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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-09-20
摘要
Predicting a criterion that is probabilistically related to pieces of 資訊, or cues, is a paradigmatic judgment task that has been investigated both, in research trying to identify the individual judgment and decision making strategies people use, and in the wisdom-of-crowds literature where the focus is on how aggregation can improve accuracy. I combine these two lines of research to investigate how the performance of individual and aggregated linear strategies are affected by different environmental and group aggregation factors and how performance differences between individual and aggregated linear strategies can be understood in a unified framework. I show that constrained linear strategies are more accurate for individual judgments, but when these judgments are averaged, an unconstrain
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