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摘要
The study of wisdom of crowds shows that the group judgment formed by aggregating individual judgments is more accurate than the individual judgments. In order to improve the aggregation quality and eliminate the systematic bias in individual judgment and heuristic process, this paper concretely constructs a variety of aggregation models based on extreme ideas. Two indicators, accuracy and superiority, were set up to quantitatively evaluate the wisdom of crowds validity of aggregation. The data of individual probability estimation were obtained through the probability judgment experiment on Master of Business Administration. Based on the data of individual probability estimation, Several sequential strategies and regional strategies of individual probability aggregation are compared, and the extreme function selection and parameter optimization are completed. The results show that: 1) If only the probability estimates within intervals [0, 0.45] and [0.55, 1] are polarized, the local extreme strategy performs better than the global extreme strategy. 2) Average-then-calibrate strategy is better than calibrate-then-average strategy; 3) In the average-then-calibrate strategy, the anti-S-type polarizing function proposed in this paper can outperform the classical polarizing function. 4) The performance of extreme calibration aggregation is better than that of traditional arithmetic average method. This paper proposes a better strategy of aggregating individual probability estimates, which can significantly improve the wisdom of the crowds validity under the same individual valuation conditions. ? 2023 Systems Engineering Society of China.
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