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Heuristic ( , from the Greek "Εὑρίσκω" for "find" or "discover") is an adjective for experience-based techniques that help in problem solving, learning and discovery. A heuristic method is particularly used to rapidly come to a solution that is hoped to be close to the best possible answer, or 'optimal solution'. Heuristics are "rules of thumb", educated guesses, intuitive judgments or simply common sense. A heuristic is a general way of solving a problem. Heuristics as a noun is another name for heuristic methods.

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


It may be argued that the most fundamental heuristic is "trial and 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 1945 book, How to Solve It:
  • 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).


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 errors or cognitive biases.

Although 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".

In 2002, Daniel Kahneman and Shane Frederick proposed that cognitive heuristics work by a process called attribute substitution which happens without conscious awareness. According to this theory, when someone makes a judgment (of a target attribute) that is computationally complex, they instead substitute a more easily calculated heuristic attribute. In effect, they deal with a cognitively difficult problem by answering a more simple problem, without being aware that this is happening. This theory explains cases where judgments fail to show regression toward the mean.

A number of classic heuristic experiments have recently (2003) come into dispute because some of the experiments conducted by Amos Tversky and Daniel Kahneman were designed in a way which made it more difficult for people to judge the relevance of stochastic variables involved in the problems. Without full knowledge of the relevance of probabilities with regard to distinct events and those variables' influence on events, humans may not apply them properly. Consequently, although humans may not properly comprehend why medical tests generate false positives, they often understand that there can be explanations for a positive result other than what the test was specifically looking for. Although simple Bayesian calculations - which facilitate the inclusion of probability based on prior results - may not be enough for individuals to overcome these lapses in judgment, it has been proven, through experimentation, that when individuals are aware of the causal network describing the problem in question, the predictions regarding such events improve.. Experiments by Joshua Tenenbaum and Tevye Krynski have disputed both the mammogram problem and the cab problem, originally formulated by Kahneman and Tversky, by adjusting the way in which subjects are made to understand the nature of the parameters involved in these predictions. Furthermore, Tenenbaum and Griffiths have shown that for "everyday decisions," individuals make reasonable predictions that are within known frequentist distributions, such as when individuals are asked what they expect to be the length of term of a congressman given that the congressman has been in office already for 10 years. These sorts of commonplace predictions from individuals fall well within the distributions that measure the occurrence of such events.

Theorized psychological heuristics

Well known

Less well known


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, or method. Philosophers of science have emphasized the importance of heuristics in creative thought and constructing scientific theories. (See the logic of discovery, and philosophers such as Imre Lakatos, Lindley Darden, and others.)


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.

For instance, in many states in the United Statesmarker 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 University of North Dakota law professor Eric E. Johnson, have argued that patents in different kinds of industries – such as software patents – should be protected for different lengths of time.

Computer science

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. Some 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 their Turing Award acceptance speech, Herbert Simon and Allen Newell discuss the Heuristic Search Hypothesis: A physical symbol system will repeatedly generate and modify known symbol structures until the created structure matches the solution structure.

That is, each successive iteration depends upon the step before it, thus the heuristic search learns what avenues to pursue and which ones to disregard by measuring how close the current iteration is to the solution. Therefore, some possibilities will never be generated as they are measured to be less likely to complete the solution.

A heuristic method can accomplish its task by utilizing search trees. However, instead of generating all possible solution branches, a heuristic selects branches more likely to produce outcomes than other branches. It is selective at each decision point; picking branches that are more likely to produce solutions.

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.


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. There are several methods which are available to engineers, and they include Failure mode and effects analysis and Fault tree analysis. The former relies on a group of qualified engineers to evaluate problems, rank them in order of importance, and then recommend solutions. The methods of forensic engineering are an important source of information for investigating problems, especially by elimination of unlikely causes and using the weakest link principle.

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.

Pitfalls of heuristics

Heuristic algorithms are often employed because they may be seen to "work" without having been mathematically proven to meet a given set of requirements.

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 does not 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 should be conducted when employing heuristics to estimate the probability of incorrect outcomes.

See also


  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) Judgment 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. Krynski, T. R. and Tenenbaum, J. B. (2007). The role of causality in judgment under uncertainty. Journal of Experimental Psychology. General, 136(3):430-450.
  6. Griffiths, Thomas, L., Tenenbaum, and Joshua, B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9):767-773.
  7. K. M. Jaszczolt (2006). "Defaults in Semantics and Pragmatics", The Stanford Encyclopedia of Philosophy, ISSN 1095-5054
  8. Roman Frigg and Stephan Hartmann (2006). "Models in Science", The Stanford Encyclopedia of Philosophy, ISSN 1095-5054
  9. Olga Kiss (2006). "Heuristic, Methodology or Logic of Discovery? Lakatos on Patterns of Thinking", Perspectives on Science, vol. 14, no. 3, pp. 302-317, ISSN 1063-6145
  10. Gerd Gigerenzer and Christoph Engel, eds. (2007). Heuristics and the Law, Cambridge, The MIT Press, ISBN 978-0-262-07275-5
  11. Eric E. Johnson (2006). "Calibrating Patent Lifetimes", Santa Clara Computer & High Technology Law Journal, vol. 22, p. 269-314
  12. Newell, A. & Simon, H. A. (1976). Computer science as empirical inquiry: symbols and search. Comm. Of the ACM. 19, 113-126.

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