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Thinking part II judgment heuristics reasoning decision-making

Thinking part II judgment heuristics reasoning decision-making. Judgment Heuristics. Tversky & Kahneman propose that people in many cases might not reason optimally (e.g. follow rules of probability or rational choice) Instead, judgment may be based on heuristics

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Thinking part II judgment heuristics reasoning decision-making

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  1. Thinking part IIjudgment heuristicsreasoningdecision-making

  2. Judgment Heuristics • Tversky & Kahneman propose that people in many cases might not reason optimally (e.g. follow rules of probability or rational choice) • Instead, judgment may be based on heuristics • Lower cognitive load but may lead to systematic errors and biases • Example heuristics • representativeness • availability Kahneman Tversky

  3. Tom Cruise Celia Weston Tom Hanks Frances O’Connor Jane Adams Mel Gibson Illeana Douglass Jim Carrey Marg Helgenberger George Clooney Debi Mazar Alyson Hannigan Russell Crowe Harrison Ford Bruce Willis Lindsay Crouse Molly Parker Brad Pitt Memory for Names

  4. Availability Heuristic • A person is said to employ the availability heuristic whenever he/she estimates frequency or probability by the ease with which instances or associations could be brought to mind

  5. Availability Heuristic • Are there more words in the English language that begin with the letter V or that have V as their third letter? • What about the letter R, K, L, and N? (Tversky & Kahneman, 1973)

  6. Which causes more deaths in developed countries? 1. (a) traffic accidents (b) stomach cancer 2. (a) homicide (b) suicide (Kahneman & Tversky, 1974)

  7. Results • Traffic accident vs. Stomach cancer: • Typical Guess (in 1974) traffic accident = 4X stomach cancer • Actual (1974 estimates) 45,000 traffic, 95,000 stomach cancer deaths in US • Ratio of newspaper reports on each subject 137 (traffic fatality) to 1 (stomach cancer death) • Actual Homicide vs. Suicide rates (2013): • Murder rate 6 per 100,000 • Suicide rate 10.8 per 100,000 (Kahneman & Tversky, 1974)

  8. (Lichtenstein et al., 1978)

  9. Why use the availability heuristic? • Availability is based on fundamental aspect of memory search • Works well under many circumstances • Availability correlates with likelihood of events

  10. A hospital is surveyed about the exact sequence of births of boys and girls (from different mothers) in a particular day. What is more likely: • G B G B B G • B B B B B B

  11. Another example A coin is flipped. What is a more likely sequence? A) H T H T T H B) H H H H H H

  12. Representativeness Heuristic • The sequence “H T H T T H” is seen as more representative of or similar to a prototypical coin sequence

  13. 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. Please choose the most likely alternative: (a) Linda is a bank teller (b) Linda is a bank teller and is active in the feminist movement

  14. Conjunction Fallacy • Nearly 90% choose the second alternative (bank teller and active in the feminist movement), even though it is logically incorrect (conjunction fallacy) bank tellers feminists bank tellers who are not feminists feminists who are not bank tellers feminist bank tellers Kahnemann and Tversky (1982)

  15. Representativeness heuristic • Kahneman and Tversky (1982) explained these results using the representativeness heuristic • tendency of people to judge probabilities or likelihoods according to how much one thing resembles another • Linda is more representative of a feminist bank teller than just a bank teller alone, so people give the second answer

  16. Hot Hand Belief in Basketball • Question: • Does a basketball player have a better chance of making a free throw shot after having just made his last two shots than he does after having just missed his last two shots? • Answers by Cornell and Stanford University Basketball fans • Yes = 91% • No = 9% (Gilovich, Vallone, & Tversky, 1985)

  17. Does the “hot hand” phenomenon exist? • Most basketball coaches/players/fans refer to players having a “Hot hand” or being in a “Hot zone” and show “Streaky shooting” • However, making a free throw shot after just making two free throw shots is just as likely as after just missing two shots  people can make errors in judging probabilities of sequential events (Gilovich, Vallone, & Tversky, 1985)

  18. What to make of these results? • One interpretation of Tversky & Kahneman’s findings: • people do not use proper probabilistic reasoning • people use arbitrary mechanisms/ heuristics with no apparent rationale • However, heuristics can often be very effective

  19. Which city has a larger population? A) San Diego B) San Antonio (TX) • 66% accuracy with University of Chicago undergraduates. However, 100% accuracy with German students. • San Diego was recognized as American cities by 78% of German students. San Antonio: 4%  With lack of information, the heuristic of picking the city that is recognized is very effective (Goldstein & Gigerenzer, 2002) (note: at the time of the experiment, San Diego actually was the larger city – this is no longer true)

  20. Decision Making • In making choices, people are sensitive to outcomes and to degrees of risk. • However, people are also heavily influenced by how a decision is framed. • When cast in terms of gains, people tend to avoid any risk. • When cast in terms of losses, people seek out risk, presumably in hopes of avoiding it.

  21. Framing effect • Problem 1 Suppose I give you $300, but you also have to select one of these two options: • 1.0 chance of gaining $100 • .50 chance of gaining $200 and a .50 chance of gaining nothing • Problem 2 Suppose I give you $500, but you also have to select one of these two options: • 1.0 chance of losing $100 • .50 chance of losing $200 and a .50 chance of losing nothing (72%) (28%) (36%) (64%) (Tversky & Kahneman, 1986)

  22. Framing effect • Problem 1: • Select one of two prizes (36%) An elegant Cross pen (64%) $6 • Problem 2: • Select one of three prizes (46%) An elegant Cross pen (52%) $6 (2%) An inferior pen (Shafir & Tversky 1995)

  23. 50% 50% Example: Cheeseburgers

  24. 30% 60% 10% Example: Cheeseburgers 50% 50%

  25. Mental Accounting (A) Imagine you are at an electronics store and about to purchase a pair of headphones for $145 and a calculator for $20. Your friend mentions though, that the same calculator is on sale for $10at a different store located 20 minutes away. Would you make the trip to the other store? (B) Now, imagine a different scenario. You are at an electronics store and about to purchase a pair of headphones for $20 and a calculator for $145. Your friend mentions that the same calculator is on sale for $135 at a different store located 20 minutes away. Would you make the trip to the other store? (Thaler, 1999)

  26. Reasoning • In reasoning, we try to draw implications from our beliefs. • This is crucial for the use of knowledge and provides a means of testing our beliefs. • However, people often show a pattern of confirmation bias. • They take evidence more seriously if it confirms their beliefs than if it challenges them.

  27. Wason Selection Task “If a card has a vowel on one side, then it has an even number on the other side” Which cards do you need to turn over to test the correctness of the rule? E K 4 7

  28. Concrete examples are much easier • If a person is drinking beer, then the person must be over 21. How to test whether somebody is abiding by this rule? Drinking beer Drinking Coke 16 years of age 22 years of age Result: 74% answered correctly

  29. Confirmation bias in hypothesis testing • 2-4-8 is a set of numbers that conforms to a rule. • Discover the rule by querying with any set of three numbers and I’ll give feedback whether it is a positive or negative example. (Wason, 1960)

  30. Confirmation Bias • Wason (1960): subjects test hypotheses by generating positive rather than negative examples • Popper (1959): confirmatory strategies provide ambiguous information. The hypothesis may be correct or another hypothesis may be correct  scientists should try to falsify their theories (However, in many cases, it might make more sense to confirm hypotheses, and not to attempt falsification)

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