Imprecise Data Sets as a Source of Ambiguity: A Model and Experimental Evidence
研究了当观测数据不精确时，个体如何形成信念，提出一个频率主义模型，将处理不精确观测的方式与模糊态度联系起来，并通过实验验证了模型的主要结论。
In many circumstances, evaluations are based on empirical data. However, some observations may be imprecise, meaning that it is not entirely clear what occurred in them. We address the question of how beliefs are formed in these situations. The individual in our model is essentially a “frequentist.” He first makes a subjective judgment about the occurrence of the event for each imprecise observation. This may be any number between zero and one. He then evaluates the event by its “subjective” frequency of occurrence. Our model connects the method of processing imprecise observations with the individual's attitude toward ambiguity. An individual who in imprecise observations puts low (high) weight on the possibility that an event occurred is ambiguity averse (loving). An experiment supports the main assertions of the model: with precise data, subjects behave as if there were no ambiguity, whereas with imprecise data subjects turn out to be ambiguity averse. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.