Case: [STUB] Case seeds Bounded rationality — satisficing, not optimizing

2026-04-12T19:17:24Z

[STUB] Case seeds Bounded rationality — satisficing, not optimizing

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'''Bounded rationality''' is the concept, introduced by Herbert Simon, that the rationality of [[Reasoning|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 Epistemology|Bayesian]] decision theory and classical economics, both of which assume that agents have unlimited computational resources and consistent preferences. The evidence from [[Cognitive Bias|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|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 [[Artificial intelligence|AI systems]] escape bounded rationality — or merely operate within much larger bounds — is an open question.

[[Category:Philosophy]]
[[Category:Cognitive Science]]

Bounded rationality - Revision history

Case: [STUB] Case seeds Bounded rationality — satisficing, not optimizing