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Statistics vs. semantics: Project similarity bias and variance neglect in forecast metric evaluation

Mr. Shir Dekel
The University of Sydney ~ Psychology
Dr. Micah Goldwater
Prof. Dan Lovallo
Dr. Bruce Burns

People make all sorts of decisions based on quantitative forecasts. However, it is unclear how people use these kinds of metrics in the context of other, non-numerical, forms of information. Here we focus on resource allocation scenarios in large companies, wherein managers often have to allocate resources across very dissimilar projects. They use financial measures that simplify this difficult comparison because they aim to be equally applicable to any kind of project, but across domains, these measures vary in their reliability. Here we investigate the effects of project similarity, and forecast variance information. We found that participants accommodate their use of a financial forecast based on its reliability when allocating resources to a set of similar projects, but use reliability information less when allocating to a set of dissimilar projects. However, they only considered reliability when it was verbally communicated, not when it was expressed numerically. When expressed numerically, people made no use of the information about the variance in the forecasts. These findings show that the use of quantitative forecasts changes based on non-numerical information, despite the motivation of developing those metrics to apply across semantic contexts. In addition, people tend to ignore the variance information in their forecasts.



decision making
resource allocation
variance neglect
similarity bias
structural alignment
statistical reasoning

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Cite this as:

Dekel, S., Goldwater, M., Lovallo, D., & Burns, B. (2021, February). Statistics vs. semantics: Project similarity bias and variance neglect in forecast metric evaluation. Paper presented at Australasian Mathematical Psychology Conference 2021. Via