Tversky A, Kahneman D
Science. 1974 Sep 27;185(4157):1124-31. doi: 10.1126/science.185.4157.1124.
This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
（i）代表性，通常在人们被要求判断物体或事件A属于类别或过程B的概率时使用；（ii）实例或情景的可获得性，通常在人们被要求评估某一类别出现的频率或特定发展的合理性时使用；（iii）从锚点进行调整，通常在有相关值时用于数值预测。这些启发法非常经济且通常有效，但它们会导致系统性的和可预测的错误。更好地理解这些启发法及其导致的偏差，可能会改善在不确定性情况下的判断和决策。
