论文标题
在有限的注意力下揭示了偏好分析
Revealed Preference Analysis Under Limited Attention
论文作者
论文摘要
一个观察者想从她的选择中了解决策者的福利。她认为，在有限的关注下做出决定。我们认为，有限注意的标准模型无法极大地帮助观察者。为了解决这个问题，我们通过在决策过程中施加注意力的注意力来研究有限关注的选择模型家族。我们构建了一种算法，该算法在这些模型中设置了一个不完整的数据，该算法恢复了显示的偏好关系。接下来，我们将这些模型带入实验数据。我们首先表明，假设受试者在完成决策之前至少进行了比较（即2个注意板）在描述行为与有限注意的标准模型相比，几乎是无成本的。另一方面，在揭示的偏好方面，修订的模型的确更好。对于标准模型中63％的受试者，我们无法恢复任何偏好，而修改后的模型则揭示了所有受试者的偏好。总的来说，修订的模型使我们能够恢复在全面关注下将回收的偏好的三分之一。
An observer wants to understand a decision-maker's welfare from her choice. She believes that decisions are made under limited attention. We argue that the standard model of limited attention cannot help the observer greatly. To address this issue, we study a family of models of choice under limited attention by imposing an attention floor in the decision process. We construct an algorithm that recovers the revealed preference relation given an incomplete data set in these models. Next, we take these models to the experimental data. We first show that assuming that subjects make at least one comparison before finalizing decisions (that is, an attention floor of 2) is almost costless in terms of describing the behavior when compared to the standard model of limited attention. In terms of revealed preferences, on the other hand, the amended model does significantly better. We can not recover any preferences for 63% of the subjects in the standard model, while the amended model reveals some preferences for all subjects. In total, the amended model allows us to recover one-third of the preferences that would be recovered under full attention.
