Heuristic ( /hjʉˈrɪstɨk/; or heuristics; Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery. Where an exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution.
Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics.
Such systematic distortions imply that cognitive mapping, as opposed to mental rotation (e.g., Shepard, 1978), is inherently biased. The biases, however, are lawful, and have been variously termed heuristics (Tversky, 1981) or gestalt forces (leveling and sharpening; Arnheim, 1969, p. 83), both of which have a common conceptual origin (gestalt) that is compatible with the functional approach to the study of mental imagery (e.g., Shepard, 1978, 1981, 1984).
In contrast, some recent scholars of decision making advocate more naturalistic, psychological and ecological approaches to decision making, and reject the classical ideal of rationality and its inherently negative assessment of heuristics ( Schwartz, 2002). From this naturalistic perspective, heuristics can be seen as natural and effective decision means that are not inherently associated with cognitive errors and extreme bias. Similarly, bounded rationality and limited cognitive capacity are viewed as natural features of human cognition and decision making, and not as imperfections relative to classical ideals ( Beach and Connolly, 2005).
A common heuristic is the representative one, whereby a person judges the likelihood of something based on how well it seems to represent a particular prototype. An unimaginative and automatic use of a heuristic can often lead to the wrong conclusion (see "Pitfalls to Problem Solving" below), but creativity and general strategies go well together.
There are several factors that influence decision making. Those factors are past experiences, cognitive biases, age and individual differences, belief in personal relevance, and an escalation of commitment. Heuristics are mental short cuts that take some of the cognitive load off decision makers. There are many kinds of heuristics, but three are important and commonly used; representative, availability, and anchoring-and-adjustment.
A group goes out on a slope and triggers an avalanche, and one of the skiers dies.
The skiers used the familiarity heuristic — it says if something comes quickly to mind, follow it. [The skiers went with their first thought — to stay the course, which led to the disaster.] They employed the mimicry heuristic — you don't want to be the naysayer in the group. [No one in the ski group disagreed with the decision to stay the course.]
Heuristics are knowledge structures, presumably learned and stored in memory. Judgments formed on the basis of heuristic processing reflect easily processed heuristic cue information (e.g., source expertise), rather than individualistic or particularistic information. As such, heuristic processing makes minimal cognitive demands. However, the heuristic mode is constrained by basic principles of knowledge activation and use--namely, availability, accessibility, and applicability (e.g., Higgins, 1996).
Heuristics are proximate mechanisms of bounded rationality that enable people to arrive at quick decisions under uncertainty, based on modest amounts of information.
We have developed and investigated heuristics across a wide range of tasks including the fluency heuristic (inference), the QuickEst heuristic (estimation), the priority heuristic (risky choice), the natural-mean heuristic (experience-based risky choice), and the equity heuristic (resource allocation).
By connecting the behavioral processes associated with the recognition heuristic to the brain markers associated with familiarity-based memory, the authors were able to establish that the recognition heuristic really does seem to depend on pure recognition, or familiarity. Rosburg says that this kind of knowledge “allows us to understand both deficient decision making and the benefits of heuristics.”
While the recognition heuristic may allow us to make decisions quickly and efficiently, it may not always lead us down the best path.
In the case of probability estimation, several recent studies have demonstrated that humans seem to follow a judgmental heuristic called "representativeness'' when faced with tasks involving intuitive prediction (1972, 1973). The idea is that people predict the outcome that appears most representative of the evidence. While this heuristic often leads to correct judgments, it can lead to large and consistent biases that are quite difficult t o eliminate.