What is it about?
The paper introduces a new way to combine multiple opinions, which are expressed as probabilities, about an event or fact. It builds on an existing device called the 'extremized-mean' that effectively boosts the consensus view when lots of people hold it. Specifically, we introduce a 'skew-adjustment' informed by accumulations of non-consensus opinions - which may reflect the views of domain experts.
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Why is it important?
The work contributes to a growing literature on crowd-wisdom, whose relevance increases as large online communities gather and share opinions. Our paper considers how someone outside such a community might best discern an 'optimal' combination of opinions from within it.
Read the Original
This page is a summary of: Skew-adjusted extremized-mean: A simple method for identifying and
learning from contrarian minorities in groups of forecasters., Decision, July 2022, American Psychological Association (APA), DOI: 10.1037/dec0000191.
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