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Representativeness Heuristic
The representativeness heuristic is used when making judgments about the probability of an event under uncertainty. It is one of a group of heuristics (simple rules governing judgment or decision-making) proposed by psychologists Amos Tversky and Daniel Kahneman in the early 1970s as "the degree to which n 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". Heuristics are described as "judgmental shortcuts that generally get us where we need to go – and quickly – but at the cost of occasionally sending us off course." Heuristics are useful because they use effort-reduction and simplification in decision-making. When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely. The representativeness heuristic is simply described as assessing similarity of ob ...
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Heuristics In Judgment And Decision-making
Heuristics is the process by which humans use mental short cuts 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 economist and cognitive psychologist Herbert A. Simon introduced the concept of heuristics in the 1950s, suggesting there were limitations to rational decision making. In the 1970s, psychologists Amos Tversky and Daniel ...
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Extension Neglect
__NOTOC__ Extension neglect is a type of cognitive bias which occurs when the sample size is ignored when its determination is relevant. For instance, when reading an article about a scientific study, extension neglect occurs when the reader ignores the number of people involved in the study (sample size) but still makes inferences about a population based on the sample. In reality, if the sample size is too small, the results might risk errors in statistical hypothesis testing. A study based on only a few people may draw invalid conclusions because only one person has exceptionally high or low scores (outlier), and there are not enough people there to correct this via averaging out. But often, the sample size is not prominently displayed in science articles, and the reader in this case might still believe the article's conclusion due to extension neglect. Extension neglect is described as being caused by judgment by prototype, of which the representativeness heuristic is a special ...
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List Of Biases In Judgment And Decision-making
Cognitive biases are systematic patterns of deviation from norm and/or rationality in judgment. They are often studied in psychology, sociology and behavioral economics. Although the reality of most of these biases is confirmed by reproducible research, there are often controversies about how to classify these biases or how to explain them. Several theoretical causes are known for some cognitive biases, which provides a classification of biases by their common generative mechanism (such as noisy information-processingMartin Hilbert (2012) "Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making"'. Psychological Bulletin, 138(2), 211–237; free access to the study here: https://www.martinhilbert.net/toward-a-synthesis-of-cognitive-biases/). Gerd Gigerenzer has criticized the framing of cognitive biases as errors in judgment, and favors interpreting them as arising from rational deviations from logical thought. Explanations include ...
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Availability Heuristic
The availability heuristic, also known as availability bias, is a mental shortcut that relies on immediate examples that come to a given person's mind when evaluating a specific topic, concept, method, or decision. This heuristic, operating on the notion that, if something can be recalled, it must be important, or at least more important than alternative solutions not as readily recalled, is inherently biased toward recently acquired information. The mental availability of an action's consequences is positively related to those consequences' perceived magnitude. In other words, the easier it is to recall the consequences of something, the greater those consequences are often perceived to be. Most notably, people often rely on the content of their recall if its implications are not called into question by the difficulty they have in recalling it. Overview and history In the late 1960s and early 1970s, Amos Tversky and Daniel Kahneman began work on a series of papers examining "heur ...
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Attribute Substitution
Attribute substitution is a psychological process thought to underlie a number of cognitive biases and perceptual illusions. It occurs when an individual has to make a judgment (of a ''target attribute'') that is computationally complex, and instead substitutes a more easily calculated ''heuristic attribute''. This substitution is thought of as taking place in the automatic ''intuitive'' judgment system, rather than the more self-aware ''reflective'' system. Hence, when someone tries to answer a difficult question, they may actually answer a related but different question, without realizing that a substitution has taken place. This explains why individuals can be unaware of their own biases, and why biases persist even when the subject is made aware of them. It also explains why human judgments often fail to show regression toward the mean. The theory of attribute substitution unifies a number of separate explanations of reasoning errors in terms of cognitive heuristics. In turn ...
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Affect Heuristic
The affect heuristic is a heuristic, a mental shortcut that allows people to make decisions and solve problems quickly and efficiently, in which current emotion—fear, pleasure, surprise, etc.—influences decisions. In other words, it is a type of heuristic in which emotional response, or " affect" in psychological terms, plays a lead role. It is a subconscious process that shortens the decision-making process and allows people to function without having to complete an extensive search for information. It is shorter in duration than a mood, occurring rapidly and involuntarily in response to a stimulus. Reading the words "lung cancer" usually generates an affect of dread, while reading the words "mother's love" usually generates a feeling of affection and comfort. The affect heuristic is typically used while judging the risks and benefits of something, depending on the positive or negative feelings that people associate with a stimulus. It is the equivalent of "going with your ...
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Logical Conjunction
In logic, mathematics and linguistics, And (\wedge) is the truth-functional operator of logical conjunction; the ''and'' of a set of operands is true if and only if ''all'' of its operands are true. The logical connective that represents this operator is typically written as \wedge or . A \land B is true if and only if A is true and B is true, otherwise it is false. An operand of a conjunction is a conjunct. Beyond logic, the term "conjunction" also refers to similar concepts in other fields: * In natural language, the denotation of expressions such as English "and". * In programming languages, the short-circuit and control structure. * In set theory, intersection. * In lattice theory, logical conjunction ( greatest lower bound). * In predicate logic, universal quantification. Notation And is usually denoted by an infix operator: in mathematics and logic, it is denoted by \wedge, or ; in electronics, ; and in programming languages, &, &&, or and. In Jan ...
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Maya Bar-Hillel
Maya Bar-Hillel ( he, מיה בר-הלל, born 1943) is a professor emeritus of psychology at the Hebrew University of Jerusalem. Known for her work on inaccuracies in human reasoning about probability, she has also studied decision theory in connection with Newcomb's paradox, investigated how gender stereotyping can block human problem-solving, and worked with Dror Bar-Natan, Gil Kalai, and Brendan McKay to debunk the Bible code. Education and career Bar-Hillel studied psychology with Amos Tversky at the Hebrew University, where she earned bachelor's and master's degrees in mathematics and a Ph.D. in psychology. Her 1975 doctoral dissertation, ''The Base-Rate Fallacy in Subjective Judgments of Probability'', introduced the concept of the base rate fallacy in probabilistic reasoning. At the Hebrew University, she was the director of the Center for the Study of Rationality from 2001 to 2005. Family Bar-Hillel is the daughter of Israeli philosopher and linguist Yehoshua Bar-Hillel. ...
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Base Rate
In probability and statistics, the base rate (also known as prior probabilities) is the class of probabilities unconditional on "featural evidence" (likelihoods). For example, if 1% of the population were medical professionals, and remaining 99% were ''not'' medical professionals, then the base rate of medical professionals will be 1%. The method for integrating base rates and featural evidence is given by Bayes' rule. In the sciences, including medicine, the base rate is critical for comparison. In medicine a treatment's effectiveness is clear when the base rate is available. For example if the control group, using no treatment at all, had their own base rate of 1/20 recoveries within 1 day (meaning 1 out of every 20 people recover in 1 day) and a treatment had a 1/100 base rate of recovery within 1 day, we see that the treatment actively decreases the recovery in the first day for the winter cold. Base rate fallacy A number of psychological studies have examined a phenom ...
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Bayes' Theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesia ...
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Conjunction Fallacy
The conjunction fallacy (also known as the Linda problem) is an inference from an array of particulars, in violation of the laws of probability, that a logical conjunction, conjoint set of two or more conclusions is likelier than any single member of that same set. It is a type of formal fallacy. Definition and basic example The most often-cited example of this fallacy originated with Amos Tversky and Daniel Kahneman. ''Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.'' Which is more probable? # Linda is a bank teller. # Linda is a bank teller and is active in the feminist movement. The majority of those asked chose option 2. However, the probability of two events occurring together (that is, in conjunction) is always less than or equal to the probability of either one occurring alone—formally, for ...
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