An Information Theory for Preferences

Publication Type: 
Ali E. Abbas
Recent literature in the Maximum Entropy workshop introduced an analogy between cumulative probability distributions and normalized utility functions. Based on this analogy, a utility density function is defined as the derivative of a normalized utility function. A utility density function has the same mathematical properties as a probability density function, and forms the basis of a mathematical correspondence between utility and probability. This paper presents several results that stem from this correspondence, and provides new interpretations to measures of information theory when applied to utility theory.