Convex combinations in judgment aggregation

Johannes G. Jaspersen

European Journal of Operational Research, 2022, vol. 299, issue 2, 780-794

Abstract: Judgments are the basis for almost all decisions. They often come from different models and multiple experts. This information is typically aggregated using simple averages, which leads to the well-known shared information problem. A weighted average of the individual judgments based on empirically estimated sophisticated weights is commonly discarded in practice, because the sophisticated weights have large estimation errors. In this paper, I explore mixture weights, which are convex combinations of sophisticated and naïve weights. I show analytically that if the data generation process is stable, there always exists a mixture weight which aggregates judgments better than the naïve weights. I thus offer a path to alleviate the shared information problem. In contrast to other proposed solutions, it does not require any control over the judgment process. I demonstrate the utility of mixture weights in numerical analyses and in two empirical applications. I also offer heuristic selection algorithms for the correct mixture weight and analyze them in my numerical and empirical settings.

Keywords: Forecasting; Judgment aggregation; Wisdom of the crowd (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:299:y:2022:i:2:p:780-794

DOI: 10.1016/j.ejor.2021.09.050

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