Editing Cognitive bias (section)

== Overview ==
The notion of cognitive biases was introduced by [[Amos Tversky]] and [[Daniel Kahneman]] in 1972<ref>{{cite book | vauthors = Kahneman D, Frederick S |chapter=Representativeness Revisited: Attribute Substitution in Intuitive Judgment | veditors = Gilovich T, Griffin DW, Kahneman D |title=Heuristics and Biases: The Psychology of Intuitive Judgment |publisher=Cambridge University Press |location=Cambridge |year=2002 |pages=51–52 |isbn=978-0-521-79679-8}}</ref> and grew out of their experience of people's ''[[Numeracy#Innumeracy and dyscalculia|innumeracy]]'', or inability to reason intuitively with the greater [[orders of magnitude]]. Tversky, Kahneman, and colleagues demonstrated several [[reproducibility|replicable]] ways in which human judgments and decisions differ from [[rational choice theory]]. Tversky and Kahneman explained human differences in judgment and decision-making in terms of heuristics. Heuristics involve mental shortcuts which provide swift estimates about the possibility of uncertain occurrences.<ref name=Baumeister2>{{cite book| vauthors = Baumeister RF, Bushman BJ |title=Social psychology and human nature: International Edition|year=2010|publisher=Wadsworth|location=Belmont, US|pages=141}}</ref> Heuristics are simple for the brain to compute but sometimes introduce "severe and systematic errors."<ref name="h_and_b" /> For example, the representativeness heuristic is defined as "The tendency to judge the frequency or likelihood" of an occurrence by the extent of which the event "resembles the typical case."<ref name="Baumeister2" />

The "Linda Problem" illustrates the representativeness heuristic (Tversky & Kahneman, 1983<ref>{{cite journal| vauthors = Tversky A, Kahneman D |title=Extensional versus intuitive reasoning: The conjunction fallacy in probability judgement|journal=Psychological Review|year=1983|volume=90|issue=4  |pages=293–315|doi=10.1037/0033-295X.90.4.293 |url=http://psy.ucsd.edu/%7Emckenzie/TverskyKahneman1983PsychRev.pdf |archive-url=https://web.archive.org/web/20070928091331/http://psy.ucsd.edu/~mckenzie/TverskyKahneman1983PsychRev.pdf |archive-date=2007-09-28 |url-status=live}}</ref>). Participants were given a description of "Linda" that suggests Linda might well be a feminist (e.g., she is said to be concerned about discrimination and social justice issues). They were then asked whether they thought Linda was more likely to be (a) a "bank teller" or (b) a "bank teller and active in the feminist movement." A majority chose answer (b). Independent of the information given about Linda, though, the more restrictive answer (b) is under any circumstance statistically less likely than answer (a). This is an example of the "[[conjunction fallacy]]". Tversky and Kahneman argued that respondents chose (b) because it seemed more "representative" or typical of persons who might fit the description of Linda. The representativeness heuristic may lead to errors such as activating stereotypes and inaccurate judgments of others (Haselton et al., 2005, p.&nbsp;726).

Critics of Kahneman and Tversky, such as [[Gerd Gigerenzer]], alternatively argued that heuristics should not lead us to conceive of human thinking as riddled with irrational cognitive biases. They should rather conceive [[rationality]] as an adaptive tool, not identical to the rules of [[formal logic]] or the [[probability calculus]].<ref>{{cite book | vauthors = Gigerenzer G |chapter=Bounded and Rational | veditors = Stainton RJ |title=Contemporary Debates in Cognitive Science |publisher=Blackwell |year=2006 |page=129 |isbn=978-1-4051-1304-5 }}</ref> Nevertheless, experiments such as the "Linda problem" grew into heuristics and biases research programs, which spread beyond academic psychology into other disciplines including medicine and [[political science]].

===Definitions===
{| class="wikitable"
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! Definition !! Source 
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| ''"bias ... that occurs when humans are processing and interpreting information"'' 
|| ISO/IEC TR 24027:2021(en), 3.2.4,<ref>{{cite ISO standard |csnumber=77607 |title=ISO/IEC TR 24027:2021 Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making ||section=3.2.4 |date=2021 |publisher=[[International Organization for Standardization|ISO]] |access-date=21 June 2023}}</ref> ISO/IEC TR 24368:2022(en), 3.8<ref>{{cite ISO standard |csnumber=78507 |title=ISO/IEC TR 24368:2022 Information technology — Artificial intelligence — Overview of ethical and societal concerns ||section=3.8 |date=2022 |publisher=[[International Organization for Standardization|ISO]] |access-date=21 June 2023}}</ref>
|}