Reasoning the fast and frugal way: Models of bounded rationality.

Humans and animals make inferences about the world under limited time and knowledge.In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might.Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: onereason decision making.These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information.By computer simulation, the authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures (e.g., multiple regression).The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy.This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference.Organisms make inductive inferences.Darwin ( 1872Darwin ( /1965 ) ) observed that people use facial cues, such as eyes that waver and lids that hang low, to infer a person's guilt.Male toads, roaming through swamps at night, use the pitch of a rival's croak to infer its size when deciding whether to fight (Krebs & Davies, 1987).Stock brokers must make fast decisions about which of several stocks to trade or invest when only limited information is available.The list goes on.Inductive inferences are typically based on uncertain cues: The eyes can deceive, and so can a tiny toad with a deep croak in the darkness.How does an organism make inferences about unknown aspects of the environment?There are three directions in which to look for an answer.From Pierre Laplace to George Boole to Jean Piaget, many scholars have defended the now classical view that the laws of human inference are the laws of probability and statistics (and to a lesser degree logic, which does not deal as easily with uncertainty).Indeed, the Enlightenment probabilists derived the laws of probability from what they believed to be the laws of human reasoning (Daston, 1988).Following this time-honored tradition, much contemporary research in psychology, behavioral ecology, and economics assumes standard