The rational inattention theory aims to evaluate instances in which a decision is made in an information-rich environment where consumers cannot process all information due to limited cognitive capacity. In contrast to classical random utility-maximizing models, rational inattention discrete choice models do not assume that decision-makers make choices with complete knowledge of the alternatives. Today’s information technology tools create a decision-making environment in which information is plentiful and easily accessible. Yet, it is cognitively impossible for households to be aware of every aspect of available options. This study uses rational inattention theory to investigate residential location choices in the Greater Toronto Area (GTA) during the COVID-19 pandemic, using an efficient-adaptive stated preference dataset collected in July 2021. The rational inattention theory requires identifying information processing costs and marginal probabilities as decision-makers’ prior beliefs. The empirical model of this paper proposes using the time respondents spend on choice problems to measure their attention span and the latent preferences produced from the efficient-adaptive survey to measure their prior beliefs.
Shakib et al. (2025) studied this question.