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  • 标题:Coherence of probability judgments from uncertain evidence: Does ACH help?
  • 本地全文:下载
  • 作者:Christopher W. Karvetski ; David R. Mandel
  • 期刊名称:Judgment and Decision Making
  • 印刷版ISSN:1930-2975
  • 出版年度:2020
  • 卷号:15
  • 期号:6
  • 页码:939-958
  • 语种:English
  • 出版社:Society for Judgment and Decision Making
  • 摘要:Although the Analysis of Competing Hypotheses method (ACH) is a structured analytic technique promoted in several intelligence communities for improving the quality of probabilistic hypothesis testing, it has received little empirical testing. Whereas previous evaluations have used numerical evidence assumed to be perfectly accurate, in the present experiment we tested the effectiveness of ACH using a judgment task that presented participants with uncertain evidence varying in source reliability and information credibility. Participants (N = 227) assigned probabilities to two alternative hypotheses across six cases that systematically varied case features. Across multiple tests of coherence, the ACH group showed no advantage over a no-technique control group. Both groups showed evidence of subadditivity, unreliability, and overly conservative non-Bayesian judgments. The ACH group also showed pseudo-diagnostic weighting of evidence. The findings do not support the claim that ACH is effective at improving probabilistic judgment.
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