 Simon’s focus on computationally efficient methods that yield solutions that are good enough contrasts with Friedman’s as if methodology, since evaluating whether a solution is “good enough”, in Simon’s terms, involves search procedures, stopping criteria, and how information is integrated in the course of making a decision. Simon offers several examples to motivate inquiry into computationally efficient methods. Here is one. Applying the game-theoretic minimax algorithm to the game of chess calls for evaluating more chess positions than the number of molecules in the universe (Simon 1957a: 6). Yet if the game of chess is beyond the reach of exact computation, why should we expect everyday problems to be any more tractable? Simon’s question is to explain how human beings manage to solve complicated problems in an uncertain world given their meager resources. Answering Simon’s question, as opposed to applying Friedman’s method to fit a constrained optimization model to observed behavior, is to demand a model with better predictive power concerning boundedly rational judgment and decision making. In pressing this question of how human beings solve uncertain inference problems, Simon opened two lines of inquiry that continue to today, namely: