Abstract
The purpose of this paper is to present a new approach to sum-fuzzy rational choice functions. By making use of the model of perceptrons in neural theory, we establish a sufficient and necessary condition for sum-fuzzy rationality. Moreover, we provide a geometric characterization of sum-fuzzy rationality for single-valued choice functions. Based on the learning rules of perceptrons, we offer an algorithm to find a sum-fuzzy implementation of a choice function and, then, provide a concrete example.
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