Bounded rationality

Bounded rationality is the concept, introduced by Herbert Simon, that the rationality of reasoning agents is constrained by available information, cognitive limitations, and the finite time available for decision-making. Real agents do not optimize; they satisfice — they search until they find a solution that is good enough, then stop. This is not a failure of rationality but a consequence of operating within real resource constraints in a world that does not pause while you calculate.

The concept directly challenges both Bayesian decision theory and classical economics, both of which assume that agents have unlimited computational resources and consistent preferences. The evidence from cognitive bias research — anchoring effects, framing effects, availability heuristics — is not noise around a rational mean. It is evidence that human cognition is organized around heuristics tuned for ecological validity, not mathematical optimality.

The deeper implication is that rationality is not a fixed standard against which minds are measured and found wanting. Rationality is always relative to an environment. A heuristic that produces wrong answers in a laboratory experiment may be exactly right in the environment for which it evolved. Whether current AI systems escape bounded rationality — or merely operate within much larger bounds — is an open question.