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PSY241 Heuristics

PSY241 Heuristics

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PSY241 Heuristics

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  1. PSY241 Heuristics Tom Stafford t.stafford@shef.ac.uk

  2. Are we smart or dumb? The reasoning problems from the lab class show we have difficulty with certain kinds of thinking Selection task: logic Holmes-Moriarty: theory testing THOGs: counter-factuals, working memory limitations Goats: probability theory Wason (1966, 1960), Wason & Brooks (1979), Selvin (1975)

  3. Systematic errors • Confirmation bias • Pattern matching http://failblog.org/2008/11/10/dear-abby-fail/

  4. Heuristics • A heuristic is a rule-of-thumb • “principle with broad application that is not intended to be strictly accurate or reliable for every situation” • Heuristics produce systematic errors (‘biases’)

  5. Why use heuristics • Perfect rationality is computationally demanding. • In the real world • Time is short • Information is limited • Information is ambiguous • Cognitive resources are limited

  6. Simple rules that make us smart (most of the time) • A good heuristic • efficient • quick • robust • mostly right • Heuristics exploit environmental structure (Gigerenzer et al, 1999)

  7. Decisions under uncertainty • Calculating exact probabilities is difficult • We rely on heuristics to help us, and heuristics lead to biases • Three heuristics which are responsible for a range of biases and errors are • representativeness • availability • adjustment and anchoring • Tversky & Kahneman (1974)

  8. Representativeness • Probabilities are evaluated according to similarity to the parent population or generating process, rather than strictly, taking into account base rate probabilities or rates of variance.

  9. Linda is 31 years old, single, outspoken, and very bright. She studied philosophy at University. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-war demonstrations. Which is more likely? 1. Linda is a bank teller. 2. Linda is a bank teller and is active in the feminist movement. Tversky & Kahneman (1983)

  10. Representativeness • As test for a Dreaded Disease is 99.9% accurate. The Dreaded Disease is rare, however, so only 1 in a million people have the disease. You get tested and the test comes back positive. Is it likely you have the disease?

  11. Bate Rates • 1,000,000 people • Likely 1 has the disease, 999,999 don’t • Test them all • Hits ~1, Misses, ~0 • False Alarms: 1/1000 x 999,999 = ~1000 • Correct Rejections: 999/1000 x 999,999 • Positive results = Hits + FAs = 1001

  12. Representativeness • As test for a Dreaded Disease is 99.9% accurate. The Dreaded Disease is rare, however, so only 1 in a million people have the diesase. You get tested and the test comes back positive. Is it likely you have the disease? • Answer: No. 1000 to 1 you don’t have the Dreaded Disease Gigerenzer & Edwards (2003)

  13. Representativeness • We ignore base rates • Law of small numbers (inverse representativeness) • gambler’s fallacy • If I toss a coin ten times, which of these sequences is most likely: a) HTHTTHTTHH b) HHHHHTTTTT c) HHHHHHHHHH • What’s the likelihood that ‘red’ will be the next colour in the roulette wheel, given that there was a ‘streak’ of ‘red’?

  14. A Quick Experiment • I will read to you a list of names for a very short time.Please listen carefully,and try to remember the names. Albert Einstein Sophie Scott Mary Smith David Cameron Barack Obama Justin Bieber Fiona Wilkins Sarah Nunn Tricia Anderson Wayne Rooney Bill Gates Julie Shanks Alice Padley Lizzie Gilman Bob Dylan Bart Simpson Marion Dickinson Russell Brand Nancy Oberlin Michael Jackson Barbara Mulligan George Clooney Lucy Andrews Martha Collins John Lennon Mia Morgen Sally Fields

  15. A Quick Experiment Were there more male or female names on the list?

  16. Availability Probabilities are assessed by the ease with which instances come to mind

  17. Availability • There were more women than men on the list (15 vs 12) • Aircrashes • r __ or __r words?

  18. Recognition heuristic • Which city has a larger population? • San Antonio or San Diego? • Hamburg or Cologne? • German and American students asked • A “Less is more“ effect Goldstein & Gigerenzer (2002)

  19. Adjustment & Anchoring • People will make an estimate and then adjust. • irrelevant information can bias the initial estimate • adjustments can be insufficient

  20. Adjustment & Anchoring Estimate 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 or 9 x 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1 512 2250 40320

  21. Adjustment & Anchoring • Take the last three digits of your phone number • Add 400. Call this X • “Do you think Attila the Hun was defeated in Europe before or after that year X?” • “Which year was Attila the Hun defeated?”

  22. Anchor - Av. Estimate 400–599 : 629 600–799 : 680 800–999 : 789 1000–1199 : 885 1200–1399 : 988 After Russo & Schoemaker (1989) cited in Nicholls (1999)

  23. The ‘Asian Disease Problem’ • Imagine that you are the Mayor of a city that is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume the exact scientific estimate of the consequences of the programs are as follows. Tversky & Kahneman (1981)

  24. Options presented as either gains or losses • Gainsborough • Program A: "200 people will be saved" • Program B: "there is a one-third probability that 600 people will be saved, and a two-thirds probability that no people will be saved" • Lossville Falls • Program 1: "400 people will die“ • Program 2: "there is a one-third probability that nobody will die, and a two-third probability that 600 people will die"

  25. Framing affects decision dramatically • Option presented as gain • 72 percent of participants preferred program A, with the remainder, 28 percent, opting for program B. • Options presented as losses • 22 percent preferred program 1, with the remaining 78 percent opting for program 2

  26. A Status Quo Bias • Result presented by Tversky & Kahneman as a result of loss aversion • aka “Status Quo Bias” • aka “Endowment effect” • Habit may be the most powerful heuristic (Kahneman et al, 1991; Ajzen, 2002)

  27. Biases are not mere errors • Visual illusions reveal the normal mechanisms of perception • Heuristic errors reveal the normal mechanisms of reasoning • Not motivational biases (eg ego-protecting biases) • Can affect experts and laypeople alike

  28. Rational vs Adaptive • What is the optimal strategy for a thinking machine • to get the logically correct answer 100% of the time, no matter how long it takes? • to get an answer quickly, which is usually right?

  29. THE END Questions? t.stafford@shef.ac.uk