A Bounded Rationality Model of Information Search and Choice in Preference Measurement
构建了一个有界理性下的动态离散选择模型，结合眼动追踪和选择数据校准，用于预测偏好测量中的信息搜索与选择行为，可缩短问卷并更好区分属性。
It is becoming increasingly easier for researchers and practitioners to collect eye-tracking data during online preference measurement tasks. The authors develop a dynamic discrete choice model of information search and choice under bounded rationality, which they calibrate using a combination of eye-tracking and choice data. Their model extends Gabaix et al.'s (2006) directed cognition model by capturing fatigue, proximity effects, and imperfect memory encoding and by estimating individual-level parameters and partworths within a likelihood-based hierarchical Bayesian framework. The authors show that modeling eye movements as the outcome of forward-looking utility maximization improves out-of-sample predictions, enables researchers and practitioners to use shorter questionnaires, and allows better discrimination between attributes.