In more precise terms, heuristics stand for strategies using readily accessible, though loosely applicable, information to control problem-solving in human beings and machines.
Here are a few other commonly used heuristics, from Polya's classic How to Solve It:
For instance, people may tend to perceive more expensive beers as tasting better than inexpensive ones (providing the two beers are of similar initial quality or lack of quality and of similar style). This finding holds true even when prices and brands are switched; putting the high price on the normally relatively inexpensive brand is enough to lead subjects to perceive it as tasting better than the beer that is normally more expensive. One might call this "price implies quality" bias. (Cf. Veblen good.)
While much of the work of discovering heuristics in human decision-makers has been done by Amos Tversky and Daniel Kahneman, the concept was originally introduced by Nobel laureate Herbert Simon. Gerd Gigerenzer focuses on how heuristics can be used to make judgments that are in principle accurate, rather than producing cognitive biases – heuristics that are "fast and frugal".
"Heuristic" is also often commonly used as a noun to describe a rule-of-thumb, procedure, method, and so on in, for example, the context of the construction of scientific theories. (See the logic of discovery, and philosophers such as Lakatos, Lindley Darden, and others.)
For instance, in many states in the United States the legal drinking age is 21, because it is argued that people need to be mature enough to make decisions involving the risks of alcohol consumption. However, assuming people mature at different rates, the specific age of 21 would be too late for some and too early for others. In this case, the somewhat arbitrary deadline is used because it is impossible or impractical to tell whether one individual is mature enough that society can trust them with that kind of responsibility. Some proposed changes, however, have included the completion of an alcohol education course rather than the attainment of 21 years of age as the criterion for legal alcohol possession. This would situate youth alcohol policy more on a case-by-case model and less on a heuristic one, since the completion of such a course would presumably be voluntary and not uniform across the population.
The same reasoning applies to patent law. Patents are justified on the grounds that inventors need to be protected in order to have incentive to invent. It is therefore argued that, in society's best interest, inventors should be issued with a temporary government-granted monopoly on their product, so that they can recoup their investment costs and make economic profit for a limited period of time. In the United States the length of this temporary monopoly is 20 years from the date the application for patent was filed, though the monopoly does not actually begin until the application has matured into a patent. However, like the drinking-age problem above, the specific length of time would need to be different for every product in order to be efficient; a 20-year term is used because it is difficult to tell what the number should be for any individual patent. More recently, some, including Lawrence Lessig, have argued that patents in different kinds of industries – such as software patents – should be protected for different lengths of time.
In computer science, a heuristic is a technique designed to solve a problem that ignores whether the solution can be proven to be correct, but which usually produces a good solution or solves a simpler problem that contains or intersects with the solution of the more complex problem. Many commercial anti-virus scanners use heuristic signatures to look for specific attributes and characteristics for detecting viruses and other forms of malware.
Heuristics are intended to gain computational performance or conceptual simplicity, potentially at the cost of accuracy or precision.
In engineering, a heuristic is an experience-based method that can be used as an aid to solve process design problems, varying from size of equipment to operating conditions. By using heuristics, time can be reduced when solving problems, which may be very valuable.
Because heuristics are fallible, it is important to understand their limitations. They are intended to be used as aids in order to make quick estimates and preliminary process designs.
Great care must be given when employing a heuristic algorithm. One common pitfall in implementing a heuristic method to meet a requirement comes when the engineer or designer fails to realize that the current data set doesn't necessarily represent future system states.
While the existing data can be pored over and an algorithm can be devised to successfully handle the current data, it is imperative to ensure that the heuristic method employed is capable of handling future data sets. This means that the engineer or designer must fully understand the rules that generate the data and develop the algorithm to meet those requirements and not just address the current data sets.
A simple example of how heuristics can fail is to answer the question "What is the next number in this sequence: 1, 2, 4?". One heuristic algorithm might say that the next number is 8 because the numbers are doubling - leading to a sequence like 1, 2, 4, 8, 16, 32... Another, equally valid, heuristic would say that the next number is 7 because each number is being raised by one higher interval than the one before - leading to a series that looks like 1, 2, 4, 7, 11, 16...
Statistical analysis must be conducted when employing heuristics to ensure that enough data points are utilized to make incorrect outcomes statistically insignificant.