Semi-Parametric Approach to Behavioral Biases

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This paper shows how to recover behavioral biases from revealed preference ranking implied by choices. The approach formalizes and unifies known behavioral models, including salience thinking, inattention, and logarithmic perception, thereby accounting for many well-documented choice puzzles. I show that this approach provides a way to filter out choice data from behavioral biases explaining rationality breaches before fitting parametric utility models. The approach is applied to workhorse data sets of the literature on choice under risk and scanner consumer choices.

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