Heuristíska

Heuristíska describes methods, approaches, or reasoning based on heuristic principles—practical rules of thumb, educated guesses, or experience-based techniques used to solve problems quickly when exact or optimal solutions are impractical to obtain. The term denotes a mindset or set of strategies rather than a guaranteed method.

Etymology and scope: The word derives from the Greek heuriskein, meaning “to find.” In modern use, heuristísk nálgun appears in several disciplines. In psychology and behavioral economics, heuristics are mental shortcuts that guide judgment and decision making, often producing fast but biased results. In computer science and artificial intelligence, heuristic methods refer to problem-solving techniques that explore promising options without exhaustively evaluating all possibilities, as in heuristic search, heuristic algorithms, and rule-of-thumb strategies for optimization or planning.

Applications and examples: Common heuristics include availability (relying on easily recalled information), representativeness (judging similarity to a category), and anchoring (relying on an initial value). In AI, heuristics guide search processes (for example in pathfinding or puzzle solving) to reduce computational effort when exact solutions are costly. In mathematics and operations research, heuristic methods provide practical methods for obtaining good solutions when exact methods are too slow.

Limitations: Heuristísk methods trade guarantee for speed and practicality. They can lead to systematic errors or biases if misapplied or used outside appropriate domains, and they do not ensure optimality or correctness in every case.

See also: heuristic, cognitive bias, rule of thumb, heuristic search, optimization.