Maximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value.

Maximization theory is an alternative to reinforcement theory as a description of steady-state behavior. Situations to which reinforcement theory has been directly applied (such as reinforcement of rats pressing levers and pigeons pecking keys in Skinner boxes) and situations to which reinforcement theory has occasionally been extended (such as human economic behavior and human self-control) can be described by maximization theory. This approach views behavior as a quantitative outcome of the interaction of the putative instrumental response, the reinforcer, and the other activities available in the situation. It provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.