Publication Date
4-1-2024
Abstract
Studies of how people actually make decisions have led to an empirical formula that predicts the probability of different decisions based on the utilities of different alternatives. This formula is known as McFadden's formula, after a Nobel prize winning economist who discovered it. A similar formula -- known as softmax -- describes the probability that the classification predicted by a deep neural network is correct, based on the neural network's degrees of confidence in the object belonging to each class. In practice, we usually do not know the exact values of the utilities -- or of the degrees of confidence. At best, we know the intervals of possible values of these quantities. For different values from these intervals, we get, in general, different probabilities. It is desirable to find the range of all possible values of these probabilities. In this paper, we provide a feasible algorithm for computing these ranges.
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Comments
Technical Report: UTEP-CS-24-09a
To appear in Proceedings of the NAFIPS International Conference on Fuzzy Systems, Soft Computing, and Explainable AI NAFIPS'2024, South Padre Island, Texas, May 27-29, 2024