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Aggregating predictions from experts: A review of statistical methods, experiments, and applications

Advanced Review

Nicholas G. Reich, Graham C. Gibson, Nutcha Wattanachit, Thomas McAndrew

Published Online: Jun 16 2020

DOI: 10.1002/wics.1514

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Abstract Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts—models that combine expert‐generated predictions into a single forecast—can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert‐elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

The cumulative proportion (a) and individual number (b) of articles published per year. The earliest analysis‐set article was published in 1992 and most recent in 2018. A sharp increase in publication occurred at or near 2010

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A consort diagram that describes the path from collected to analysis‐set article. The search term used to collect the initial set of articles is reported and all intermediate steps between initial and analysis‐set articles

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Complementary cumulative distribution of the total number of forecasts made per article (a), and the proportion of articles eliciting less than 10, 100, 10³, 10⁴, and 10⁵ forecasts. Articles collecting more than 10⁴ forecasts were simulations

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The complementary cumulative distribution (CCDF) of the number of experts elicited per article (a). The proportion of articles enrolling less than 10, less than 100, less than 10³, and less than 10⁴ expert forecasters (b). The small number of articles enrolling more than 10³ were simulation studies

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The annual proportion of the top 12 most prevalent word stems among all abstract text. Note: The words probability and probabilities were stemmed to probabl). For each year, word w frequency was divided by the frequency of all words present in all abstracts

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The top 5% most frequent words used in all analysis‐set abstracts. Expert, forecast, and judgment are the most frequent and likely related to the search words used to collect these articles

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