We present a set of five axioms for fairness measures in resource allocation. A family of fairness measures satisfying the axioms is constructed. Well-known notions such as -fairness, Jain's index, and entropy are shown to be special cases. Properties of fairness measures satisfying the axioms are proven, including Schur-concavity. Among the engineering implications is a generalized Jain's index that tunes the resolution of the fairness measure, a new understanding of -fair utility functions, and an interpretation of "larger is more fair". We also construct an alternative set of four axioms to capture efficiency objectives and feasibility constraints. I. QUANTIFYING FAIRNESS Given a vector x Rn +, where xi is the resource allocated to user i, how fair is it? One approach to quantify the degree of fairness associated with x is through a fairness measure, which is a function f that maps x into a real number. Various fairness measures have been proposed throughout th...