Definition:
A heuristic is a practical problem-solving technique or approach that produces a solution that’s not guaranteed to be optimal, but is sufficient for a given set of goals. It’s often used when a perfect solution is impractical or when a rough solution is sufficient. Heuristics are often used due to their speed in coming to a satisfactory solution, where traditional methods might be slower or unfeasible.
Contexts of Use:
Cognitive Psychology:
- Heuristics in this context refer to simple, efficient rules, learned or hard-coded by evolutionary processes, that humans use to make decisions, come to judgments, and solve problems typically when facing complex situations or incomplete information.
Computer Science:
- In computer algorithms, a heuristic is a technique designed to solve a problem more quickly when classic methods are too slow, or to find an approximate solution when classic methods fail to find an exact solution.
Usability:
- Heuristic evaluations in user interface design involve expert evaluators who examine the interface and judge its compliance with recognized usability principles (heuristics).
Game Theory:
- Heuristics are strategies that seem logically correct but may not necessarily be so.
Search Algorithms:
- Used in path-finding algorithms used in routing and games. A known heuristic is the “Manhattan distance” in the A* search algorithm.
Characteristics:
Speed over Accuracy:
- Heuristics typically prioritize speed over accuracy, providing “good enough” solutions.
Bias and Error:
- Relying heavily on heuristics can lead to systematic errors or biases.
Adaptability:
- Due to their flexible nature, heuristics can be adapted to a wide range of situations.
Examples:
- Rule of Thumb: A simple, general principle applied to solve complex problems.
- Educated Guess: Making a decision based on prior knowledge and intuition.
- Stereotyping: Making assumptions about someone or something based on perceived group characteristics.
Conclusion:
While heuristics can be incredibly useful and efficient, they can also lead to oversights, errors, and biases. It’s essential to understand when and where to employ heuristics and to be aware of their limitations.