descriptions of two people: Solomon E. Asch, “Forming {#823.
Impressions of Personality,” Journal of Abnormal and Social Psychology 41 (1946): 258–90.
all six adjectives: Ibid.
Wisdom of Crowds: James Surowiecki, The Wisdom of Crowds (New York: Anchor Books, 2005).
one-sided evidence: Lyle A. Brenner, Derek J. Koehler, and Amos Tversky, “On the Evaluation of One-Sided Evidence,” Journal of Behavioral Decision Making 9 (1996): 59–70.
8: How Judgments Happen
biological roots: Alexander Todorov, Sean G. Baron, and Nikolaas N. Oosterhof, “Evaluating Face Trustworthiness: A Model-Based Approach,” Social Cognitive and Affective Neuroscience 3 (2008): 119–27.
friendly or hostile: Alexander Todorov, Chris P. Said, Andrew D. Engell, and Nikolaas N. Oosterhof, “Understanding Evaluation of Faces on Social Dimensions,” Trends in Cognitive Sciences 12 (2008): 455–60.
may spell trouble: Alexander Todorov, Manish Pakrashi, and Nikolaas N. Oosterhof, “Evaluating Faces on Trustworthiness After Minimal Time Exposure,” Social Cognition 27 (2009): 813–33.
Australia, Germany, and Mexico: Alexander Todorov et al., “Inference of Competence from Faces Predict Election Outcomes,” Science 308 (2005): 1623–26. Charles C. Ballew and Alexander Todorov, “Predicting Political Elections from Rapid and Unreflective Face Judgments,” PNAS 104 (2007): 17948–53. Christopher Y. Olivola and Alexander Todorov, “Elected in 100 Milliseconds: Appearance-Based Trait Inferences and Voting,” Journal of Nonverbal Behavior 34 (2010): 83–110.
watch less television: Gabriel Lenz and Chappell Lawson, “Looking the Part: Television Leads Less Informed Citizens to Vote Based on Candidates’ Appearance,” American Journal of Political Science (forthcoming).
absence of a specific task set: Amos Tversky and Daniel Kahneman, “Extensional Versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment,” Psychological Review 90 (1983): 293–315.
Exxon Valdez: William H. Desvousges et al., “Measuring Natural Resource Damages with Contingent Valuation: Tests of Validity and Reliability,” in Contingent Valuation: A Critical Assessment, ed. Jerry A. Hausman (Amsterdam: North-Holland, 1993), 91–159.
sense of injustice: Stanley S. Stevens, Psychophysics: Introduction to Its Perceptual, Neural, and Social Prospect (New York: Wiley, 1975).
detected that the words rhymed: Mark S. Seidenberg and Michael K. Tanenhaus, “Orthographic Effects on Rhyme Monitoring,” Journal of Experimental Psychology—Human Learning and Memory 5 (1979): 546–54.
95–96 sentence was literally true: Sam Glucksberg, Patricia Gildea, and Howard G. Boo {How>
Journal of Verbal Learning and Verbal Behavior 21 (1982): 85–98.
9: Answering an Easier Question
an intuitive answer to it came readily to mind: An alternative approach to judgment heuristics has been proposed by Gerd Gigerenzer, Peter M. Todd, and the ABC Research Group, in Simple Heuristics That Make Us Smart (New York: Oxford University Press, 1999). They describe “fast and frugal” formal procedures such as “Take the best [cue],” which under some circumstances generate quite accurate judgments on the basis of little information. As Gigerenzer has emphasized, his heuristics are different from those that Amos and I studied, and he has stressed their accuracy rather than the biases to which they inevitably lead. Much of the research that supports fast and frugal heuristic uses statistical simulations to show that they could work in some real-life situations, but the evidence for the psychological reality of these heuristics remains thin and contested. The most memorable discovery associated with this approach is the recognition heuristic, illustrated by an example that has become well-known: a subject who is asked which of two cities is larger and recognizes one of them should guess that the one she recognizes is larger. The recognition heuristic works fairly well if the subject knows that the city she recognizes is large; if she knows it to be small, however, she will quite reasonably guess that the unknown city is larger. Contrary to the theory, the subjects use more than the recognition cue: Daniel M. Oppenheimer, “Not So Fast! (and Not So Frugal!): Rethinking the Recognition Heuristic,” Cognition 90 (2003): B1–B9. A weakness of the theory is that, from what we know of the mind, there is no need for heuristics to be frugal. The brain processes vast amounts of information in parallel, and the mind can be fast and accurate without ignoring information. Furthermore, it has been known since the early days of research on chess masters that skill need not consist of learning to use less information. On the contrary, skill is more often an ability to deal with large amounts of information quickly and efficiently.
best examples of substitution: Fritz Strack, Leonard L. Martin, and Norbert Schwarz, “Priming and Communication: Social Determinants of Information Use in Judgments of Life Satisfaction,” European Journal of Social Psychology 18 (1988): 429–42.
correlations between psychological measures: The correlation was .66.
dominates happiness reports: Other substitution topics include marital satisfaction, job satisfaction, and leisure time satisfaction: Norbert Schwarz, Fritz Strack, and Hans-Peter Mai, “Assimilation and Contrast Effects in Part-Whole Question Sequences: A Conversational Logic Analysis,” Public Opinion Quarterly 55 (1991): 3–23.
evaluate their happiness: A telephone survey conducted in Germany included a question about general happiness. When the self-reports of happiness were correlated with the local weather at the time of the interview, a pronounced correlation was found. Mood is known to vary with the weather, and substitution explains the effect on reported happiness. However, another version of the telephone survey yielded a somewhat different result. These respondents were asked about the current weather before they were asked the happiness quest {ppiournal ofion. For them, weather had no effect at all on reported happiness! The explicit priming of weather provided them with an explanation of their mood, undermining the connection that would normally be made between current mood and overall happiness.
view of the benefits: Melissa L. Finucane et al., “The Affect Heuristic in Judgments of Risks and Benefits,” Journal of Behavioral Decision Making 13 (2000): 1–17.
10: The Law of Small Numbers
“It is both…without additives”: Howard Wainer and Harris L. Zwerling, “Evidence That Smaller Schools Do Not Improve Student Achievement,” Phi Delta Kappan 88 (2006): 300–303. The example was discussed by Andrew Gelman and Deborah Nolan, Teaching Statistics: A Bag of Tricks (New York: Oxford University Press, 2002).
50% risk of failing: Jacob Cohen, “The Statistical Power of Abnormal-Social Psychological Research: A Review,” Journal of Abnormal and Social Psychology 65 (1962): 145–53.
“Belief in the Law of Small Numbers”: Amos Tversky and Daniel Kahneman, “Belief in the Law of Small Numbers,” Psychological Bulletin 76 (1971): 105–10.
“statistical intuitions…whenever possible”: The contrast that we drew between intuition and computation seems to foreshadow the distinction between Systems 1 and 2, but we were a long way from the perspective of this book. We used intuition to cover anything but a computation, any informal way to reach a conclusion.
German spies: William Feller, Introduction to Probability Theory and Its Applications (New York: Wiley, 1950).
randomness in basketball: Thomas Gilovich, Robert Vallone, and Amos Tversky, “The Hot Hand in Basketball: On the Misperception of Random Sequences,” Cognitive Psychology 17 (1985): 295–314.
11: Anchors
“‘reasonable’ volume”: Robyn Le Boeuf and Eldar Shafir, “The Long and Short of It: Physical Anchoring Effects,” Journal of Behavioral Decision Making 19 (2006): 393–406.
nod their head: Nicholas Epley and Thomas Gilovich, “Putting Adjustment Back in the Anchoring and Adjustment Heuristic: Differential Processing of Self-Generated and Experimenter-Provided Anchors,” Psychological Science 12 (2001): 391–96.
stay closer to the anchor: Epley and Gilovich, “The Anchoring-and-Adjustment Heuristic
.”
associative coherence: Thomas Mussweiler, “The Use of Category and Exemplar Knowledge in the Solution of Anchoring Tasks,” Journal of Personality and Social Psychology 78 (2000): 1038–52.
San Francisco Exploratorium: Karen E. Jacowitz and Daniel Kahneman, “Measures of Anchoring in Estimation Tasks,” Person {pantion ality and Social Psychology Bulletin 21 (1995): 1161–66.
substantially lower: Gregory B. Northcraft and Margaret A. Neale, “Experts, Amateurs, and Real Estate: An Anchoring-and-Adjustment Perspective on Property Pricing Decisions,” Organizational Behavior and Human Decision Processes 39 (1987): 84–97. The high anchor was 12% above the listed price, the low anchor was 12% below that price.
rolled a pair of dice: Birte Englich, Thomas Mussweiler, and Fritz Strack, “Playing Dice with Criminal Sentences: The Influence of Irrelevant Anchors on Experts’ Judicial Decision Making,” Personality and Social Psychology Bulletin 32 (2006): 188–200.
NO LIMIT PER PERSON: Brian Wansink, Robert J. Kent, and Stephen J. Hoch, “An Anchoring and Adjustment Model of Purchase Quantity Decisions,” Journal of Marketing Research 35 (1998): 71–81.
resist the anchoring effect: Adam D. Galinsky and Thomas Mussweiler, “First Offers as Anchors: The Role of Perspective-Taking and Negotiator Focus,” Journal of Personality and Social Psychology 81 (2001): 657–69.
otherwise be much smaller: Greg Pogarsky and Linda Babcock, “Damage Caps, Motivated Anchoring, and Bargaining Impasse,” Journal of Legal Studies 30 (2001): 143–59.
amount of damages: For an experimental demonstration, see Chris Guthrie, Jeffrey J. Rachlinski, and Andrew J. Wistrich, “Judging by Heuristic-Cognitive Illusions in Judicial Decision Making,” Judicature 86 (2002): 44–50.
12: The Science of Availability
“the ease with which”: Amos Tversky and Daniel Kahneman, “Availability: A Heuristic for Judging Frequency and Probability,” Cognitive Psychology 5 (1973): 207–32.
self-assessed contributions: Michael Ross and Fiore Sicoly, “Egocentric Biases in Availability and Attribution,” Journal of Personality and Social Psychology 37 (1979): 322–36.
A major advance: Schwarz et al., “Ease of Retrieval as Information.”
role of fluency: Sabine Stepper and Fritz Strack, “Proprioceptive Determinants of Emotional and Nonemotional Feelings,” Journal of Personality and Social Psychology 64 (1993): 211–20.
experimenters dreamed up: For a review of this area of research, see Rainer Greifeneder, Herbert Bless, and Michel T. Pham, “When Do People Rely on Affective and Cognitive Feelings in Judgment? A Review,” Personality and Social Psychology Review 15 (2011): 107–41.
affect their cardiac health: Alexander Rotliman and Norbert Schwarz, “Constructing Perceptions of Vulnerability: Personal Relevance and the Use of Experimental Information in Health Judgments,” Personality and Social Psychology Bulletin 24 (1998): 1053–64.
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%.
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