Semi-Parametric Approach to Behavioral Biases

Résumé

This paper shows how to recover behavioral biases from revealed preference rankings implied by choices. The approach is sufficiently flexible to account for many well-documented behavioral biases in the literature. I apply the model to workhorse datasets in the literature on choice under risk and scanner consumer choices. I show that accounting for simple behavioral biases such as inattention and salience thinking suffices to explain most violations of rationality. Finally, I demonstrate that well-known regularities, such as the correlates of risk aversion, might in fact be confounded by simple behavioral biases. Overall, this research delineates a robust method for extracting behavioral biases from choice data, thereby not only enhancing our understanding of consumer decision-making but also offering a fresh lens through which to explore the contours of rationality in human behavior.