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Improving the wisdom of crowds with analysis of variance of predictions of related outcomes
- Source :
- International Journal of Forecasting. 37:1728-1747
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Decision-makers often collect and aggregate experts’ point predictions about continuous outcomes, such as stock returns or product sales. In this article, we model experts as Bayesian agents and show that means, including the (weighted) arithmetic mean, trimmed means, median, geometric mean, and essentially all other measures of central tendency, do not use all information in the predictions. Intuitively, they assume idiosyncratic differences to arise from error instead of private information and hence do not update the prior with all available information. Updating means in terms of unused information improves their expected accuracy but depends on the experts’ prior and information structure that cannot be estimated based on a single prediction per expert. In many applications, however, experts consider multiple stocks, products, or other related items at the same time. For such contexts, we introduce ANOVA updating – an unsupervised technique that updates means based on experts’ predictions of multiple outcomes from a common population. The technique is illustrated on several real-world datasets.
- Subjects :
- education.field_of_study
Central tendency
Computer science
05 social sciences
Bayesian probability
Population
Information structure
Aggregate (data warehouse)
Bayesian inference
0502 economics and business
Econometrics
050207 economics
Business and International Management
Geometric mean
education
Private information retrieval
050205 econometrics
Arithmetic mean
Subjects
Details
- ISSN :
- 01692070
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- International Journal of Forecasting
- Accession number :
- edsair.doi.dedup.....ac8565e4484978b68c8eee5aac73c1d3
- Full Text :
- https://doi.org/10.1016/j.ijforecast.2021.03.011