2003/10/23 by Ali E. Abbas
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Analogy #Applied mathematics #Artificial intelligence #Computer science #Cumulative distribution function #Cumulative prospect theory #Decision-Making and Behavioral Economics #Entropy (arrow of time) #Expected utility hypothesis #Forecasting Techniques and Applications #Function (biology) #Information theory #Mathematical economics #Mathematics #Physics #Principle of maximum entropy #Probability density function #Probability distribution #Probability theory #Statistical Mechanics and Entropy #Statistical physics #Statistics #Utility theory #cs.AI
paper · pdf · doi:10.1063/1.1751362
arxiv created 2003/10/23 · openalex publication_date 2004/01/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.