April 21, 2004
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.
Abbas, A. 2003. An Information Theory for Preferences. In: G.J. Erickson, Y. Zhai (eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jackson Hole WY August 3rd – 8th 2003, AIP Conference Proceedings 707, American Institute of Physics, Melville NY, pp. 127-144.