Linda problem

Priceless: The Myth of Fair Value (And How to Take Advantage of It)

by William Poundstone  · 1 Jan 2010  · 519pp  · 104,396 words

. Tversky and Kahneman resorted to “a series of increasingly desperate manipulations” intended to get their subjects to obey simple logic. They tried giving volunteers the Linda problem, followed by two arguments about what the answer should be. The subjects didn’t have to commit to an answer, just to say which argument

Rationality: What It Is, Why It Seems Scarce, Why It Matters

by Steven Pinker  · 14 Oct 2021  · 533pp  · 125,495 words

later, national borders have barely budged. The conjunction fallacy was first illustrated by Tversky and Kahneman with an example that has become famous as “the Linda problem”:50 Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of

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today, highly intelligent Amanda who marches for Black Lives Matter is still deemed likelier to be a feminist registered nurse than a registered nurse. The Linda problem engages our intuitions in a particularly compelling way. Unlike the selection task, where people make errors when the problem is abstract (“If P then Q

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appearance of truth, or likelihood of being realized, which any statement or event bears in the light of present evidence.”54 People faced with the Linda problem know that “frequency in the long run” is irrelevant: there’s only one Linda, and either she is a feminist bank teller or she isn

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matter, and invite an arbiter to join them in carrying it out.58 Fittingly, Kahneman collaborated with Hertwig to see who was right about the Linda problem, recruiting the psychologist Barbara Mellers to act as arbiter. The team of rivals agreed to run three studies that couched the problem in frequencies (“Of

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it means.”7 More accurately, different people have different notions of what it means, as we saw in chapter 1 with the Monty Hall and Linda problems.8 There is the classical definition of probability, which goes back to the origins of probability theory as a way of understanding games of chance

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, 146–48 cognitive reflection, lack of, 8–10, 311 collider fallacy, 261, 262–63 confirmation bias, 13–14, 142–43, 216, 290, 342n26 conjunction fallacy (Linda problem), 26–29, 115, 116, 156 correlation implies causation, 245–47, 251–52, 312, 321, 323–24, 329–30 data snooping, 145–46, 160 denying the

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, 342n26 conflicts of interest, 93 confounding (epiphenomena), 246, 257, 260, 263, 265, 267–68, 270–71 conjunction fallacy definition, 25 in forecasting, 23–26, 343n46 Linda problem, 26–29, 115, 116, 156 relative frequency and, 28–29 conjunction of events, probability of, 128–32, 137 connectionist nets. See deep learning conservation, the

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, 135 likelihood (technical term), 152–54, 349n4, 351n6, 352n21 and statistical significance, 224–25 See also under Bayesian reasoning Lilienfeld, Scott, 90 Lincoln, Abraham, 144 Linda problem, 26–29, 115, 116, 156 Linnaean categorization, 102, 108 Locke, John, 335–36 Loftus, Elizabeth, 216 logic connector words, 75–76 vs. content, 14–16

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of, 224–26 statistical power and, 223 Type I & II errors, 223–24, 225 stereotypes base-rate neglect and, 155–56 in the conjunction fallacy (Linda problem), 156 failures of critical thinking, 19–20, 27 family resemblance categories and, 99–100, 105, 155 illusory correlation and, 251–52 vs. propositional reasoning, 108

The Science of Fear: How the Culture of Fear Manipulates Your Brain

by Daniel Gardner  · 23 Jun 2009  · 542pp  · 132,010 words

more likely that Linda is a bank teller and a feminist than a bank teller only. Kahneman and Tversky also put both versions of the “Linda problem,” as they called it, under the noses of experts trained in logic and statistics. When the experts answered the original question, with its long list

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Rule of Typical Things an effective way to simplify complex situations and come up with reliable snap judgments. Or at least, it usually is. The Linda problem demonstrates one way the Rule of Typical Things can go wrong. When there’s something “typical” involved, our intuition is triggered. It just feels right

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future. If anyone could be expected to judge the chances of things happening rationally, it was this bunch. The psychologists gave a version of the “Linda problem” to two groups, totaling 115 experts. The first group was asked to evaluate the probability of “a complete suspension of diplomatic relations between the USA

Fancy Bear Goes Phishing: The Dark History of the Information Age, in Five Extraordinary Hacks

by Scott J. Shapiro  · 523pp  · 154,042 words

one toss). Similarly, the probability that Linda is a feminist bank teller cannot be greater than the probability that Linda is a bank teller. The Linda problem, first formulated by the Israeli psychologists Daniel Kahneman and Amos Tversky, is perhaps the most famous example of human violations of the basic rules of

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; cybersecurity threat relation to human; dual-process theories and; Fancy Bear exploitation of; of hackers; of helplessness to cybercrime; Kahneman and Tversky studies on human; Linda problem on probability and; Loss Aversion Heuristic in; Representativeness Heuristic and; of virus writers Putin, Vladimir Radai, Yisrael ransomware attacks Rasch, Mark rational/irrational choices Reagan

The Undoing Project: A Friendship That Changed Our Minds

by Michael Lewis  · 6 Dec 2016  · 336pp  · 113,519 words

in the feminist movement than simply a bank teller. With something of a heavy heart, Danny put what would come to be known as the Linda problem to a class of a dozen students at the University of British Columbia. “Twelve out of twelve fell for it,” he said. “I remember I

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still insisted that Linda was more likely to be a bank teller in the feminist movement than she was to be a bank teller. The Linda problem resembled a Venn diagram of two circles, but with one of the circles wholly contained by the other. But people didn’t see the circles

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of logic?” he asked. “So what!” a young woman shouted from the back of the room. “You just asked for my opinion!” They put the Linda problem in different ways, to make sure that the students who served as their lab rats weren’t misreading its first line as saying “Linda is

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. And they showed all over again the power of the mental rules of thumb—these curious forces that they had curiously named “heuristics.” To the Linda problem Danny and Amos added another, from work they had done in the early 1970s in Jerusalem. In four pages of a novel (about 2,000

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ing. Those words were more available. People’s misjudgment of the problem was simply the availability heuristic in action. The paper was another hit.* “The Linda problem” and “the conjunction fallacy” entered the language. Danny had misgivings, however. The new work was jointly written but it was, he said, “joint and painful

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he contrived not to feel anger toward their new German critic. He even found himself in some slight sympathy with Gigerenzer on one point: the Linda problem. Gigerenzer had shown that, by changing the simplest version of the problem, he could lead people to the correct answer. Instead of asking people to

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’d written as much, with less emphasis, in their original paper. At any rate, they’d always thought that the most outrageous version of the Linda problem was superfluous to the point they were making—that people judged by representativeness. The very first experiment, like their earlier work on human judgment, showed

Thinking, Fast and Slow

by Daniel Kahneman  · 24 Oct 2011  · 654pp  · 191,864 words

%"> Linda: Less Is More The best-known and most controversial of our experiments involved a fictitious lady called Linda. Amos and I made up the Linda problem to provide conclusive evidence of the role of heuristics in judgment and of their incompatibility with logic. This is how we described Linda: Linda is

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with a list of eight possible scenarios for Linda. As in the Tom W problem, some ranked the scenarios by representativeness, others by probability. The Linda problem is similar, but with a twist. Linda is a teacher in elementary school. Linda works in a bookstore and takes yoga classes. Linda is active

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illusion, the fallacy remains attractive even when you recognize it for what it is. The naturalist Stephen Jay Gould described his own struggle with the Linda problem. He knew the correct answer, of course, and yet, he wrote, “a little homunculus in my head continues to jump up and down, shouting at

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him in insistent tones. (The two-system terminology had not yet been introduced when he wrote.) The correct answer to the short version of the Linda problem was the majority response in only one of our studies: 64% of a group of graduate students in the social sciences at Stanford and at

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the substantial minority (36%) of this knowledgeable group who chose incorrectly. The judgments of probability that our respondents offered, in both the Tom W and Linda problems, corresponded precisely to judgments of representativeness (similarity to stereotypes). Representativeness belongs to a cluster of closely related basic assessments that are likely to be generated

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is more probable? Jane is a teacher. Jane is a teacher and walks to work. The two questions have the same logical structure as the Linda problem, but they cause no fallacy, because the more detailed outcome is only more detailed—it is not more plausible, or more coherent, or a better

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of a collection of baseball cards is a sum-like variable. Adding a positively valued item to the set can only increase its value. The Linda problem and the dinnerware problem have exactly the same structure. Probability, like economic value, is a sum-like variable, as illustrated by this example: probability (Linda

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) = probability (Linda is feminist teller) + probability (Linda is non-feminist teller) This is also why, as in Hsee’s dinnerware study, single evaluations of the Linda problem produce a less-is-more pattern. System 1 averages instead of adding, so when the non-feminist bank tellers are removed from the set, subjective

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recognized in the “how many?” representation, but it was not apparent to the thousands of people who have committed the conjunction fallacy in the original Linda problem and in others like it. In all these cases, the conjunction appeared plausible, and that sufficed for an endorsement of System 2. The laziness of

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the logical rule that more dishes can only add value. Intuition governs judgments in the between-subjects condition; logic rules in joint evaluation. In the Linda problem, in contrast, intuition often overcame logic even in joint evaluation, although we identified some conditions in which logic prevails. Amos and I believed that the

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the power of judgment heuristics, and that they would persuade doubters. And in this we were quite wrong. Instead, the Linda problem became a case study in the norms of controversy. The Linda problem attracted a great deal of attention, but it also became a magnet for critics of our approach to judgment. As

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had already done, researchers found combinations of instructions and hints that reduced the incidence of the fallacy; some argued that, in the context of the Linda problem, it is reasonable for subjects to understand the word “probability” as if it means “plausibility.” These arguments were sometimes extended to suggest that our entire

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not challenged—it was simply not addressed, and its salience was diminished by the exclusive focus on the conjunction fallacy. The net effect of the Linda problem was an increase in the visibility of our work to the general public, and a small dent in the credibility of our approach among scholars

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of bias in human judgment is a large issue. Some years ago I had a friendly conversation with Ralph Hertwig, a persistent critic of the Linda problem, with whom I had collaborated in a vain attempt to settle our differences. I asked him why he and others had chosen to focus exclusively

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; malpractice; outcome bias in leisure time less-is-more pattern Lewis, Michael libertarian policies Lichtenstein, Sarah life: evaluation of; stories in; satisfaction in; thinking about Linda problem List, John loans logarithmic functions loss aversion; in animals; enhanced; goals as reference points in; in legal decisions; status quo and loss aversion ratio losses

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%"> Princeton University probability; base rates in, see base rates; decision weights and, see decision weights; definitions of; and disciplining intuition; less-is-more pattern and; Linda problem and; overestimation of; plausibility and; and predicting by representativeness; prior, insensitivity to; professional stereotypes and; of rare events, see rare events; representativeness and, see representativeness

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in Hereditary Stature” (Galton) regret religion remembering self Remote Association Test (RAT) reorganizations in companies repetition representativeness; base rates and; see also base rates; in Linda problem; predicting by; professional stereotypes and; sins of; in Tom W problem research: artifacts in; hypothesis testing in; optimism in resemblance; in predictions resilience responsibility retrievability

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

by Robert Elliott Smith  · 26 Jun 2019  · 370pp  · 107,983 words

way I passed a group of people talking in a circle of metal chairs in the main hall. They were discussing the ‘Linda Problem’. Dammit! The ‘Kill Decision’ session, sadly, would have to wait, as the ‘Linda Problem’ was one of my personal bug bears, and I couldn’t resist taking a chair. The

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‘Linda Problem’ was introduced in 1983 by psychologists Daniel Kahneman and Amos Tversky,1 at the headwaters of the field of behavioural economics, which would eventually result

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reason ‘correctly’ relative to probability theory. Maybe it was the coffee (I had another cup during my session), but at that moment at SCIFOO, the ‘Linda Problem’ represented everything that I thought was going wrong with AI, and I launched in crusader style. I interrupted, and asked ‘What if the fallacy in

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question “incorrectly”, but in the people asking the question?’ That got me the floor, so I continued, explaining that what the people who pose the ‘Linda Problem’ think they are presenting is a well-structured problem in probability theory, which has a ‘correct’ answer. But what they are actually doing is asking

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was headed. He continued, saying that there is always a set of variables whose probabilities, correctly characterized, define correct answers to any situation, including the ‘Linda Problem’. This must be the case, as probability is the correct representation of the uncertain world around us, and statistics are the only basis from which

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vantage point we can learn new things about our human doughnut, in contrast to the AI hole. Experiments involving the thoughts of people, like the ‘Linda Problem’, show us that people do not reason with tractable, rote rules and probabilities like computers. They do not optimize (at either an individual or a

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, Alice, here Lee, William, here Leibniz, Gottfried, here, here, here, here, here, here, here Leigh, Augusta, here Levi-Strauss, Claude, here Lighthill, Sir James, here Linda Problem, The, here, here LinkedIn, here, here Llull, Ramon, here, here, here, here, here, here, here, here logistic equation, here longitude problem, here, here, here Lovelace

Radical Uncertainty: Decision-Making for an Unknowable Future

by Mervyn King and John Kay  · 5 Mar 2020  · 807pp  · 154,435 words

answer to such a question is a knave (and anyone who accepts them a fool). You will wind up with an earful of cider. The ‘Linda problem’ is one of the most frequently reported experiments in behavioural economics. In his bestseller Thinking, Fast and Slow , Daniel Kahneman describes it thus: ‘Linda is

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Sky Masterson’s father had conveyed to his son, and it is one well understood by Kahneman’s respondents. People do not think of the Linda problem in terms of frequencies, or as an exercise in probabilistic reasoning. They see the description of Linda as a story about a real person, and

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–7 , 175 , 393 , 442 ; dual systems of, 170–1 , 172 , 271 ; experiments with Tversky, 141–7 , 152 , 215 ; and the invisible gorilla, 140 ; and the Linda problem, 90–1 ; Thinking, Fast and Slow , 90 Kalahari Bushmen, 219–20 Katrina, Hurricane (2005), 426–7 Kay, John: The British Tax System (with Mervyn King

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, 87 ; and the law, 196 , 197 , 198–203 , 206–7 , 210–12 , 214 ; and likelihood, 86–7 , 89–91 , 96–7 , 206–7 , 403 ; the ‘Linda problem’, 90–1 , 98 ; and markets in risk, 55–7 ; models as contingent and transitory, 235–6 ; the Monty Hall problem, 62–3 , 64–6 , 98

Statistics hacks

by Bruce Frey  · 9 May 2006  · 755pp  · 121,290 words

and the incongruent options. Perhaps the most well-known problem that demonstrates the conjunction fallacy is the now-famous (at least in cognitive psychology circles) Linda Problem: 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

How the Mind Works

by Steven Pinker  · 1 Jan 1997  · 913pp  · 265,787 words

down with a purple rash died the following day. Gigerenzer, Cosmides, Tooby, and the psychologist Klaus Fiedler noticed that the medical decision problem and the Linda problem ask for single-event probabilities: how likely is that this patient is sick, how likely is it that Linda is a bankteller. A probability instinct

Licence to be Bad

by Jonathan Aldred  · 5 Jun 2019  · 453pp  · 111,010 words

The Great Mental Models: General Thinking Concepts

by Shane Parrish  · 22 Nov 2019  · 147pp  · 39,910 words

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets

by Nassim Nicholas Taleb  · 1 Jan 2001  · 111pp  · 1 words