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Disentangling preferences and limited attention: Random-utility models with consideration sets
Journal of Mathematical Economics ( IF 1.3 ) Pub Date : 2021-01-04 , DOI: 10.1016/j.jmateco.2020.102468
Peter Gibbard

This paper presents a model of choice with limited attention. The decision-maker forms a consideration set, from which she chooses her most preferred alternative. Both preferences and consideration sets are stochastic. While we present axiomatisations for this model, our focus is on the following identification question: to what extent can an observer retrieve probabilities of preferences and consideration sets from observed choices? Our first conclusion is a negative one: if the observed data are choice probabilities, then probabilities of preferences and consideration sets cannot be retrieved from choice probabilities. We solve the identification problem by assuming that an “enriched” dataset is observed, which includes choice probabilities under two frames. Given this dataset, the model is “fully identified”, in the sense that we can recover from observed choices (i) the probabilities of preferences (to the same extent as in models with full attention) and (ii) the probabilities of consideration sets. While a number of recent papers have developed models of limited attention that are, in a similar sense, “fully identified”, they obtain this result not by using an enriched dataset but rather by making a restrictive assumption about the default option, which our paper avoids.



中文翻译:

解开偏好和有限的关注:带有考虑因素集的随机效用模型

本文提出了一种有限关注的选择模型。决策者形成一个考虑因素集,从中选择她最喜欢的替代方案。偏好和考虑因素集都是随机的。当我们提出该模型的公理化时,我们的重点是以下识别问题:观察者可以在多大程度上从观察到的选择中获得偏好和考虑因素的概率?我们的第一个结论是一个否定的结论:如果观察到的数据是选择概率,那么就无法从选择概率中检索出偏好和考虑因素的概率。我们通过假设观察到一个“丰富的”数据集来解决识别问题,该数据集包括两个框架下的选择概率。有了这个数据集,就可以“完全识别”模型,从某种意义上说,我们可以从观察到的选择中恢复(i)偏好的概率(达到与全神贯注模型相同的程度)和(ii)对价集的概率。尽管最近有许多论文开发了注意力有限的模型,这些模型以类似的方式“完全识别”,但它们不是通过使用丰富的数据集而是通过对默认选项进行限制性假设来获得此结果的,因此本文避免。

更新日期:2021-01-04
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