 Satisficing is the strategy of considering the options available to you for choice until you find one that meets or exceeds a predefined threshold—your aspiration level—for a minimally acceptable outcome. Although Simon originally thought of procedural rationality as a poor approximation of global rationality, and thus viewed the study of bounded rationality to concern “the behavior of human beings who satisfice because they have not the wits to maximize” (Simon 1957a: xxiv), there are a range of applications of satisficing models to sequential choice problems, aggregation problems, and high-dimensional optimization problems, which are increasingly common in machine learning.