This paper presents a comprehensive analysis of revenue management under a general discrete choice model of consumer behavior. The authors analyze a single-leg yield management problem where buyers' choice behavior is explicitly modeled. The choice model specifies the probability of purchasing each fare product as a function of the set of fare products offered. The control problem involves deciding which subset of fare products to offer at each point in time. The optimal policy is shown to have a simple form: identifying an ordered family of "nondominated" subsets and selecting one of these subsets based on the remaining capacity. The optimal policy is nested if and only if the ordered sets are increasing. The paper also shows that two important models, the independent demand model and the multinomial logit model (MNL), satisfy this condition and hence nested-by-fare-order policies are optimal in these cases. Additionally, the authors develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure. The paper concludes that the proposed methodology substantially fills the void in existing revenue management methodologies that do not directly and completely address consumer choice behavior.This paper presents a comprehensive analysis of revenue management under a general discrete choice model of consumer behavior. The authors analyze a single-leg yield management problem where buyers' choice behavior is explicitly modeled. The choice model specifies the probability of purchasing each fare product as a function of the set of fare products offered. The control problem involves deciding which subset of fare products to offer at each point in time. The optimal policy is shown to have a simple form: identifying an ordered family of "nondominated" subsets and selecting one of these subsets based on the remaining capacity. The optimal policy is nested if and only if the ordered sets are increasing. The paper also shows that two important models, the independent demand model and the multinomial logit model (MNL), satisfy this condition and hence nested-by-fare-order policies are optimal in these cases. Additionally, the authors develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure. The paper concludes that the proposed methodology substantially fills the void in existing revenue management methodologies that do not directly and completely address consumer choice behavior.