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Heuristics - Shortcuts Instead of Normative Decision Making

Heuristics - Shortcuts Instead of Normative Decision Making. Representativeness Heuristic - We make judgments of probability based on similarity and do not consider other rules that we should. Let’s look at the evidence - first we ignore base-rates.

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Heuristics - Shortcuts Instead of Normative Decision Making

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  1. Heuristics - Shortcuts Instead of Normative Decision Making • Representativeness Heuristic - We make judgments of probability based on similarity and do not consider other rules that we should. • Let’s look at the evidence - first we ignore base-rates. • The normative rules come from Bayes Theorem which sounds pretty confusing but basically suggests that when we make a decision we should take prior probabilities into account unless we are absolutely certain about the decision.

  2. What is Tom W.’s Major? Tom W. is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to have little feel and little sympathy for other people and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense.

  3. What is Tom W.’s Major? • Kahneman & Tversky asked 3 questions • What percentage of people in the different majors? • How similar is Tom W. to each major? • How likely is Tom W. each major? • They found estimates of how likely Tom W. is a particular major are strongly influenced by how similar he is to their stereotype about the major, but unrelated to the percentage of people in the different majors. This violates Bayes Theorem unless they are certain from the description what major Tom W. is which they weren’t.

  4. Is Jack a Lawyer or an Engineer? Jack is a 45 year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies which include home carpentry, sailing, and mathematical puzzles. The probability that Jack is one of the 30 engineers in the sample of 100 is ______%.

  5. Is Dick a Lawyer or an Engineer? Dick is a 30-year-old man. He is married with no children. A man of high ability and high motivation, he promises to be quite successful in his field. He is well liked by his colleagues. The probability that Dick is one of the 30 engineers in the sample of 100 is ______%.

  6. Is Jack/Dick a Lawyer or an Engineer? • K & T found that people’s ratings of whether Jack was a Lawyer or an Engineer were virtually unaffected by the base rate information of whether there were 30% Lawyers or 70% Lawyers. • They also found that people paid attention to base-rates if they were given no information about Jack • However, Dick was judged to be 50% likely to be a Lawyer regardless of the base-rates.

  7. Violations of Logic and the Representativeness Heuristic - Is Jane a Bank Teller or a Feminist Bank Teller? 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 of the following is more probable? A. Linda is a bank teller B. Linda is a bank teller and active in the feminist movement

  8. Social Effects of the Representativeness Heuristic • Consensus Information and the Fundamental Attribution Error - Nisbett & Borgida (1975) • Seizure study and attribution about a typical student • Those given information about the results of this study ignored it • The Dilution Effect • Basic Effect - (Nisbett, et al., 1981) Child molester with an IQ of 110. • Effect of Pseudo-Relevant Information and truly irrelevant information - Fein & Hilton (1989) Paul whose has a single mother

  9. Social Effects of the Representativeness Heuristic (cont.) • Stereotypes and the Representativeness heuristic • Locksley’s Research - people ignore stereotypes like base rates when given a personal description • But it is not that simple as we will see later when we cover stereotypes note: • Representativeness information is often the stereotype rather than the base-rate. • Goals and motivation affects these processes • Other research by Biernat and her colleagues calls this into question • Clear base rates • Fuzzy categories

  10. Qualifications of the Original Findings • Focussing Attention on Chance Factors increases use of base rates • Asking people to draw the Lawyer or Engineer out of a Bingo Drum decreases reliance on representativeness by increasing people’s use of base rates. • Rules of Language use may exaggerate the K & T findings • Order of whether base-rates and individuating information affects people’s use of base-rates. • People use base-rates more when predicting multiple occurrences in the long run.

  11. Taking a Single Instance as Representative: Implications for Statistical Reasoning • Numerical Predictions- Participants read a short description (either a report or just a list of adjectives) about a college freshman. • K & T then asked 2 questions • What percentage of descriptions of freshman do you believe would impress you more? • What is the percentage of freshman who will obtain a higher grade point average? • The normative model suggests that since the second question is more uncertain than the first you should predict less success for the second. • The results suggest that people make the same predictions for these two questions.

  12. Ignoring Statistical Rules (cont). • Regression to the Mean • Score on an IQ test • Pilots and Feedback • Ignoring Sample Size • Hospital Problem • Interviews • Misconceptions of Chance • Interpretations of random events (go with red or black?) • Belief in the hot hand

  13. Score on an IQ Test A problem of testing. A randomly selected individual has obtained a score of 140 on a standardized IQ test. Suppose that an IQ score is the sum of a “true” score and a random error of measurement which is normally distributed. Please give your best guess about the 95% upper and lower confidence bounds for the true IQ of this person. That is, give a high estimate such that you are 95% sure that the true IQ score is, in fact, lower than that estimate, and a low estimate such that you are 95% sure that the true score is in fact higher.

  14. Hospital Problem A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days? A) The larger hospital B) The smaller hospital C) About the same (that is, within 5 percent of each other)

  15. Statistical Heuristics - Factors that Affect their Use • Clarity of the sample space - some things are more clearly statistical problems than others; sports and ability, yes; personality, no • Experience with the domain - Domains where chance is emphasized experts use statistical heuristics more - • Why don’t tryouts match actual performance • People can answer hospital problem if it is just changed slightly and resembles how people think about families • Statistical Education - • statistical training can lead people to use statistical reasoning more often • Rookie of the Year Problem

  16. Availability Heuristic • We make decisions based on how easy things come to mind rather when judging how common something is • Are there more words with “k” as the first letter or “k” as the third letter? • Should women be more concerned about being assaulted by a stranger or a friend? • What causes the availability heuristic? - Is it the number of objects that come to mind or how easy it is for the objects to come to mind? • Schwarz et al., 1991 study - List six or twelve examples when you were assertive (or unassertive). Now tell me how assertive you are.

  17. What’s Behind the Availability Heuristic -How Easy or How Many?

  18. Reasons for the Availability Heuristic • Ignoring biases in available samples and accessible cognitions • False consensus effect - we think other people agree with us and do the things that we do more than is justified • The effect of media coverage • One-sided questions - What would you do to liven the party? What things do you dislike about loud parties? • Salience • When attention is focussed on someone we think they have more influence than they do • Solo status studies

  19. Consequences of the Availability Heuristic • Egocentric biases • Who does more of the housework? • Belief Perseverance? • Why do you think this class is fun and exciting? • Firefighters study • False feedback studies • Imagination • When we imagine things they seem more likely to occur • Cable TV study

  20. Anchoring and Adjustment • Do you hope to get a grade higher or lower than an 95 in this class? How much lower? • Length of the Mississippi study • Fundamental Attribution Error and Anchoring • Two-step models of Attribution • Make a trait attribution first which acts as an anchor • Make an adjustment which is insufficient • This type of two step model is not the same as dual process model in persuasion

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