Thinking, Fast and Slow (57 page)

effortful task at the same time
: Rainer Greifeneder and Herbert Bless, “Relying on Accessible Content Versus Accessibility Experiences: The Case of Processing Capacity,”
Social Cognition
25 (2007): 853–81.

happy episode in their life
: Markus Ruder and Herbert Bless, “Mood and the Reliance on the Ease of Retrieval Heuristic,”
Journal of Personality and Social Psychology
85 (2003): 20–32.

low on a depression scale
: Rainer Greifeneder and Herbert Bless, “Depression and Reliance on Ease-of-Retrieval Experiences,”
European Journal of Social Psychology
38 (2008): 213–30.

knowledgeable novices
: Chezy Ofir et al., “Memory-Based Store Price Judgments: The Role of Knowledge and Shopping Experience,”
Journal of Retailing
84 (2008): 414–23.

true experts
: Eugene M. Caruso, “Use of Experienced Retrieval Ease in Self and Social Judgments,”
Journal of Experimental Social Psychology
44 (2008): 148–55.

faith in intuition
: Johannes Keller and Herbert Bless, “Predicting Future Affective States: How Ease of Retrieval and Faith in Intuition Moderate the Impact of Activated Content,”
European Journal of Social Psychology
38 (2008): 1–10.

if they are…powerful
: Mario Weick and Ana Guinote, “When Subjective Experiences Matter: Power Increases Reliance on the Ease of Retrieval,”
Journal of Personality and Social Psychology
94 (2008): 956–70.

13: Availability, Emotion, and Risk

because of brain damage
: Damasio’s idea is known as the “somatic marker hypothesis” and it has gathered substantial support: Antonio R. Damasio,
Descartes’ Error: Emotion, Reason, and the Human Brain
(New York: Putnam, 1994). Antonio R. Damasio, “The Somatic Marker Hypothesis and the Possible Functions of the Prefrontal Cortex,”
Philosophical Transactions: Biological Sciences
351 (1996): 141–20.

risks of each technology
: Finucane et al., “The Affect Heuristic in Judgments of Risks and Benefits.” Paul Slovic, Melissa Finucane, Ellen Peters, and Donald G. MacGregor, “The Affect Heuristic,” in Thomas Gilovich, Dale Griffin, and Daniel Kahneman, eds.,
Heuristics and Biases
(New York: Cambridge University Press, 2002), 397–420. Paul Slovic, Melissa Finucane, Ellen Peters, and Donald G. MacGregor, “Risk as Analysis and Risk as Feelings: Some Thoughts About Affect, Reason, Risk, and Rationality,”
Risk Analysis
24 (2004): 1–12. Paul Slovic, “Trust, Emotion, Sex, Politics, and Science: Surveying the Risk-Assessment Battlefield,”
Risk Analysis
19 (1999): 689–701.

British Toxicology Society
: Slovic, “Trust, Emotion, Sex, Politics, and Science.” The technologies and substances used in these studies are not alternative solutions to the same problem. In realistic problems, where competitive solutions are considered, the correlation between costs and benefits must be negative; the solutions that have {ns problems,the largest benefits are also the most costly. Whether laypeople and even experts might fail to recognize the correct relationship even in those cases is an interesting question.

“wags the rational dog”
: Jonathan Haidt, “The Emotional Dog and Its Rational Tail: A Social Institutionist Approach to Moral Judgment,”
Psychological Review
108 (2001): 814–34.

“‘Risk’ does not exist”
: Paul Slovic,
The Perception of Risk
(Sterling, VA: EarthScan, 2000).

availability cascade: Timur Kuran and Cass R. Sunstein, “Availability Cascades and Risk Regulation,”
Stanford Law Review
51 (1999): 683–768.
CERCLA
, the Comprehensive Environmental Response, Compensation, and Liability Act, passed in 1980.

nothing in between
: Paul Slovic, who testified for the apple growers in the Alar case, has a rather different view: “The scare was triggered by the CBS
60 Minutes
broadcast that said 4, 000 children will die of cancer (no probabilities there) along with frightening pictures of bald children in a cancer ward—and many more incorrect statements. Also the story exposed EPA’s lack of competence in attending to and evaluating the safety of Alar, destroying trust in regulatory control. Given this, I think the public’s response was rational.” (Personal communication, May 11, 2011.)

14: Tom W’s Specialty

“a shy poetry lover”
: I borrowed this example from Max H. Bazerman and Don A. Moore,
Judgment in Managerial Decision Making
(New York: Wiley, 2008).

always weighted more
: Jonathan St. B. T. Evans, “Heuristic and Analytic Processes in Reasoning,”
British Journal of Psychology
75 (1984): 451–68.

the opposite effect
: Norbert Schwarz et al., “Base Rates, Representativeness, and the Logic of Conversation: The Contextual Relevance of ‘Irrelevant’ Information,”
Social Cognition
9 (1991): 67–84.

told to frown
: Alter, Oppenheimer, Epley, and Eyre, “Overcoming Intuition.”

Bayes’s rule
: The simplest form of Bayes’s rule is in odds form, posterior odds = prior odds × likelihood ratio, where the posterior odds are the odds (the ratio of probabilities) for two competing hypotheses. Consider a problem of diagnosis. Your friend has tested positive for a serious disease. The disease is rare: only 1 in 600 of the cases sent in for testing actually has the disease. The test is fairly accurate. Its likelihood ratio is 25:1, which means that the probability that a person who has the disease will test positive is 25 times higher than the probability of a false positive. Testing positive is frightening news, but the odds that your friend has the disease have risen only from 1/600 to 25/600, and the probability is 4%.

For the hypothesis that Tom W is a computer scientist, the prior odds that correspond to a base rate of 3% are (.03/. 97 = .031). Assuming a likelihood ratio of 4 (the description is 4 times as likely if Tom W is a computer scientist than if he is not), the posterior odds are 4 × . 031 = 12.4. From these odds you can { odes as l compute that the posterior probability of Tom W being a computer scientist is now 11% (because 12.4/112. 4 = .11).

15: Linda: Less is More

the role of heuristics
: Amos Tversky and Daniel Kahneman, “Extensional Versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment,”
Psychological Review
90(1983), 293-315.

“a little homunculus”
: Stephen Jay Gould,
Bully for Brontosaurus
(New York: Norton, 1991).

weakened or explained
: See, among others, Ralph Hertwig and Gerd Gigerenzer, “The ‘Conjunction Fallacy’ Revisited: How Intelligent Inferences Look Like Reasoning Errors,”
Journal of Behavioral Decision Making
12 (1999): 275–305; Ralph Hertwig, Bjoern Benz, and Stefan Krauss, “The Conjunction Fallacy and the Many Meanings of And,”
Cognition
108 (2008): 740–53.

settle our differences
: Barbara Mellers, Ralph Hertwig, and Daniel Kahneman, “Do Frequency Representations Eliminate Conjunction Effects? An Exercise in Adversarial Collaboration,”
Psychological Science
12 (2001): 269–75.

16: Causes Trump Statistics

correct answer is 41%
: Applying Bayes’s rule in odds form, the prior odds are the odds for the Blue cab from the base rate, and the likelihood ratio is the ratio of the probability of the witness saying the cab is Blue if it is Blue, divided by the probability of the witness saying the cab is Blue if it is Green: posterior odds = (.15/.85) × (.80/.20) = .706. The odds are the ratio of the probability that the cab is Blue, divided by the probability that the cab is Green. To obtain the probability that the cab is Blue, we compute: Probability (Blue) = .706/1. 706 = .41. The probability that the cab is Blue is 41%.

not too far from the Bayesian
: Amos Tversky and Daniel Kahneman, “Causal Schemas in Judgments Under Uncertainty,” in
Progress in Social Psychology
, ed. Morris Fishbein (Hillsdale, NJ: Erlbaum, 1980), 49–72.

University of Michigan
: Richard E. Nisbett and Eugene Borgida, “Attribution and the Psychology of Prediction,”
Journal of Personality and Social Psychology
32 (1975): 932–43.

relieved of responsibility
: John M. Darley and Bibb Latane, “Bystander Intervention in Emergencies: Diffusion of Responsibility,”
Journal of Personality and Social Psychology
8 (1968): 377–83.

17: Regression to the Mean

help of the most brilliant statisticians
: Michael Bulmer,
Francis Galton: Pioneer of Heredity and Biometry
(Baltimore: Johns Hopkins University Press, 2003).

standard scores: Researchers transform each original score into a standard score by subtracting the mean and dividing the result by the standard deviation. Standard scores have a mean of zero and a standard deviation of 1, can be compared across variables (especially when the statistica {he deviatiol distributions of the original scores are similar), and have many desirable mathematical properties, which Galton had to work out to understand the nature of correlation and regression.

correlation between parent and child
: This will not be true in an environment in which some children are malnourished. Differences in nutrition will become important, the proportion of shared factors will diminish, and with it the correlation between the height of parents and the height of children (unless the parents of malnourished children were also stunted by hunger in childhood).

height and weight
: The correlation was computed for a very large sample of the population of the United States (the Gallup-Healthways Well-Being Index).

income and education
: The correlation appears impressive, but I was surprised to learn many years ago from the sociologist Christopher Jencks that if everyone had the same education, the inequality of income (measured by standard deviation) would be reduced only by about 9%. The relevant formula is v (1–r
²
), where
r
is the correlation.

correlation and regression
: This is true when both variables are measured in standard scores—that is, where each score is transformed by removing the mean and dividing the result by the standard deviation.

confusing mere correlation with causation
: Howard Wainer, “The Most Dangerous Equation,”
American Scientist
95 (2007): 249–56.

18: Taming Intuitive Predictions

far more moderate
: The proof of the standard regression as the optimal solution to the prediction problem assumes that errors are weighted by the squared deviation from the correct value. This is the least-squares criterion, which is commonly accepted. Other loss functions lead to different solutions.

19: The Illusion of Understanding

narrative fallacy: Nassim Nicholas Taleb,
The Black Swan
:
The Impact of the Highly Improbable
(New York: Random House, 2007).

one attribute that is
particularly significant
:.

throwing the ball
: Michael Lewis,
Moneyball: The Art of Winning an Unfair Game
(New York: Norton, 2003).

sell their company
: Seth Weintraub, “Excite Passed Up Buying Google for $750,000 in 1999,”
Fortune
, September 29, 2011.

ever felt differently
: Richard E. Nisbett and Timothy D. Wilson, “Telling More Than We Can Know: Verbal Reports on Mental Processes,”
Psychological Review
84 (1977): 231–59.

United States and the Soviet Union
: Baruch Fischhoff and Ruth Beyth, “I Knew It Would Happen: Remembered Probabilities of Once Future Things,”
Organizational Behavior and Human Performance
13 (1975): 1–16.

quality of a decision
: Jonathan Baron and John C. Hershey, “Outcome Bias in Decision {s iiv> Evaluation,”
Journal of Personality and Social Psychology
54 (1988): 569–79.

should have hired the monitor
: Kim A. Kamin and Jeffrey Rachlinski, “Ex Post? Ex Ante: Determining Liability in Hindsight,”
Law and Human Behavior
19 (1995): 89–104. Jeffrey J. Rachlinski, “A Positive Psychological Theory of Judging in Hindsight,”
University of Chicago Law Review
65 (1998): 571–625.

tidbit of intelligence
: Jeffrey Goldberg, “Letter from Washington: Woodward vs. Tenet,”
New Yorker
, May 21, 2007, 35–38. Also Tim Weiner,
Legacy of Ashes: The History of the CIA
(New York: Doubleday, 2007); “Espionage: Inventing the Dots,”
Economist
, November 3, 2007, 100.