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Heuristic
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Heuristic (hju?'r?s.t?k) is an adjective for methods that help in problem solving, in turn leading to learning and discovery. These methods in most cases employ experimentation and trial-and-error techniques. A heuristic method is particularly used to rapidly come to a solution that is reasonably close to the best possible answer, or 'optimal solution'. Heuristics are "rules of thumb", educated guesses, intuitive judgments or simply common sense.
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Encyclopedia
Heuristic (hju?'r?s.t?k) is an adjective for methods that help in problem solving, in turn leading to learning and discovery. These methods in most cases employ experimentation and trial-and-error techniques. A heuristic method is particularly used to rapidly come to a solution that is reasonably close to the best possible answer, or 'optimal solution'. Heuristics are "rules of thumb", educated guesses, intuitive judgments or simply common sense. Heuristics (hyu-'ris-tiks) 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. Forensic engineering is an important tool in tracing defects in products and processes.
Example
Perhaps 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).
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 errors or cognitive biases. Heuristic theories in psychology motivate both proponents and opponents of the theory to pursue research related to it.
For example, 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.)
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".
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 considered non-optimal for humans 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 do not regularly understand the idea that certain medical tests generate false positives, they can and do understand that there are alternative explanations for a positive result, such as cysts. Although simple Bayesian calculations alone 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 the 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
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, 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.)
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.
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 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.
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
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, so by using optical microscopy to examine fracture surface for example, may point to the reason whya particular component failed.
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.
The 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 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 analyses should be conducted when employing heuristics to estimate the probability of incorrect outcomes.
See also
Further reading
- How To Solve It: Modern Heuristics, Zbigniew Michalewicz and David B. Fogel, Springer Verlag, 2000. ISBN 3-540-66061-5
External links
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- – The use of heuristics and AI techniques in finance and investment.
- by Paul Niquette — Highly recommended
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