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A heuristic [1] or heuristic technique ( problem solving , mental shortcut , rule of thumb ) [2] [3] [4] [5] is any approach to problem solving that employs a pragmatic method that is not fully optimized, perfected, or rationalized, but is nevertheless "good enough" as an approximation or attribute substitution. [6] [7] Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. [8] [9] Heuristics can be mental shortcuts that ease the cognitive load of making a decision. [10] [11] [12]
Heuristic reasoning is often based on induction, or on analogy[.] [...] Induction is the process of discovering general laws [...] Induction tries to find regularity and coherence [...] Its most conspicuous instruments are generalization, specialization, analogy. [...] Heuristic discusses human behavior in the face of problems [...that have been] preserved in the wisdom of proverbs . [13]
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Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]
A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7). [15]
— S. Chow, "Many Meanings of 'Heuristic'", The British Journal for the Philosophy of Science
Heuristics are strategies based on rules to generate optimal decisions, like the anchoring effect and utility maximization problem. [16] These strategies depend on using readily accessible, though loosely applicable, information to control problem solving in human beings, machines and abstract issues. [17] [18] When an individual applies a heuristic in practice, it generally performs as expected. However it can alternatively create systematic errors. [19]
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.
Dual process theory concerns embodied heuristics. [20]
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. [21]
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Lakatosian heuristics is based on the key term: Justification (epistemology). [22]
One-reason decisions are algorithms that are made of three rules: search rules, confirmation rules (stopping), and decision rules [23] [24] [25]
A class that's function is to determine and filter out superfluous things. [33]
Tracking heuristics is a class of heuristics. [38]
Social heuristics – Decision-making processes in social environments [43]
George Polya studied and published on heuristics in 1945. [68] Polya (1945) cites Pappus of Alexandria as having written a text that Polya dubs Heuristic. [69] Pappus' heuristic problem-solving methods consist of analysis and synthesis [ disambiguation needed ]. [70]
The study of heuristics in human decision-making was developed in the 1970s and the 1980s, by the psychologists Amos Tversky and Daniel Kahneman, [82] 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. [83]
Rudolf Groner analysed the history of heuristics from its roots in ancient Greece up to contemporary work in cognitive psychology and artificial intelligence, [84] proposing a cognitive style "heuristic versus algorithmic thinking", which can be assessed by means of a validated questionnaire. [85]
The adaptive toolbox contains strategies for fabricating heuristic devices. [86] The core mental capacities are recall (memory), frequency, object permanence, and imitation. [87] 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. [88] 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. [89] 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. [90] This finding, known as a less-is-more effect [ disambiguation needed ], 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. [91]
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. [92] 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. [93]
In 2002, Daniel Kahneman and Shane Frederick proposed that cognitive heuristics work by a process called attribute substitution , which happens without conscious awareness. [94] 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. [94] This theory explains cases where judgements fail to show regression toward the mean. [95] Heuristics can be considered to reduce the complexity of clinical judgments in health care. [96]
A heuristic is stored in the memory. [97] Heuristics are inherently phenomenological, e.g., I and Thou . [98]
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 model that, 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 this 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 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 opted for certain principles and carried them through rigorously.
Heuristic is also often used as a noun to describe a rule of thumb, procedure, or method. [99] Philosophers of science have emphasised the importance of heuristics in creative thought and the construction of scientific theories. [100] Seminal works include Karl Popper's The Logic of Scientific Discovery and others by Imre Lakatos, [101] Lindley Darden, and William C. Wimsatt.
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. [102]
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 patent law. Patents are justified on the grounds that inventors must be protected so they have incentive to invent. It is therefore argued that it is in society's best interest that inventors receive a temporary government-granted monopoly on their idea, so that they can recoup investment costs and make economic profit for a limited period. In the United States, the length of this temporary monopoly is 20 years from the date the patent application 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 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. [103]
The bias–variance tradeoff gives insight into describing the less-is-more strategy. [104] A heuristic can be used in artificial intelligence systems while searching a solution space. The heuristic is derived by using some function that is put into the system by the designer, or by adjusting the weight of branches based on how likely each branch is to lead to a goal node.
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.
Anchoring and adjustment has been observed in a wide range of decision-making contexts, including financial decision-making, consumer behavior, and negotiation. Researchers have identified a number of strategies that can be used to mitigate the effects of anchoring and adjustment, including providing multiple anchors, encouraging individuals to generate alternative anchors, and providing cognitive prompts to encourage more deliberative decision-making.
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, [105] 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. [106]
Stereotyping is a type of heuristic that people use to form opinions or make judgements about things they have never seen or experienced. [107] They work as a mental shortcut to assess everything from the social status of a person (based on their actions), [12] 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. [108] [109]
A cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective reality" from their perception of the input. An individual's construction of reality, not the objective input, may dictate their behavior in the world. Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, and irrationality.
Bounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal.
Satisficing is a decision-making strategy or cognitive heuristic that entails searching through the available alternatives until an acceptability threshold is met. The term satisficing, a portmanteau of satisfy and suffice, was introduced by Herbert A. Simon in 1956, although the concept was first posited in his 1947 book Administrative Behavior. Simon used satisficing to explain the behavior of decision makers under circumstances in which an optimal solution cannot be determined. He maintained that many natural problems are characterized by computational intractability or a lack of information, both of which preclude the use of mathematical optimization procedures. He observed in his Nobel Prize in Economics speech that "decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science".
The representativeness heuristic is used when making judgments about the probability of an event being representional in character and essence of a known prototypical event. It is one of a group of heuristics proposed by psychologists Amos Tversky and Daniel Kahneman in the early 1970s as "the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated". The representativeness heuristic works by comparing an event to a prototype or stereotype that we already have in mind. For example, if we see a person who is dressed in eccentric clothes and reading a poetry book, we might be more likely to think that they are a poet than an accountant. This is because the person's appearance and behavior are more representative of the stereotype of a poet than an accountant.
The conjunction fallacy is an inference that a conjoint set of two or more specific conclusions is likelier than any single member of that same set, in violation of the laws of probability. It is a type of formal fallacy.
The recognition heuristic, originally termed the recognition principle, has been used as a model in the psychology of judgment and decision making and as a heuristic in artificial intelligence. The goal is to make inferences about a criterion that is not directly accessible to the decision maker, based on recognition retrieved from memory. This is possible if recognition of alternatives has relevance to the criterion. For two alternatives, the heuristic is defined as:
If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion.
Gerd Gigerenzer is a German psychologist who has studied the use of bounded rationality and heuristics in decision making. Gigerenzer is director emeritus of the Center for Adaptive Behavior and Cognition (ABC) at the Max Planck Institute for Human Development, Berlin, director of the Harding Center for Risk Literacy, University of Potsdam, and vice president of the European Research Council (ERC).
Daniel G. Goldstein is an American cognitive psychologist known for the specification and testing of heuristics and models of bounded rationality in the field of judgment and decision making. He is an honorary research fellow at London Business School and works with Microsoft Research as a principal researcher.
In psychology, the take-the-best heuristic is a heuristic which decides between two alternatives by choosing based on the first cue that discriminates them, where cues are ordered by cue validity. In the original formulation, the cues were assumed to have binary values or have an unknown value. The logic of the heuristic is that it bases its choice on the best cue (reason) only and ignores the rest.
The gaze heuristic falls under the category of tracking heuristics, and it is used in directing correct motion to achieve a goal using one main variable. McLeod & Dienes' (1996) example of the gaze heuristic is catching a ball.
In psychology, the human mind is considered to be a cognitive miser due to the tendency of humans to think and solve problems in simpler and less effortful ways rather than in more sophisticated and effortful ways, regardless of intelligence. Just as a miser seeks to avoid spending money, the human mind often seeks to avoid spending cognitive effort. The cognitive miser theory is an umbrella theory of cognition that brings together previous research on heuristics and attributional biases to explain when and why people are cognitive misers.
Heuristics is the process by which humans use mental shortcuts to arrive at decisions. Heuristics are simple strategies that humans, animals, organizations, and even machines use to quickly form judgments, make decisions, and find solutions to complex problems. Often this involves focusing on the most relevant aspects of a problem or situation to formulate a solution. While heuristic processes are used to find the answers and solutions that are most likely to work or be correct, they are not always right or the most accurate. Judgments and decisions based on heuristics are simply good enough to satisfy a pressing need in situations of uncertainty, where information is incomplete. In that sense they can differ from answers given by logic and probability.
The heuristic-systematic model of information processing (HSM) is a widely recognized model by Shelly Chaiken that attempts to explain how people receive and process persuasive messages.
The rhyme-as-reason effect, also known as the Eaton–Rosen phenomenon, is a cognitive bias where sayings or aphorisms are perceived as more accurate or truthful when they rhyme.
Heuristics are simple strategies for decision making that are used to achieve a specific goal quickly and efficiently, and are commonly implemented in sports. Many sports require the ability to make fast decisions under time pressure, and the proper use of heuristics is essential for many of these decisions.
Social heuristics are simple decision making strategies that guide people's behavior and decisions in the social environment when time, information, or cognitive resources are scarce. Social environments tend to be characterised by complexity and uncertainty, and in order to simplify the decision-making process, people may use heuristics, which are decision making strategies that involve ignoring some information or relying on simple rules of thumb.
Ecological rationality is a particular account of practical rationality, which in turn specifies the norms of rational action – what one ought to do in order to act rationally. The presently dominant account of practical rationality in the social and behavioral sciences such as economics and psychology, rational choice theory, maintains that practical rationality consists in making decisions in accordance with some fixed rules, irrespective of context. Ecological rationality, in contrast, claims that the rationality of a decision depends on the circumstances in which it takes place, so as to achieve one's goals in this particular context. What is considered rational under the rational choice account thus might not always be considered rational under the ecological rationality account. Overall, rational choice theory puts a premium on internal logical consistency whereas ecological rationality targets external performance in the world. The term ecologically rational is only etymologically similar to the biological science of ecology.
Fast-and-frugal treeormatching heuristic(in the study of decision-making) is a simple graphical structure that categorizes objects by asking one question at a time. These decision trees are used in a range of fields: psychology, artificial intelligence, and management science. Unlike other decision or classification trees, such as Leo Breiman's CART, fast-and-frugal trees are intentionally simple, both in their construction as well as their execution, and operate speedily with little information. For this reason, fast-and-frugal-trees are potentially attractive when designing resource-constrained tasks.
Intuitive statistics, or folk statistics, is the cognitive phenomenon where organisms use data to make generalizations and predictions about the world. This can be a small amount of sample data or training instances, which in turn contribute to inductive inferences about either population-level properties, future data, or both. Inferences can involve revising hypotheses, or beliefs, in light of probabilistic data that inform and motivate future predictions. The informal tendency for cognitive animals to intuitively generate statistical inferences, when formalized with certain axioms of probability theory, constitutes statistics as an academic discipline.
Ralph Hertwig is a German psychologist whose work focuses on the psychology of human judgment and decision making. Hertwig is Director of the Center for Adaptive Rationality at the Max Planck Institute for Human Development in Berlin, Germany. He grew up with his brothers Steffen Hertwig and Michael Hertwig in Talheim, Heilbronn.
heuriskein (ancient Greek) and heurisricus (Latin): 'to find out, discover.')
'guiding discovery' or 'improving problem solving' [...] its origin in ancient Greece where the verb 'heuriskein' means to find.
The word heuristic is taken directly from the Greek verb, heuriskein, 'to discover'. As a noun it is defined as 'a technique of discovery' and as an adjective, it means 'serving to guide, discover, or reveal'. The more common designation for all of this is 'the discovery method'.
The origin of the term goes back to the Ancient Greek verb heuriskein, which means 'to find out' or 'to discover.' Heuristics are sometimes also referred to as 'mental shortcuts' or 'rules of thumb.'
Not only is 'heuristic' used in diverse ways across and within disciplines, but its meaning has evolved over the years.
Kahneman & Frederick (2002) proposed that a heuristic assesses a target attribute by another property (attribute substitution) that comes more readily to mind.
A good error is a consequence of the adaptation of mental heuristics to the structure of environments. This ecological view is illustrated by visual illusions. Not making good errors would destroy human intelligence.
Heuristics are commonly understood as economical shortcut procedures that may not lead to optimal or correct results, but will generally produce outcomes that are in some sense satisfactory or 'good enough'.
Hence to paraphrase Polya, heuristic is a science of problem-solving behavior that focuses on plausible, provisional, useful, but fallible, mental operations for discovering solutions.
Shah & Oppenheimer (2008) proposed that all heuristics rely on effort reduction by one or more of the following: (a) examining fewer cues, (b) reducing the effort of retrieving cue values, (c) simplifying the weighting of cues, (d) integrating less information, and (e) examining fewer alternatives.
Heuristics are a subset of strategies; strategies also include complex regression or Bayesian models.
In a recent review article written with Wolfgang Gaissmaier, the following definition is proposed:
Another negative and substantial consequence was that computational models of heuristics, such as lexicographic rules (Fishburn, 1974) and elimination-by-aspects (Tversky, 1972), became replaced by one-word labels: availability, representativeness, and anchoring.
Gigerenzer (2021) [says] humans [and] other organisms evolved to acquire what he calls 'embodied heuristics' that can be both innate or learnt rules of thumb, which in turn supply the agility to respond to the lack of information by fast judgement. The 'embodied heuristics' use the mental capacity that includes the motor and sensory abilities that start to develop from the moment of birth. [...] 'dual-process theories' [...] we find it helpful to point out that one may distinguish between 'System 1 heuristics' [neuro] and 'System 2 heuristics' [neuro] (Kahneman 2011, p. 98).
As Popperians and Lakatosians use the term, a 'justificationist' theory of knowledge is one committed to the existence of foundations of knowledge, at least probabilistic foundations.
This stopping rule, termed a confirmation rule, works well in situations where (a) the decision maker knows little about the validity of the cues, and (b) the costs of cues are rather low (Karelaia, 2006).
One-reason decisions: a class of heuristics that bases judgments on one good reason only, ignoring other cues (e.g., take-the-best and hiatus heuristic)
Just as there is a class of such tracking heuristics, there is a class of one-good-reason heuristics, of which take-the-best is one member. These heuristics also have three building blocks: search rules, stopping rules, and decision rules.
TTB consists of three building blocks. (1) Search rule: Search through cues in the order of their validity, a measure of accuracy equal to the proportion of correct decisions made by a cue out of all the times that cue discriminates between pairs of options. (2) Stopping rule: Stop search as soon as one cue is found that discriminates between the two options. (3) Decision rule: Select the option to which the discriminating cue points, that is, the option that has the cue value associated with higher criterion values.
Take the best (Gigerenzer & Goldstein, 1996). Infer which of two alternatives has the higher value by (a) searching through cues in order of validity, (b) stopping the search as soon as a cue discriminates, (c) choosing the alternative this cue favors.
Take-the-best is a member of the one-good-reason family of heuristics because of its stopping rule: Search is stopped after finding the first cue that enables an inference to be made.
Wubben & Wangenheim (2008) reported that experienced managers use a simple recency-of-last-purchase rule: 'Hiatus heuristic: If a customer has not purchased within a certain number of months (the hiatus), the customer is classified as inactive; otherwise, the customer is classified as active.'
Default heuristic (Johnson & Goldstein, 2003). If there is a default, do nothing about it.
The priority heuristic, a one-good-reason heuristic with no free parameters (Brandstätter, Gigerenzer, & Hertwig, 2008; Brandstätter et al., 2006) that has similar building blocks to take-the-best, has been shown to imply (not just have parameter sets that are consistent with) several of the major violations simultaneously, including the Allais paradox and the fourfold pattern (Katsikopoulos & Gigerenzer, 2008).
Johnson & Raab (2003) proposed a variant of the fluency heuristic when alternatives are sequentially retrieved rather than simultaneously perceived: 'Take-the-first heuristic: Choose the first alternative that comes to mind.'
Recognition-based decisions: a class of heuristics that bases judgments on recognition information only, ignoring other cues (e.g., recognition and fluency heuristic)
For two alternatives, the heuristic is defined as (Goldstein & Gigerenzer 2002): 'Recognition heuristic: If one of two alternatives is recognized and the other is not, then infer that the recognized alternative has the higher value with respect to the criterion.'
Recognition heuristic (Goldstein & Gigerenzer, 2002). If one of two alternatives is recognized, infer that it has the higher value on the criterion.
Fluency heuristic (Schooler & Hertwig, 2005). If one alternative is recognized faster than another, infer that it has the higher value on the criterion.
'Fluency heuristic: If both alternatives are recognized but one is recognized faster, then infer that this alternative has the higher value with respect to the criterion.' The fluency heuristic builds on earlier work on fluency (Jacoby & Dallas 1981).
The gaze heuristic introduced earlier has three building blocks. [...] there is a class of such tracking heuristics[.]
Trade-offs: a class of heuristics that weights all cues or alternatives equally and thus makes trade-offs (e.g., tallying and 1/N)
Tallying (unit-weight linear model; Dawes, 1979). To estimate a criterion, do not estimate weights but simply count the number of favoring cues.
This also could be in accordance with the tallying heuristic where people count the number of arguments (for example, pros and cons) and disregard the relative importance of each argument (Bonnefon, Dubois, Fargier, & Leblois, 2008; Gigerenzer, 2004).
1/N; equality heuristic (DeMiguel et al., 2006). Allocate resources equally to each of N alternatives.
[Social heuristics] include imitation heuristics, tit-for-tat, the social-circle heuristic, and averaging the judgments of others to exploit the 'wisdom of crowds' (Hertwig & Herzog 2009). Imitate the-successful, for instance, speeds up learning of cue orders and can find orders that excel take-the-best's validity order (Garcia-Retamero et al. 2009).
Imitate the majority (Boyd & Richerson, 2005). Look at a majority of people in your peer group, and imitate their behavior. Imitate the successful (Boyd &Richerson, 2005). Look for the most successful person and imitate his or her behavior.
Tit-for-tat (Axelrod, 1984). Cooperate first, keep a memory of Size 1, and then imitate your partner's last behavior.
[I]f a person believes that audience consensus usually offers accurate guidance as to the merits of persuasive messages, then positive audience reaction to a specific message would prompt the individual to accept the speaker's claims. The cognitive heuristic is the holding that audience consensus in this case is representative of situations in which audience consensus provides a reliable guide (Axsom, Yates, and Chaiken, 1987).
Lozinski and Collinson (1999, as cited in Giugni, 2006) were the first to employ the concept of an 'epistemological shudder' to describe how one's preferred representations of one's known world can prove incapable of immediately making sense of the 'marvellous' (p. 101).
The first epistemic heuristic essential to mechanistic reasoning is that students think across scalar levels. Most definitions of mechanistic reasoning (e.g., Grotzer & Perkins, 2000; Machamer et al., 2000) use the term underlying to describe the kinds of things that must be identified and characterized in order to explain a target phenomenon.
second epistemic heuristic: identifying and characterizing relevant elements at a scalar level below that of the target phenomenon. [...] we use the term factor to refer generally to the relevant elements at the scalar level below that of the aggregate phenomenon. Similarly, we refer generally to the intellectual work involved in characterizing the relevant properties, rules, and behaviors of factors as unpacking those factors.
Finally, the third heuristic essential to mechanistic reasoning involves checking how well the underlying mechanisms fit the observed phenomenon.
The affect heuristic is one of the most common heuristics in individuals, and has been a popular topic in the study of behavioral finance (Finucane et al. 2000).
Adaptive heuristics commonly appear in behavioral models, such as reinforcement, feedback, and stimulus-response.
However, a different meaning of 'heuristic' was invoked in psychology with the Gestalt theorists, and later with Simon's notion of 'satisficing'.
Satisficing (Simon, 1955; Todd & Miller, 1999). Search through alternatives, and choose the first one that exceeds your aspiration level.
Simon's (1955) satisficing heuristic searches through options in any order, stops as soon the first option exceeds an aspiration level, and chooses this option.
[T]he representativeness heuristic[:]Probabilities are evaluated by the degree to which one thing or event is representative of (resembles) another; the higher the representativeness (resemblance) the higher the probability estimation[.]
The belief that a sequence such as 11111111111111111111 is less probable than a sequence such as 66234441536125563152 is often referred to as the representativeness heuristic (Kahneman and Tversky 1972; Shaughnessy 1977, 1992).
[T]he availability heuristic[:]The frequency of a class or the probability of an event is assessed according to the ease with which instances or associations can be brought to mind (Tversky and Kahneman [1974])
Max Wertheimer, who was a close friend of Einstein, and his fellow Gestalt psychologists spoke of heuristic methods such as 'looking around' to guide search for information.
In building social theory, Marx used not one (as generally regarded) but three heuristic models: base-superstructure, organic totality, and dialectical development.
The continuum limit heuristic is one member of a more general class of heuristics for variable reduction (Wilson [2007], pp. 184-92).
One of the political heuristics that has been most studied from an evolutionary perspective is the deservingness heuristic.[...] the deservingness heuristic is the psychological tendency of people to base their opinions about welfare programs on the efforts of the recipients. Specifically, the heuristic motivates people to support welfare benefits to recipients who are represented as victims of bad luck and reject benefits to recipients who are represented as lazy.
The even simpler Minimalist heuristic, which searches through available cues in a random order[.]
The focus on unification as a heuristic strategy parallels certain elements of a related type of reasoning, namely that found in robustness analysis.
As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question [...] 2. Bounding the domain of inquiry [...] 3. Selection of salient features [...] 4. Teleological description of the system [...] 5. Search for the optimal solution [...] 6. Empirical comparisons [...] 7. Further refinement of the model [...] 8. Generation of new hypotheses[.] [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application.
Minsky's (1961 b) subject bibliography lists Polya (1945) as the earliest reference to heuristic in the AI literature.
The methods of analysis and synthesis appear later in almost every treatise on problem-solving methods [from Pappus].
[I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer.
The most important work in heuristic teaching has been done by George Polya. His How To Solve It has been a best seller since its first printing in 1945-copies sold number in the hundreds of thousands. Complementary to How To Solve It are two other works, each in two volumes: Mathematical Discovery and Mathematics And Plausible Reasoning.
It is difficult to overstate the influence of Tversky and Kahneman's work and the so-called 'heuristics-and-biases research programme' that followed.
'To choose a ripe cantaloupe, press the spot on the candidate cantaloupe where it was attached to the plant and smell it; if the spot smells like the inside of a cantaloupe, it's probably ripe' (Pearl [1984])
'Start in the centre square when beginning a game of tic-tac-toe' (Dunbar [1998])
Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905.
Lakatos ([1965]) also adopted the term to characterize his methodology of scientific research programmes, which would lead researchers to either avoid or pursue certain lines of inquiry 'negative' and 'positive' heuristics, respectively).
Wimsatt's ([1980], [1981], [2006], [2007]) work on reductionist modelling strategies - also built upon Simon's programme of bounded rationality - provides an alternative starting point that is more useful for understanding the role that heuristics play in science.
In a series of papers beginning in 1980 and represented in his 2007 book, Bill Wimsatt analyzed a series of 'heuristics,' thought of as guides or 'rules of thumb,' which are employed when scientists proceed in a reductionist manner (1980, 2007).
In summary, Hodgkin and Huxley use heuristics in the Wimsatt sense, and the heuristics fall both into what Wimsatt calls reductionistic heuristics and also nonreductionistic heuristics.
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ignored (help)The collection of heuristics and building blocks an individual or a species has at its disposal for constructing heuristics, together with the core mental capacities that building blocks exploit, has been called the adaptive toolbox (Gigerenzer et al. 1999).
Core capacities include recognition memory, frequency monitoring, object tracking, and the ability to imitate.
[T]he heuristic mode is constrained by basic principles of knowledge activation and use—namely, availability, accessibility, and applicability (e.g., Higgins, 1996). That is, heuristic processing requires that heuristics are stored in memory (i.e., available), are retrieved from memory (i.e., accessible), and are relevant (i.e., applicable) to the judgmental task at hand.
Heuristics is a qualitative model of research design developed from humanistic psychology traditions. It embraces the significance of human experience and embodies the spirit of Buber's (1958) "I-Thou" mutuality. The heuristic model is inherently phenomenological in nature, and it seeks to uncover the meaning and essence of human experience from the frame of reference of the experiencing person.
This 'bias-variance dilemma' helps to explicate the rationality of simple heuristics and how less can be more (Brighton & Gigerenzer 2008, Gigerenzer & Brighton 2009).
Rather, as rules, heuristics are procedures that can be specified and applied in a given situation.