Hilla Shinitzky , Yhonatan Shemesh , David Leiser , Michael Gilead
{"title":"用 100 个大脑和一台计算机改进地缘政治预测","authors":"Hilla Shinitzky , Yhonatan Shemesh , David Leiser , Michael Gilead","doi":"10.1016/j.ijforecast.2023.08.004","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving geopolitical forecasts with 100 brains and one computer\",\"authors\":\"Hilla Shinitzky , Yhonatan Shemesh , David Leiser , Michael Gilead\",\"doi\":\"10.1016/j.ijforecast.2023.08.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":14061,\"journal\":{\"name\":\"International Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2023-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169207023000791\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169207023000791","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Improving geopolitical forecasts with 100 brains and one computer
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.
期刊介绍:
The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.