(15) PSY452 - Chapter 12: Decision Making

12. Discuss the anchoring and adjustment heuristic, including in your answer a description of the methods and results of Tversky and Kahneman (1974).

*Anchoring and Adjustment* -Make an initial estimate (the anchor) then adjust that value to fit additional info -Very old technique in sales Tversky and Kahneman (1974): -Ss given 5 sec to produce an estimate of this problem (1 x 2 x ... x 8) -Median estimate = 512 -Other Ss estimate the reversed problem (8 x 7 x...x1) -Median estimate = 2250

19. What assumptions have been made by most decision making research about rationality and our use of heuristics? How does this differ from Gigerenzer's view of heuristics?

*Assumptions about rationality*: -We are not always rational in decision making -This is in part b/c we use heuristics *Assumptions about heuristics*: 1. Heuristics are always less than optimal 2. We use heuristics because of our own limitations 3. If we had more info, we would make better choices *Gerd Gigerenzer*: heuristics are optimal 1. Allow us to solve problems and make decisions quickly and accurately 2. Require little information

1. Distinguish between deductive reasoning and decision making, providing an example of each.

*Deductive reasoning*: you begin with some specific premises that are true, and you need to judge whether those premises allow you to draw a particular conclusion, based on the principles of logic. A deductive-reasoning task provides you with all the information you need to draw a conclusion. Furthermore, the premises are either true or false, and you must use the rules of formal logic in order to draw conclusions -ex: If a child is allergic to peanuts, then eating peanuts produces a breathing problem. A child has a breathing problem. Therefore, this child has eaten peanuts. *Decision making*: Any case where we are presented with 2 or more courses of action and must select just one -ex: Should we have pizza or hamburgers for dinner?

10. Distinguish between descriptive invariance and procedure invariance. Describe how these principles are violated in the following problems: a. Shafir (1993—choosing vs. rejecting) b. Tversky and Kahenman (1981—the disease problem)

*Descriptive invariance*: make the same choice no matter how a problem is described *Procedure invariance*: make the same choice no matter how it is measured a. Shafir (1993, choosing vs. rejecting) -If considering 1 of 2 options, shouldn't matter whether you choose or reject (ie: if you prefer the 1st option, should reject the second option) -More positive features = more likely chosen -More negative features = more likely rejected -Shafir suggests that we focus on different features when choosing or rejecting -*choosing*: focus on positive features -*rejecting*: focus on negative features -ex: Jury on only child sole custody case example Parent A: average descriptors Parent B: more positive and more negative descriptors Results: -Parent A: award = 36%, deny = 45% -Parent B: award = 64%, deny = 55% -When choosing, focused on positive features of Parent B -When rejecting, negative features of Parent B made them less desirable -*This violates procedure invariance* b. Tversky and Kahenman (1981 - disease problem): There is an outbreak of a disease that is expected to kill 600 people, and two programs to fight the disease are being considered (A) 200 people will be saved (B) There's a 1/3 chance that no one will die and 2/3 chance that 600 people will die (A) 400 people will die (B) There's a 1/3 chance that no one will die and 2/3 chance that 600 people will die -The problems are the exact same, just framed differently Results: -For Ss given the first framing: (A) = 72% (200 people saved) (B) = 28% (1/3 chance no one will die, 2/3 chance 600 people will die) -For Ss given the second framing: (A) = 22% (400 people die) (B) = 78% (1/3 chance no one will die, 2/3 chance 600 people will die) -Problem description affected decisions > *This violates descriptive invariance* > If we were rational, "saving" vs. "dying" should not affect decisions

7. What is the belief bias effect? Describe, providing an example.

*The belief-bias effect*: The belief-bias effect occurs in reasoning when people make judgments based on prior beliefs and general knowledge, rather than on the rules of logic. In general, people make errors when the logic of a reasoning problem conflicts with their background knowledge -We rely too heavily on our established beliefs. Ex: If a feather is thrown at a window, the window will break. A feather is thrown at a window. Therefore, the window will break. -this background information sometimes encourages us to make mistakes. -In everyday life, it's a good bet that this conclusion is incorrect; how could a feather possibly break a window? However, in the world of logic, this feather-window task actually affirms the antecedent, so it must be correct.

14. What is the hindsight bias? Describe, discussing the methods and results of Carli's (1999) study of hindsight bias. What explanations have been offered to explain hindsight bias?

*The hindsight bias*: occurs when an event has happened, and we say that the event had been inevitable; we had actually "known it all along" -reflects our overconfidence that we could have accurately predicted a particular outcome at some point in the past -demonstrates that we often reconstruct the past so that it matches our present knowledge *Carli (1999)*: asked students to read a two-page story about a young woman named Barbara and her relationship with Jack, a man she had met in graduate school. -The story, told from Barbara's viewpoint, provided background information about Barbara and her growing relationship with Jack. -Half of the students read a version that had a tragic ending, in which Jack rapes Barbara. -The other half read a version that was identical except that it had a happy ending, in which Jack proposes marriage to Barbara. -After reading the story, each student then completed a true/false memory test. This test examined recall for the facts of the story, but it also included questions about information that had not been mentioned in the story. -Some of these questions were consistent with a stereotyped version of a rape scenario, such as, "Barbara met many men at parties." Other questions were consistent with a marriage-proposal scenario, such as, "Barbara wanted a family very much." -The results of Carli's (1999) study demonstrated the hindsight bias. People who read the version about the rape responded that they could have predicted Barbara would be raped. -Furthermore, people who read the marriage-proposal version responded that they could have predicted Jack would propose to Barbara (remember that the two versions were actually identical, except for the final ending.). -Furthermore, each group committed systematic errors on the memory test. Each group recalled items that were consistent with the ending they had read, even though this information had not appeared in the story. -Carli's (1999) study is especially important because it helps us understand why many people "blame the victim" following a tragic event such as a rape. In reality, this person's earlier actions may have been perfectly appropriate. However, people often search the past for reasons why a victim deserved that outcome. -As we've seen in Carli's research, people may even "reconstruct" some reasons that did not actually occur. *Explanations for the Hindsight Bias*: Despite all the research, the explanations for the hindsight bias are not clear -However, one likely cognitive explanation is that people might use anchoring and adjustment. After all, they have been told that a particular outcome actually did happen—that it was 100% certain. -Therefore, they use this 100% value as the anchor in estimating the likelihood that they would have predicted the answer, and then they do not adjust their certainty downward as much as they should. -We also noted in discussing Carli's (1999) study that people may misremember past events, so that those events are consistent with current information. These events help to justify the outcome.

15. Describe what is meant by an illusory correlation, providing an example.

*illusory correlation*: occurs when people believe that two variables are statistically related, even though there is no actual evidence for this relationship. For example, consider the following illusory correlations: (1) Females have poor math skills, (2) people on welfare are cheaters,

13. Discuss the small sample fallacy. Describe, in detail, how it relates to the Hospital Problem.

*small sample fallacy*: assumption that small samples represent the population -Sample: a subset of a population -The larger the sample size, the closer the average value is to the average of the population *Hospital problem:* -For example, Kahneman and Tversky (1972) asked college students to consider a hypothetical small hospital, where about 15 babies are born each day, and a hypothetical large hospital, where about 45 babies are born each day. Which hospital would be more likely to report that more than 60% of the babies on a given day would be boys, or would they both be equally likely to report more than 60% boys? -Results (% of Ss selecting) -The larger hospital = 22% -The smaller hospital = 22% -About the same = 56% -Large sample least likely to deviate

17. Discuss the planning fallacy. According to your text, why does the planning fallacy occur?

According to the *planning fallacy*, people typically underestimate the amount of time (or money) required to complete a project; they also estimate that the task will be relatively easy to complete -Notice why this fallacy is related to overconfidence. Suppose that you are overconfident when you make decisions. You will then estimate that your paper for cognitive psychology will take only 10 hours to complete, and you can easily finish it on time if you start next Tuesday. *Why does the planning fallacy occur?* -One factor is that people create an optimistic scenario that represents the ideal way in which they will make progress on a project. This scenario fails to consider the large number of problems that can arise -People also recall that they completed similar tasks relatively quickly in the past -In addition, they estimate that they will have more free time in the future, compared to the free time they have right now. In other words, people use the anchoring and adjustment heuristic, and they do not make large enough adjustments to their original scenario, based on other useful information.

6. The book states that people have difficulties dealing with negative information. What sort of problems do they have and what can possibly account for these problems?

People can handle positive information better than negative information. -people have trouble processing sentences that contain words such as no or not. -This same issue is also true for conditional reasoning tasks. -For example, try the following reasoning task: If today is not Friday, then we will not have a quiz today. We will not have a quiz today. Therefore, today is not Friday. -This item has four instances of the word not, and is definitely more challenging than a similar but linguistically positive item that begins, "If today is Friday...." -Research shows that people take longer to evaluate problems that contain linguistically negative information, and they are also more likely to make errors on these problems -A reasoning problem is especially likely to strain our working memory if the problem involves denying the antecedent or denying the consequent. -Most of us squirm when we see a reasoning task that includes a statement like, "It is not true that today is not Friday." Furthermore, we often make errors when we translate either the initial statement or the conclusion into more accessible, linguistically positive forms.

4. Describe two factors accounting for why people are not rational decision makers

We do not always make rational decisions - Why? 1. Lack of resources (use heuristics: a heuristic is a general rule that is usually correct) 2. We are affected by other, non-rational factors

2. a. What is a confirmation bias? b. Describe the Wason (1968) selection task. How does confirmation bias operate in this task? c. Discuss variations of Wason's task that influence the pattern of data observed. For example, how did Griggs and Cox (1982) study confirmation bias?

a. *Confirmation bias*: The tendency to try to confirm or support a hypothesis rather than try to disprove it. b. *Wason Selection Task*: Imagine that each square below represents a card. Suppose that you are participating in a study in which the experimenter tells you that every card has a letter on one side and a number on the other side. You are then given this rule about these four cards: "IF A CARD HAS A VOWEL ON ONE SIDE, THEN IT HAS AN EVEN NUMBER ON THE OTHER SIDE." Your task is to decide which card (or cards) you would need to turn over, so that you can find out whether this rule is valid or invalid. -EJ67 -Wason (1968) found that people show a confirmation bias; they would rather try to confirm or support a hypothesis than try to disprove it When people try this classical selection task, they typically choose to turn over the E card. This strategy allows the participants to confirm the hypothesis by the valid method of affirming the antecedent, because this card has a vowel on it. -If this E card has an even number on the other side, then the rule is correct. If the number is odd, then the rule is incorrect. -As discussed above, the other valid method in deductive reasoning is to deny the consequent. To accomplish this goal, you must choose to turn over the 7 card. The information about the other side of the 7 card is very valuable. In fact, it is just as valuable as the information about the other side of the E card. Remember that the rule is: "If a card has a vowel on its letter side, then it has an even number on its number side." - To deny the consequent in this Wason Task, we need to check out a card that does not have an even number on its number side. -In this case, then, we must check out the 7 card. We noted that many people are eager to affirm the antecedent. In contrast, they are reluctant to deny the consequent by searching for counterexamples. This approach would be a smart strategy for rejecting a hypothesis, but people seldom choose this appropriate strategy c. *Griggs and Cox (1982)*: -people perform much better when the task is concrete, familiar, and realistic. -Griggs and Cox (1982) tested college students in Florida using a variation of the selection task. This task focused on the drinking age, which was then 19 in the state of Florida. -Specifically, the students were asked to test this rule: "If a person is drinking beer, then the person must be over 19 years of age". -Each participant was instructed to choose two cards to turn over—out of four—in order to test whether people were lying about their age. -Griggs and Cox (1982) found that 73% of the students who tried the drinking age problem made the correct selections, in contrast to 0% of the students who tried the standard, abstract form of the selection task. -According to later research, people are especially likely to choose the correct answer when the wording of the selection task implies some kind of social contract designed to prevent people from cheating

16. What is the relationship between confidence and the accuracy of decision making? In your answer you should: a. Describe what research has shown on confidence and decision making. b. Describe the reasons offered the relationship between confidence and decision making.

a. *General Studies on Overconfidence*: -A variety of studies show that humans are overconfident in many decision-making situations. -For example, people are overconfident about how long a person with a fatal disease will live, which firms will go bankrupt, and whether the defendant is guilty in a court trial. -People typically have more confidence in their own decisions than in predictions that are based on statistically objective measurements. -In addition, people tend to overestimate their own social skills, creativity, leadership abilities, and a wide range of academic skills -In addition, physicists, economists, and other researchers are overconfident that their theories are correct. -We need to emphasize, however, that individuals differ widely with respect to overconfidence. -For example, a large-scale study showed that 77% of the student participants were overconfident about their accuracy in answering general-knowledge questions. Still, these results tell us that 23% were either on target or under-confident. -Furthermore, people from different countries may differ with respect to their confidence. -For example, a cross-cultural study in three countries reported that Chinese residents showed the greater overconfidence, and the U.S. residents were intermediate. However, the least confident group was Japanese residents, who also took the longest to make their decisions b. *Reasons for Overconfidence*. -This overconfidence arises from errors during many different stages in the decision-making process: 1. People are often unaware that their knowledge is based on very tenuous, uncertain assumptions, and on information from unreliable or inappropriate sources. 2. Examples that confirm our hypotheses are readily available, but we resist searching for counterexamples. You'll recall from the discussion of deductive reasoning that people also persist in confirming their current hypothesis, rather than looking for negative evidence. 3. People have difficulty recalling the other possible hypotheses, and decision making depends on memory. If you cannot recall the competing hypotheses, you will be overly confident about the hypothesis you have endorsed 4. Even if people manage to recall the other possible hypotheses, they do not treat them seriously. The choice once seemed ambiguous, but the alternatives now seem trivial 5. Researchers do not educate the public about the overconfidence problem. As a result, we typically do not pause—on the brink of making a decision—and ask ourselves, "Am I relying only on Type 1 thinking? I need to switch over to Type 2 thinking!" -When people are overconfident in a risky situation, the outcome can often produce disasters, deaths, and widespread destruction. -The term my-side bias describes the overconfidence that your own view is correct in a confrontational situation. -Conflict often arises when individuals (or groups or national leaders) each fall victim to my-side bias. -People are so confident that their position is correct that they cannot even consider the possibility that their opponent's position may be at least partially correct. -If you find yourself in conflict with someone, try to overcome my-side bias. Could some part of the other people's position be worth considering? -More generally, try to reduce the overconfidence bias when you face an important decision. Emphasize Type 2 processing, and review the five points listed above.

21. In class we described the nature of "intuition", if it indeed exists. Discuss intuition, including in your answer the following: a. What is the naturalistic decision making approach (NDM). How is intuition defined by researchers interested in naturalistic decision making? b. Describe those conditions most likely and least likely to lead to the development of intuition. c. Distinguish between clinical judgment and mechanical prediction and describe the results of Grove et al.'s (2000) analysis of studies examining these methods of decision making. d. Overall, when should we rely on clinical judgments and when should we rely on mechanical prediction? (Note: The answer will likely be a mix of your opinion and information from class.)

a. *Naturalistic decision making approach*: study experts making decisions in their domains (eg: fireground commanders) -Look at decisions with: > high stakes > time pressure > uncertainty b. *Conditions for intuition*: -Need a highly valid decision environment > Causal, predictable (stable) relationship between cues and outcomes -Opportunity to learn -Objective feedback -ex: Neonatal nurses seeing infections coming *Conditions against intuition*: -Unpredictable (low validity) environments make intuition unlikely -Cannot identify the right cues -ex: predicting financial markets, world events, predicting human behavior (academic performance, criminal behavior), medical diagnosis c. *Clinical judgment*: judge puts together data using subjective methods -ex: psychologists, physicians *Mechanical judgment*: judgment based on an algorithm using well-specified variables -ex: insurance companies, banks *Grove et al (2000)*: meta-analysis of 136 studies comparing clinical vs. mechanical prediction -Wide range of domains (medicine, psychology, psychiatry, academics, etc...) -Results: > Mechanical superior to clinical: 46% > Mechanical = clinical: 48% > Clinical superior to mechanical: 6% -Clinical judgment generally no better or inferior to mechanical prediction d. *Rely on clinical*: stable relationship between cues and outcomes, opportunity to learn, objective feedback *Rely on mechanical*: unpredictable relationship between cues and outcomes

5. Discuss framing effects in problem solving. In your answer you should: a. Describe what is meant by a problem frame. b. Discuss Tversky and Kahneman's (1981) classic study of problem frames (the killer disease questions), outlining their basic methods and findings.

a. *Problem frame*: The way the question/problem is phrased/framed. -The framing effect demonstrates that the outcome of your decision can be inl uenced by two factors: (1) The background context of the choice and (2) the way in which a question is worded—or framed b. Tversky and Kahneman (1981): There is an outbreak of a disease that is expected to kill 600 people, and two programs to fight the disease are being considered (A) 200 people will be saved (B) There's a 1/3 chance that no one will die and 2/3 chance that 600 people will die (A) 400 people will die (B) There's a 1/3 chance that no one will die and 2/3 chance that 600 people will die -The problems are the exact same, just framed differently Results: -For Ss given the first framing: (A) = 72% (200 people saved) (B) = 28% (1/3 chance no one will die, 2/3 chance 600 people will die) -For Ss given the second framing: (A) = 22% (400 people die) (B) = 78% (1/3 chance no one will die, 2/3 chance 600 people will die) -Problem description affected decisions > This violates descriptive invariance > If we were rational, "saving" vs. "dying" should not affect decisions

20. Discuss and describe each of the following heuristics that Gigerenzer proposes: a. Recognition heuristic b. Take-the-best heuristic c. Default heuristic

a. *Recognition heuristic*: if one object is recognized and the other object is not, infer that the one recognized has "more" of the criterion -Assumes that there is a strong correlation between recognition and the criterion -ex: Rafael Nadal played Marcos Daniel at the French Open in 2009, who won? b. *Take-the-best heuristic*: What's the most important? --> base decision on this -ex: Doctor's decisions > Green & Mehr (1997): studied decisions of who was sent to coronary care unit (CCU). Created simple decision tree for doctors --> no loss in accuracy CCU admissions down to 54% -Reduction in info = better decisions c. *Default heuristic*: If there is a default, do nothing about it -Assumes that the values of the decision maker match the values of those setting the default -ex: organ donation > US = 28% (default = have to opt in) > France 99.9% (default = have to opt out)

8. a. What is a sunk cost? Does sunk cost affect decision making? Discuss, using an example. b. Discuss loss aversion, describing in detail Kahneman et al.'s (1990) study of loss aversion.

a. *Sunk cost*: an investment (money, time, emotion) that you can't get back -Shouldn't affect decisions -Yet, have you ever sat through a terrible movie to "get your money's worth"? --> your ticket is a sunk cost b. *Loss Aversion*: People view loss as much worse than the pleasure of gain Kahneman et al (1990): Ss shown a coffee mug -1/2 Ss told they can keep the mug (sellers/owners); the mug is yours -1/2 Ss told they can pick a mug or cash (choosers) -All Ss told to assign a market value > If the price is higher than market value get to keep the mug > If the price is lower than market value get the cash Results (avg price assigned) -Owners: $7.12 -Choosers: $3.12 -People given the mug demand a high price for giving it up

11. Discuss the availability heuristic. In your answer you should: a. Define what is meant by the availability heuristic. b. Discuss Tversky and Kahneman's (1973) study of the availability heuristic (i.e., the study concerning the letter R). c. List two important factors in the availability heuristic, describing the importance and outlining an example of each.

a. *availability heuristic*: People estimate the frequency of an event by the ease of retrieving examples from memory -Greater the ease = more probable the event b. Tversky and Kahneman (1973): asked Ss whether there are more words with R as the 1st letter or 3rd letter -70% rate R as the 1st most common -Why? --> can think of more examples with R first -(R is an uncommon case; most letters do have more words with it as the 1st letter than 3rd letter) c. *Recency*: -your memory is better for items that you've recently seen, compared to items you saw long ago. In other words, those more recent items are more available. -As a result, we judge recent items to be more likely than they really are. -Research on the availability heuristic has important implications for clinical psychology. Consider a study by MacLeod and Campbell (1992), who encouraged one group of people to recall pleasant events from their past. -These individuals later judged pleasant events to be more likely in their future. -The researchers also encouraged another group to recall unpleasant events. -These individuals later judged unpleasant events to be more likely in their future. Psychotherapists might encourage depressed clients to envision a more hopeful future by having them recall and focus on previous pleasant events. *Familiarity*: -The familiarity of the examples—as well as their recency—can also produce a distortion in frequency estimation. -Brown and his colleagues discovered that the media can distort people's estimates of a country's population. -For example, Brown and Siegler (1992) conducted a study during an era when El Salvador was frequently mentioned in the news because of U.S. intervention in Latin America. -In contrast, Indonesia was seldom mentioned. -Brown and Siegler found that the students' estimates for the population of these two countries were similar, even though the population of Indonesia was about 35 times as large as the population of El Salvador. -The media can also influence viewers' ideas about the prevalence of different points of view. For instance, the media often give equal coverage to several thousand protesters and to several dozen counterprotesters.

18. Discuss the role of base rates in decision making. In your answer you should: a. Define what is meant by base rate. b. Describe the base rate fallacy. How is it operating in Kahneman' (2011) example of Tom W? c. How is base rate information important in decision making in medical diagnosis? Also Practice problem (see notes) d. How aware are doctors of the importance of base rate information? Discuss, describing the work of Gigerenzer cited in class.

a. *base rate*: how often something occurs b. *base-rate fallacy*: paying too little attention to important information about base rate Kahneman (2011): -The description of Tom W was highly similar to (i.e., representative of) the stereotype of a computer scientist or an engineer. -Most students in this study used the representativeness heuristic, and they most frequently guessed that Tom W was a graduate student in either computer science or engineering -If people pay appropriate attention to the base rate in this demonstration, they should select graduate programs that have a relatively high enrollment (base rate). These would include the two options "humanities and education" and "social science and social work." -Maybe the students failed to consider that there are more graduate students in the "social sciences and social work" category than in the "computer science" category. - In fact, this description for Tom W is highly representative of our stereotype for students in computer science. As a result, people tend to select this particular answer. c. False positives will outnumber the true positives with very low base rates. For example, the mammogram example in class. Even if your test comes back positive, you really only have an 8.3% chance of having breast cancer. see notes for practice problem d. Gigerenzer: gave 160 gynecologists the problem we did in class -Do a pre-test and then given training on how to convert the problem into frequencies -Before training: 21% -After training: 87%

3. Discuss normative theories of decision making. In your answer you should: a. Provide a basic definition of what is meant by normative decision making. b. Distinguish between expected value and expected utility and provide an example of each. c. Based on what you know about expected utility, why do people play slot machines and lotteries?

a. *normative theories*: some choices are better (or more optimal) than others -One choice is better than other possibilities b. *expected value*: the best choice is the one with the largest financial payoff -ex: (A) 50% chance of winning $50 (0.5 * 50 = $25) (B) 25% chance of winning $110 (0.25 * 110 = $27.50) > If expected value is used, should pick choice B *expected utility*: The personal value we attach to outcomes -getting some money is more important than maximizing financial gain -ex: Say you are really hungry and also broke (A) 85% chance of winning $8 (expected val = $6.8) (b) 25% chance of winning $28 (ex val = $7.00) c. Why do people play the lottery? -ex: (A) 100% chance of winning $1 (expected value = $1) (B) 0.0000000038% chance of winning $20 million (expected value = $0.08) > expected utility of $20 million greater than the utility of $1 -Many decisions can't be explained by expected value or utility

9. Discuss the representativeness heuristic. In your answer you should, a. Define what is meant by the representativeness heuristic. b. Describe how irrelevant details can influence representativeness, providing an example. c. Describe Tversky and Kahneman's (1983) study of representativeness (given as Demonstration 12.4 in your text), outlining their basic methods and findings. d. What is meant by a conjunction fallacy? Did Tversky and Kahneman's (1983) subjects commit a conjunction fallacy? Explain.

a. *representative heuristic*: used when categorizing people or situations; judge the likelihood that something belongs to a category based on how it resembles your idea of the category b. Ex: You've watched a (fair) coin toss come up heads 5 times in a row. If you bet $10 on the next toss, would you choose heads or tails? -Most people choose tails, even though heads or tails is equally likely -Tails seems more representative of randomness c. Tversky and Kahneman (1983): 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 she also participated in antinuclear demonstrations. Now rank the following options in terms of the probability of their describing Linda. Give a ranking of 1 to the most likely option and a ranking of 8 to the least likely option: _____ Linda is a teacher at an elementary school. _____ Linda works in a bookstore and takes yoga classes. _____ Linda is active in the feminist movement. _____ Linda is a psychiatric social worker. _____ Linda is a member of the League of Women Voters. _____ Linda is a bank teller. _____ Linda is an insurance salesperson. _____ Linda is a bank teller and is active in the feminist movement. -Now compare which of these two choices you ranked more likely: (1) Linda is a bank teller, or (2) Linda is a bank teller and is active in the feminist movement. -Tversky and Kahneman (1983) presented the "Linda" problem and another similar problem to three groups of people. One was a "statistically naïve" group of undergraduates. The "intermediate-knowledge" group consisted of first-year graduate students who had taken one or more courses in statistics. The "statistically sophisticated" group consisted of doctoral students in a decision science program who had taken several advanced courses in statistics. -In each case, the participants were asked to rank all eight statements according to their probability, with the rank of 1 assigned to the most likely statement. -Figure 12.1 shows the average rank for each of the three groups for the two critical statements: (1) "Linda is a bank teller" and (2) "Linda is a bank teller and is active in the feminist movement." -Notice that the people in all three groups believed— incorrectly—that the second statement would be more likely than the first. -Think for a moment why this conclusion is mathematically impossible. According to the conjunction rule, the probability of the conjunction of two events cannot be larger than the probability of either of its constituent events. -In the Linda problem, the conjunction of the two events—bank teller and feminist—cannot occur more often than either event by itself, for instance, being a bank teller. c. *conjunction fallacy*: mistake of assuming a conjunction is more likely than a constituent event -Yes, committed a conjunction fallacy: bank teller and feminist can't occur more often than either event by itself