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Abstract: 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, that they calibrate using a combination of eye-tracking and choice data. Their model extends the directed cognition model of Gabaix et al. (2006) 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.

Keywords: Preference Measurement; Incentive Compatibility; Eye Tracking; Dynamic Discrete Choice Models

JEL-codes: D83; M31

49 pages, October 14, 2014

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