Heuristic
A heuristic (
Examples that employ heuristics include using trial and error, a rule of thumb or an educated guess.
Heuristics are the strategies derived from previous experiences with similar problems. These strategies depend on using readily accessible, though loosely applicable, information to control problem solving in human beings, machines and abstract issues.[3][4] When an individual applies a heuristic in practice, it generally performs as expected. However it can alternatively create systematic errors.[5]
The most fundamental heuristic is trial and error, which can be used in everything from matching nuts and bolts to finding the values of variables in algebra problems. In mathematics, some common heuristics involve the use of visual representations, additional assumptions, forward/backward reasoning and simplification. Here are a few commonly used heuristics from George Pólya's 1945 book, How to Solve It:[6]
- 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, either learned or inculcated by evolutionary processes. These psychological heuristics have been proposed to explain how people make decisions, come to judgements, and solve problems. These rules typically come into play when people face complex problems or incomplete information. Researchers employ various methods to test whether people use these rules. The rules have been shown to work well under most circumstances, but in certain cases can lead to systematic errors or cognitive biases.[7]
History
The study of heuristics in human decision-making was developed in the 1970s and the 1980s, by the psychologists Amos Tversky and Daniel Kahneman,[8] although the concept had been originally introduced by the Nobel laureate Herbert A. Simon. Simon's original primary object of research was problem solving that showed that we operate within what he calls bounded rationality. He coined the term satisficing, which denotes a situation in which people seek solutions, or accept choices or judgements, that are "good enough" for their purposes although they could be optimised.[9]
Rudolf Groner analysed the history of heuristics from its roots in ancient Greece up to contemporary work in cognitive psychology and artificial intelligence,[10] proposing a cognitive style "heuristic versus algorithmic thinking", which can be assessed by means of a validated questionnaire.[11]
Adaptive toolbox
Gerd Gigerenzer and his research group argued that models of heuristics need to be formal to allow for predictions of behavior that can be tested.[12] They study the fast and frugal heuristics in the "adaptive toolbox" of individuals or institutions, and the ecological rationality of these heuristics; that is, the conditions under which a given heuristic is likely to be successful.[13] The descriptive study of the "adaptive toolbox" is done by observation and experiment, while the prescriptive study of ecological rationality requires mathematical analysis and computer simulation. Heuristics – such as the recognition heuristic, the take-the-best heuristic and fast-and-frugal trees – have been shown to be effective in predictions, particularly in situations of uncertainty. It is often said that heuristics trade accuracy for effort but this is only the case in situations of risk. Risk refers to situations where all possible actions, their outcomes and probabilities are known. In the absence of this information, that is under uncertainty, heuristics can achieve higher accuracy with lower effort.[14] This finding, known as a less-is-more effect, would not have been found without formal models. The valuable insight of this program is that heuristics are effective not despite their simplicity – but because of it. Furthermore, Gigerenzer and Wolfgang Gaissmaier found that both individuals and organisations rely on heuristics in an adaptive way.[15]
Cognitive-experiential self-theory
Heuristics, through greater refinement and research, have begun to be applied to other theories, or be explained by them. For example, the cognitive-experiential self-theory (CEST) is also an adaptive view of heuristic processing. CEST breaks down two systems that process information. At some times, roughly speaking, individuals consider issues rationally, systematically, logically, deliberately, effortfully, and verbally. On other occasions, individuals consider issues intuitively, effortlessly, globally, and emotionally.[16] From this perspective, heuristics are part of a larger experiential processing system that is often adaptive, but vulnerable to error in situations that require logical analysis.[17]
Attribute substitution
In 2002, Daniel Kahneman and Shane Frederick proposed that cognitive heuristics work by a process called attribute substitution, which happens without conscious awareness.[18] According to this theory, when somebody makes a judgement (of a "target attribute") that is computationally complex, a more easily calculated "heuristic attribute" is substituted. In effect, a cognitively difficult problem is dealt with by answering a rather simpler problem, without being aware of this happening.[18] This theory explains cases where judgements fail to show regression toward the mean.[19] Heuristics can be considered to reduce the complexity of clinical judgments in health care.[20]
Psychology
Philosophy
A 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
Heuristic is also often used as a noun to describe a rule of thumb, procedure, or method.[31] Philosophers of science have emphasised the importance of heuristics in creative thought and the construction of scientific theories.[32] Seminal works include Karl Popper's The Logic of Scientific Discovery and others by Imre Lakatos,[33] Lindley Darden, and William C. Wimsatt.
Law
In
The present securities regulation regime largely assumes that all investors act as perfectly rational persons. In truth, actual investors face cognitive limitations from biases, heuristics, and framing effects. For instance, in all states in the United States the legal drinking age for unsupervised persons is 21 years, 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 delineation is used because it is impossible or impractical to tell whether an individual is sufficiently mature for society to 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 put youth alcohol policy more on a case-by-case basis 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
Stereotyping
Stereotyping is a type of heuristic that people use to form opinions or make judgements about things they have never seen or experienced.[36] They work as a mental shortcut to assess everything from the social status of a person (based on their actions),[2] to classifying a plant as a tree based on it being tall, having a trunk, and that it has leaves (even though the person making the evaluation might never have seen that particular type of tree before).
Stereotypes, as first described by journalist Walter Lippmann in his book Public Opinion (1922), are the pictures we have in our heads that are built around experiences as well as what we are told about the world.[37][38]
Artificial intelligence
A
Behavioural economics
Heuristics refers to the cognitive shortcuts that individuals use to simplify decision-making processes in economic situations. Behavioral economics is a field that integrates insights from psychology and economics to better understand how people make decisions.
Anchoring and adjustment is one of the most extensively researched heuristics in behavioural economics. Anchoring is the tendency of people to make future judgements or conclusions based too heavily on the original information supplied to them. This initial knowledge functions as an anchor, and it can influence future judgements even if the anchor is entirely unrelated to the decisions at hand. Adjustment, on the other hand, is the process through which individuals make gradual changes to their initial judgements or conclusions.
Other heuristics studied in behavioral economics include the representativeness heuristic, which refers to the tendency of individuals to categorize objects or events based on how similar they are to typical examples,[39] and the availability heuristic, which refers to the tendency of individuals to judge the likelihood of an event based on how easily it comes to mind.[40]
Types
Availability heuristic
According to Tversky and Kahneman (1973), the availability heuristic can be described as the tendency to consider events that they can remember with greater facilitation as more likely to occur than events that are more difficult to recall.[41]
Representative heuristic
The representativeness heuristic refers to the cognitive bias where people rely on their preconceived mental image/prototype of a particular category or concept rather than actual probabilities and statistical data for making judgments. This behavior often leads to stereotyping/generalization with limited information causing errors as well as distorted views about reality.[42]
For instance, when trying to guess someone's occupation based on their appearance, a representative heuristic might be used by assuming that an individual in a suit must be either a lawyer or businessperson while assuming that someone in uniform fits the police officer or soldier category. This shortcut could sometimes be useful but may also result in stereotypes and overgeneralizations.
See also
- Algorithm
- Behavioral economics
- Failure mode and effects analysis
- Heuristics in judgment and decision-making
- Ideal type
- List of biases in judgment and decision making
- Neuroheuristics
- Predictive coding
- Priority heuristic
- Social heuristics
- Thought experiment
References
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Further reading
- How To Solve It: Modern Heuristics, Zbigniew Michalewicz and David B. Fogel, Springer Verlag, 2000. ISBN 3-540-66061-5
- LCCN 20190474.
- The Problem of Thinking Too Much, 11 December 2002, Persi Diaconis