243 hasOpenAccess W4386885243 @default.
- W4386885243 hasPrimaryLocation W43868852431 @default.
- W4386885243 hasRelatedWork W2298661587 @default.
- W4386885243 hasRelatedWork W2417154619 @default.
- W4386885243 hasRelatedWork W2587255375 @default.
- W4386885243 hasRelatedWork W2893796098 @default.
- W4386885243 hasRelatedWork W2961085424 @default.
- W4386885243 hasRelatedWork W3024570948 @default.
- W4386885243 hasRelatedWork W3177723589 @default.
- W4386885243 hasRelatedWork W4306674287 @default.
- W4386885243 hasRelatedWork W4312524061 @default.
- W4386885243 hasRelatedWork W4224009465 @default.
- W4386885243 isParatext "false" @default.
- W4386885243 isRetracted "false" @default.
- W4386885243 workType "article" @default.