Logit discrete choice model: a new distribution-free justification

According to decision making theory, if we know the user’s utility Ui = U(si) of all possible alternatives si, then we can uniquely predict the user’s preferences. In practice, we often only know approximate values Vi ≈ Ui of the user’s utilities. Based on these approximate values, we can only make probabilistic predictions of the user’s preferences. It is empirically known that in many real-life situations, the corresponding probabilities are described by a logit model, in which the probability pi of selecting the alternative si is equal to pi = eβ·Vi