A heuristic is a method to help to solve a problem, commonly informal. It is particularly used for a method that often rapidly leads to a solution that is usually reasonably close to the best possible answer. Heuristics are "rules of thumb", educated guesses, intuitive judgments or simply common sense.

In more precise terms, heuristics stand for strategies using readily accessible, though loosely applicable, information to control problem-solving in human beings and machines.^[1].

Example

Perhaps the most fundamental heuristic is "trial & error," which can be used in everything from matching bolts to bicycles to finding the values of variables in algebra problems.

Here are a few other commonly used heuristics, from Polya's classic How to Solve It:^[2]

• Look to the unknown.
• If you are having difficulty understanding a problem, try drawing a picture.
• If you can't find a solution, try assuming that you have a solution and seeing what you can derive from that ("working backward").
• If the problem is abstract, try examining a concrete example.
• Try solving a more general problem first (the "inventor's paradox": the more ambitious plan may have more chances of success).

Psychology

In psychology, heuristics are simple, efficient rules, hard-coded by evolutionary processes or learned, which have been proposed to explain how people make decisions, come to judgments, and solve problems, typically when facing complex problems or incomplete information. These rules work well under most circumstances, but in certain cases lead to systematic cognitive biases.

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.)

Much of the work of discovering heuristics in human decision-makers was ignited by Amos Tversky and Daniel Kahneman^[3]. 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".^[4]

Theorized psychological heuristics

Well known:

• Anchoring and adjustment
• Availability heuristic
• Representativeness heuristic

Less well known:

Affect heuristic Contagion heuristic Effort heuristic Familiarity heuristic Fluency heuristic

Gaze heuristic Peak-end rule Recognition heuristic Scarcity heuristic Similarity heuristic

Simulation heuristic Social proof Take-the-best heuristic

Philosophy

In philosophy, especially in Continental European philosophy, the adjective "heuristic" (or the designation "heuristic device") is used when an entity X exists to enable understanding of, or knowledge concerning, some other entity Y. A good example is a model, which, as it is never identical with what it models, is a heuristic device to enable understanding of what it models. Stories, metaphors, etc., can also be termed heuristic in that sense. A classic example is the notion of utopia as described in Plato's best-known work, The Republic. This means that the "ideal city" as depicted in the The Republic is not given as something to be pursued, or to present an orientation-point for development; rather, it shows how things would have to be connected, and how one thing would lead to another (often with highly problematic results), if one would opt for certain principles and carry them through rigorously.

"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.)

Law

In legal theory, especially in the theory of law and economics, heuristics are used in the law when case-by-case analysis would be impractical, insofar as "practicality" is defined by the interests of a governing body.^[5]

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; but 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.

Computer science

Main article: Heuristic algorithm

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.

Human-computer interaction

In human-computer interaction, heuristic evaluation is a usability-testing technique devised by expert usability consultants. In heuristic evaluation, the user interface is reviewed by experts and its compliance to usability heuristics (broadly stated characteristics of a good user interface) is assessed, and any violating aspects are recorded.

Engineering

Main article: Heuristic (engineering)

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.

Notes

1. Pearl, Judea (1983). Heuristics: Intelligent Search Strategies for Computer Problem Solving. New York, Addison-Wesley, p. vii.
2. Polya, George (1945) How To Solve It: A New Aspect of Mathematical Method, Princeton, NJ: Princeton University Press. ISBN 0-691-02356-5   ISBN 0-691-08097-6
3. Daniel Kahneman, Amos Tversky and Paul Slovic, eds. (1982) Judgement under Uncertainty: Heuristics & Biases. Cambridge, UK, Cambridge University Press ISBN 0-521-28414-7
4. Gerd Gigerenzer, Peter M. Todd, and the ABC Research Group (1999). Simple Heuristics That Make Us Smart. Oxford, UK, Oxford University Press. ISBN 0-19-514381-7
5. Gerd Gigerenzer and Christoph Engel, eds. (2007). Heuristics and the Law, Cambridge, The MIT Press, ISBN 978-0-262-07275-5

See also

• Algorithm
• Behavioral economics – an economic subfield with heuristics as one of its main arguments
• Problem solving
• List of cognitive biases

External links

• The Heuristic Wiki
• Heuristics and artificial intelligence in finance and investment – The use of heuristics and AI techniques in finance and investment.
• “Discovering Assumptions” by Paul Niquette - Highly recommended

Further reading

• How To Solve It: Modern Heuristics, Zbigniew Michalewicz and David B. Fogel, Springer Verlag, 2000. ISBN 3-540-66061-5
• Russell, Stuart J. & Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, <http://aima.cs.berkeley.edu/> 

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Source: adapted by the editor from Wikipedia, the free encyclopedia; from the article "Heuristic". Image Credit.

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