CONCEPT
Heuristic Search
Simon and Newell's 1972 framework for how bounded minds navigate problem spaces too large for exhaustive search — using rules of thumb to direct attention toward promising alternatives, the cognitive engine of every expert performance and the specific capability AI most powerfully augments.
Heuristic search is the central operation in Simon and Newell's theory of problem-solving: the use of rules of thumb to direct a bounded agent's navigation through a
problem space too large to search exhaustively. The chess grandmaster does not evaluate every legal move; she evaluates four or five, selected by pattern-recognition heuristics built from years of practice. The software architect does not consider every possible system design; she considers a handful, filtered by heuristics about what has worked in similar domains. The heuristic does not guarantee optimality — it directs attention toward regions of the problem space where good solutions are most likely to be found, but cannot
promise that the best solution will be discovered. The framework applies directly to AI-augmented work, which provides extraordinary implementation heuristics (the tool's pattern libraries guide the builder to competent solutions) while leaving
goal heuristics — the capacity to specify what is worth pursuing — bounded by