Judgments and Decisions Psych 253

1. Judgments and DecisionsPsych 253 • Heuristics and Biases in Probability Judgments • Availability Heuristic • Anchoring Heuristic • Representativeness Heuristic • Overconfidence

2. You can play a lottery by selecting A, B, or C. Below are the payoffs for each option, depending on the throw of a die. Which one do you want to play? Die Roll 1 2 3 4 5 6 A$1 $2 $3 $4 $5 $6 B $6 $2 $5 $2 $2 $2 C $7 $5 $4 $4 $2 $1 What should you choose?

3. Die Roll 1 2 3 4 5 6EV A $1 $2 $3 $4 $5 $6 $3.50 B $6 $2 $5 $2 $2 $2 $3.17 C $7 $5 $4 $4 $2 $1 $3.83

4. Which one would you select if you knew what number would come up on the die? Die Roll 1 2 3 4 5 6 A$1 $2 $3 $4 $5 $6 B$6 $2 $5 $2 $2 $2 C$7 $5 $4 $4 $2 $1

5. What is the expected value of knowing the number that would come up? 1/6*[ $7 + $5 + $5 + $4 + $5 + $6 ] = $5.33 Die Roll 1 2 3 4 5 6 A $1 $2 $3 $4 $5 $6 B$6 $2 $5 $2 $2 $2 C$7 $5 $4 $4 $2 $1

6. The value of the information is $5.33 - $3.83 = $1.50 Value of the decision WITH perfect information Value of the decision WITHOUT perfect information

7. Which would you prefer? • $30 for sure • A gamble in which a fair coin is tossed. If it comes up heads, you win $100. If tails, you get $0 • How much should you pay to find out which side of the coin will come up? .5*(100) + .5*(30) = $65

8. Heuristics in Probability Judgments • When we reason probabilistically, we often use the wrong information, ignore the right information, and apply rules that result in logic errors.

9. Kahneman and Tversky identified three judgmental heuristics – all of which involve associations. • Availability Heuristic -- People assess the probability of an event based on the degree to which instances are readily available. If you can think of it, it must be important. • Anchoring Heuristic: People start with an anchor then adjust up or down, but their adjustments are often insufficient. Initial associations are hard to shake off. • Representativeness Heuristic: People often think about the resemblance between an object and a larger reference class. If two things are similar, the probability of one given the other must be higher.

10. Heaven and Hell • Heaven is where the Police are British, the Chefs are French, the Mechanics are German, the Lovers are Italian, and it's all organized by the Swiss. • Hell is where the Chefs are British, the Mechanics are French, the Lovers are Swiss, the Police are German, and it's all organized by the Italians We make associations about everything – especially each other!

11. Availability: Vivid Associations Which events kill more people? Homicide or Suicide Floods or Tuberculosis Tornados or Asthma We focus on information that is vivid -- recent or well publicized, even when it is less likely to occur, and our perceptions of risk are influenced by these associations.

12. Vividness of Cases and the Banality of statistics “A single death is a tragedy; a million deaths is a statistic." Joseph Stalin “If I look at the masses I will never act, but if I look at one, I will.” Mother Teresa Save the Children: Donation Request • Food Shortages in Malawi are affecting 3 Million children • More than 11 Million in Ethiopia need immediate assistance • One-third of Angolans have been forced to flee their homes Any money you donate will go to Roika, a seven year old girl from Mali, Africa. Roika is desperately poor and faces the threat of starvation. Her life will be transformed as a result of your contribution. $1.14 $2.38

13. Availability: Easily-Recalled Associations • Married couples were asked to estimate the percentage of work they contributed to various activities, including cooking, cleaning, and taking out the garbage. Each member of the couple was asked independently. In 16 of 20 activities, the total percentage exceeded 100%. • The percentage of effort contributed to a collaborative project such as professor/student or supervisor/supervisee summed to approximately 130%. • The salience of what we personally do (relative to the other person) can lead to an overestimation of our contribution to a joint effort.

14. Think of three wonderful things about your significant other. Now rate your feelings about your significant other on a scale from 1 = Not at all Satisfied to 10 = Perfectly Satisfied • Now think of 10 wonderful things about your significant other… • The experienced difficulty (or ease) of recall can influence our judgments and evaluations.

15. Availability: Consistent Associations • When we have stereotypical impressions of a group, we often make follow up evaluations that are consistent with those impressions. • Is music different when a women plays it? • Auditions were done in front of a panel of judges before 1970. Since then, auditions are done behind drapes. (And now they also done barefoot!)

16. Representativeness: Detailed Associations • What are the chances of a massive flood occurring in the next 30 years somewhere in North America in which 1,000 people drown? • What are the chances of an earthquake occurring in the next 30 years somewhere in California which causes a massive flood in which 1,000 people drown?

17. Rank in terms of probability four possible crimes committed by Mr. P who is currently under investigation by the police. 1 = the most probable crime and 4 = the least probable crime. Group 1 a. Mr. P is a child molester. b. Mr. P is involved in espionage and the sale of secret documents. c. Mr. P is a drug addict. d. Mr. P. killed one of his employees. Group 2 a. Mr. P is a child molester. b. Mr. P is involved in espionage and the sale of secret documents. c. Mr. P is a drug addict. d. Mr. P. killed one of his employees to prevent him from talking to the police

18. Representativeness: Expected Associations • You're playing roulette, and red has just come up eight times in a row! Is black more likely on the next spin? • No, it is not. Both red and black are equally likely. If you thought otherwise then the casinos love you.

19. A cab was involved in a hit and run accident at night. Two cab companies, the Green and the Blue, operate in the city. 85% of the cabs in the city are Green and 15% are Blue. • A witness identified the cab as Blue. The court tested the reliability of the witness under the same circumstances that existed on the night of the accident and concluded that the witness correctly identified each color 80% of the time and failed 20% of the time. • What is the probability that the cab involved in the accident was Blue rather than Green knowing that this witness identified it as Blue?"

20. Anchoring: Associations • Are there more than 2 billion mobile phones in the world? • What is your best estimate of the number of mobile phones in the world? • We often give disproportionate weight to a single piece of information that becomes the anchor for our estimate.

21. No Limit per Person (3.3 cans on average 73 cans sold) Limit 4 per Person (3.5 cans on average 106 cans sold)

22. Limit 12 per Person (7 cans on average 188 cans sold)

23. Other Examples of Anchors Initial offers in negotiations First impressions at the workplace Anchors set reference points and can be hard to ignore, even when they’re irrelevant.

24. Anchors in Real Estate. All participants in this study visited a piece of property in Tucson. They were each given a 10 page packet including: 1) The MLS listing sheet, 2) Sale information from the last 6 months, 3)Current listings in the area and the accompanying MLS listing information. • A questionnaire asked them to • Appraise the house’s value • Set an appropriate selling price • Estimate an actual, reasonable sale price • The lowest price the house should go for • The actual listing price and appraised value of the house was $74,900 • There were two groups of subjects, and the only difference between groups was the actual listing price. • Low Price Group’s packet had it listed as $65,900 (-12%) • High Group’s packet had it listed as $83,900 (+12%)

25. Results for Undergraduate Business Students: Results For Real Estate Agents:

26. Increased success rates for women since blind auditions • 1st Round – 50% and Final Round – 300% • Overall Differences in Representation • Pre 1970 - 5% of top five orchestras had women • 1990 – 36% of top five orchestras have women • Today - 50% of top five orchestras have women

27. I am an above-average driver. • I have an above average ability to judge humor. • My academic performance places me in the top half of my class.

28. To protect our mindsets, we engage in self-serving biases. We claim credit for success and blame failure on bad luck. • Strong economy during a presidential era • Increase in company stocks • Pay raises

29. Overconfidence in Action • 80% of entrepreneurs perceived their chances of success as 70% or higher. 33% perceived their chances as certain, but the average success rate for similar businesses was only 59%. The five year survival rate of similar businesses was 33% (Cooper et al, 1988) • More than 70% of new plants in North America close within the first decade. 3/4 of mergers and acquisitions don’t pay off. 2/3 of all small businesses fail within the first year.

30. Overconfidence Quotes 1929 (September 16): "A severe depression like that of 1920-21 is outside the range of probability.“ - Harvard Economic Society. 1932: “There is not the slightest indication that nuclear energy will ever be attainable. That would mean that the atom would have to be shattered at will.” - Albert Einstein 1943: "I think there is a world market for about five computers.“ - Thomas J. Watson (Chairman of the Board of IBM) 1964: "Reagan doesn't have the presidential look.“ - United Artists Executive, dismissing the idea that Ronald Reagan be offered the starring role in the movie THE BEST MAN. 1977: "There is no reason for any individual to have a computer in their home.“ - Ken Olson (President of Digital Equipment Corporation), World Future Society Convention, Boston. 1981: “640Kb is enough for anyone.” - Bill Gates

31. Avoiding Overconfidence requires • Good Calibration: You know what you know and you know what you don’t know. (The average subjective probability of a set of events is equivalent to the relative frequency of those events.) • Good Discrimination:You can tell the difference between signals and noise. (The assignment of higher probabilities to things that happen than to things that do not happen.)

32. Best-Possible Calibration, Good Discrimination 1 0.8 0.6 Objective Frequency 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Subjective Probability

33. Physicians’ Diagnoses of Suspected Bacteremia

34. Economists’ Predictions of Recession in 3–6 Months

35. Lawyers’ Predictions of Winning Their Case

36. Meteorologists’ Predictions of Precipitation

37. Greenspan: 'I was right 70% of the time'"When you've been in government for 21 years, as I have been, the issue of retrospect and what you should have done is a really futile activity," Greenspan said. "I was right 70% of the time. But I was wrong 30% of the time, and there were an awful lot of mistakes in 21 years," he added.

38. How Can This Be? • If people just try hard enough, the errors and biases in human judgment will usually take care of themselves. True or False? • The market takes care of these issues – the fittest will survive and the biases will vanish. True or False?