Decision Making

Decision Making

INTRODUCTION

Think about your class schedule for this semester. Chances are that it came together as a result of you engaging in decision making—the selecting of the best alternative from among several options. For instance, why are you in this psych course? Perhaps it’s because you decided to major in psychology instead of in business or English or to remain undeclared in your major. Maybe this is an elective course, and you choose it over another course in economics or geology. You may be taking this course because it is a distributional requirement, and perhaps you had to decide among sections offered in the morning, afternoon, and evening.

In theory, the mental process for making any decision is simple: You determine the relative costs and benefits of each choice, then rationally choose the option with the most favorable outcomes. For instance, if you chose psychology for an elective course, perhaps you weighed the value of what you would learn in psychology against that of some other course, such as geology, and then rationally choose psychology because you expect it will be more applicable to your life now or in the future.

Until the late 20th century, this highly rational process for decision making is how most psychologists and economists assumed people acted. Expected utility theory, the dominant theory of decision making, held that people simply calculate the “expected utility,” or value, of the possible choices for any decision they need to make and choose the option that maximizes the desired outcome.

In the 1970s, psychologists Daniel Kahneman and Amos Tversky realized that they themselves made less-than-rational decisions that were not consistent with expected utility theory. They wondered why and engaged in research in which they asked people what they would do in different situations and under different contexts to evaluate whether people always rationally maximize expected value.

In this ZAPS lab, you will participate in a series of “mini-experiments” from Kahneman and Tversey’s classic research on decision making.

Instructions

You will answer a series of ten questions. Many of the questions describe a hypothetical scenario and ask what you would do in that scenario. Other questions simply ask you to make a judgment or estimate some value. Your responses to these questions will not be graded; just try to answer each one with the choice, judgment, or estimate you would honestly make if you were notparticipating in a psychology experiment.

EXPERIENCE

Question 1: You have a chance to participate in a guessing game. A coin is tossed, and you must guess whether heads or tails will come up.

· If you guess heads and heads comes up you will win $1.

· If you guess tails and tails comes up you will win $2.

· If you’re wrong, you won’t lose anything.

Would you participate in the game?

Click or tap a choice to answer the question.

Yes, I would participate.

No, I would not participate.

EXPERIENCE

Question 1: You have a chance to participate in a guessing game. A coin is tossed, and you must guess whether heads or tails will come up.

· If you guess heads and heads comes up you will win $1.

· If you guess tails and tails comes up you will win $2.

· If you’re wrong, you won’t lose anything.

Would you participate in the game?

Click or tap a choice to answer the question.

Yes, I would participate.

No, I would not participate.

Experimenters often ask questions like this one to make sure participants are answering honestly. You were asked whether you would participate in a “bet” where you always win and can never lose. Obviously, everyone should participate. So if a participant answers “no” to such a question, it is a red flag to the experimenter that the participant is not paying attention or is not taking the experiment seriously.

Question 2: You have a chance to gamble on the toss of a coin.

· If the coin shows heads, you will win $125.

· If the coin shows tails, you will lose $100.

Would you accept the gamble?

Click or tap a choice to answer the question.

Yes, I would accept the gamble.

No, I would not accept the gamble.

In the case of Question 2, you would win $125 if heads comes up but lose only $100 if tails comes up, so the expected value is 125 – 100 = +25, and expected utility theory predicts everyone should take the bet.

However, as this graph indicates, 80% of respondents in fact say that they would not take the bet. Kahneman and Tversky’s prospect theoryholds that people evaluate the psychological prospects of the choices, rather than the objective expected values. In this case, you must weigh the fear of losing $100 against the hope of gaining $125, and for most people, the former is more salient than the latter. Research has shown that people are generally more eager to avoid costs than potentially gain benefits, a principle known as loss aversion.

Question 3: Would you accept a gamble that offers:

· a 10% chance to win $95

· a 90% chance to lose $5?

Click or tap a choice to answer the question.

Yes, I would accept the gamble.

No, I would not accept the gamble.

Question 4: Would you pay $5 to participate in a lottery that offers:

· a 10% chance to win $100

· a 90% chance to win nothing?

Click or tap a choice to answer the question.

Yes, I would participate in the lottery.

No, I would not participate in the lottery.

Graph of typical results for Questions 3 and 4The expected values are identical in this question and the previous one:

· In Question 3’s gamble, there is a 10% chance you will win $95 (expected value = 0.1 × $95 = +$9.50), and a 90% chance to lose $5 (expected value = -$4.50).

· In Question 4’s lottery, you also have a 10% chance to win $95 ($100 minus the $5 you paid for the ticket) and a 90% chance to lose $5 (the price you paid for the ticket).

However, as the graph shows, most people would not accept the gamble, but would participate in the lottery.

This illustrates another principle demonstrated and described by Tversky and Kahneman: framing. The way a choice is worded (framed) changes the psychology of how people perceive the choice, and prospect theory says it is the value of the choices’ prospects that determines what people do, as opposed to the objective expected values.

Note that the actual expected value is (0.1 × $95) − (0.9 × $5) = +$5 for both the gamble and the lottery, so according to expected utility theory, everyone should both take the gamble and buy the lottery ticket, since doing nothing has an expected value of $0.

Question 5: You have decided to see a play where admission is $10 a ticket. As you enter the theater, you discover that you have lost a $10 bill. Would you still pay $10 for a ticket to the play?

Click or tap a choice to answer the question.

Yes, I would still pay for a ticket.

No, I would not pay for a ticket.

Question 6: You have decided to see a play where admission is $10 a ticket. As you enter the theater, you discover that you have lost the ticket. Would you pay $10 for another ticket to the play?

Click or tap a choice to answer the question.

Yes, I would pay for another ticket.

No, I would not pay for another ticket.

Questions 5 and 6 are another pair of questions that illustrate framing effects.

Graph of typical results for Questions 5 and 6You should recognize that, as with the previous two questions, the objective situation is identical in both cases: you just lost something worth $10 (a ten dollar bill in Question 5 and your first ticket in Question 6), and you must now choose whether to spend another $10 to see the play.

But, as with Questions 3 and 4, people do not respond identically to the two situations. As shown in the graph, a majority of people say they would still pay for a ticket after discovering they’ve lost a $10 bill, but a majority of people say they would not pay for a second ticket after losing their first one.

Question 7: The coast redwood (Sequoia sempervirens) is the tallest species of tree on Earth. Do you think the height of the tallest redwood tree in the world is more or less than 1,200 feet? (No Googling, please.)

Click or tap a choice to answer the question.

more than 1,200 feet

less than 1,200 feet

Question 8: Do you think the height of the tallest redwood tree in the world is more or less than 400 feet? (Again, please refrain from Googling.)

Click or tap a choice to answer the question.

more than 400 feet

less than 400 feet

Questions 7 and 8 illustrate anchoring, which is another type of framing effect described by Kahneman and Tversky.

As shown in the first set of bars (“High Anchor”) in the graph at right, when people are first asked whether the tallest redwood is more or less than 1,200 Graph of typical results for Question 8feet (as you were), most respond in the second question that the tallest redwood is more than 400 feet (green bar).

However, when people are first asked whether the tallest redwood is more or less than 100 feet, most respond to the second question by estimating that the tallest redwood is less than 400 feet (red bar in the “Low Anchor” condition).

This shows that when making their estimates in the second question, people start from the “anchor” established in the first question. (In fact, the actual height of the tallest measured redwood tree is 379 feet.)

Question 9: Kristen is 31 years old, single, outspoken, and very bright. She majored in English literature. As a student, she was deeply concerned with issues of discrimination and social justice, and later she participated in the “Occupy Wall Street” demonstrations of 2011.

Which of the following statements is more probable?

Click or tap a choice to answer the question.

Kristen is a bank teller.

Kristen is a bank teller and a liberal Democrat.

This question illustrates yet another discovery by Tversky and Kahneman. If you think about the choices carefully, you will realize that the first choice must be more likely than the second choice, because every liberal Democrat bank teller is also a bank teller, while not all bank tellers are liberal Democrats. But Kristen sounds so much like a liberal Democrat (and so unlike a Graph of typical results for Question 9bank teller) that most people mistakenly choose the second option, as shown in the graph at right.

Tversky and Kahneman postulated that when making decisions under conditions of uncertainty, we automatically apply heuristics (aka “rules of thumb”) to try to reduce the uncertainty. In this case, we apply the representativeness heuristic: Kristen’s description is more representative of a stereotypical liberal Democrat than of a stereotypical banker, so our intuitive heuristic tells us that the second option seems more plausible. Working out which option is more probable takes more mental work, so we may not bother to do it—and even when we do, the pull of the more plausible choice is difficult to overcome.

Question 10: If a random word is taken from an English dictionary, is it more likely that the word starts with a K, or that K is the third letter? (Again, no Googling please.)

Click or tap a choice to answer the question.

It is more likely that the word starts with a K.

It is more likely that K is the third letter of the word.

Graph of typical results for Question 10 Here, you probably applied another of the Kahneman and Tversky-discovered heuristics: availability. Lacking the time to go through every word in the dictionary and answer the actual question, you substituted a different question (“is it easier to think of words that start with K or have K as the third letter?”), which is answerable: words that start with K (e.g. kangaroo, kick, ketchup) come to mind much more readily than words with K as the third letter (e.g. ask, bike, cake). As the graph at right shows, 90% of people go with this intuition.

As is the case with most heuristics, availability works, on average, better than no decision rule at all, but it can fail us at times, including this one: in fact, there are about three times as many English words whose third letter is K than there are words that start with K.

Data Introduction

The ten questions you answered in this ZAPS lab represent several different “mini-experiments” conducted by Tversky and Kahneman. The results table you will see in this section is designed to summarize all the data together. There is one row for each question, with the following columns:

· A summary of the question you were asked.

· The “Correct” response to the question. In some cases there is a factually correct response (e.g., the height of the tallest redwood tree); in other cases, we list as the correct response the response predicted by expected utility theory—the one that an entirely rational respondent would choose to maximize his or her expected value. After the correct response we give the percentage of people in a neutral reference group (a group of people who are not currently taking a psychology class) who chose this option.

· The “Incorrect” response to the question, and percentage of people in the reference group who chose this response.

· Your response.

· The percentages of other students in your class who chose the “Correct” and “Incorrect” responses.

The response cells in the table are color-coded to help highlight the most important results:

· The responses chosen by the majority of people in the reference group are shown on a blue background, while the responses chosen by the minority are shown on an orange background.

· Questions where people generally make a systematic “error” (compared to expected utility theory) can thus be easily seen: they are the rows where the “Correct” answer cell is orange and the “Incorrect” answer cell is blue.

· Your response is shown in blue if it matches the majority response, or orange if it matches the minority response.

Your answer for each question will be shown in blue type if which of the following applies?

Click or tap a choice to answer the question.

Your answer matches the answer that the majority of people typically give.

Your answer matches the answer that the minority of people typically give.

Your answer does not match the one predicted by expected utility theory.

Your answer matches the one predicted by expected utility theory.

YOUR DATA

DISCUSSION

By taking the time to ask people what decisions they would make and carefully phrasing their questions in interesting ways, Kahneman and Tversky revolutionized the study of decision making, showing that people evaluate the psychological prospects of the choices, rather than the objective expected values. Put another way, expected utility theory predicts what individuals would do if they were entirely rational; Tversky and Kahneman’s prospect theory research revealed what most people actually do.

For his work on decision making, Kahneman won the Nobel Prize for Economics in 2002; Tversky would have shared the prize had he not passed away in 1996.

As you saw and read as you worked through the examples, Tversky and Kahneman revealed some of the specific ways in which psychological prospects can substantially differ from expected values. Here is a review:

· The principle of framing states that the way a choice is worded can substantially change its psychological prospects. One of the most powerful framing effects involves whether a choice is framed in terms of gains or losses. For example, people prefer lotteries, which frame the prospect in terms of potential gains, to straight-up bets which frame the prospect in terms of how much one might lose.

· Context can have profound effects on people’s judgments when making estimates, as when anchoring causes one judgment to be affected by the results of a previous estimate, even though both judgments were intended to have been made independently.

· When making decisions under conditions of uncertainty, we automatically apply heuristics (aka “rules of thumb” or mental shortcuts) to try to reduce the uncertainty. For example, the representativeness heuristic directs us to choose the most plausible conclusion when we are unsure of which conclusion is most probable, and the availability heuristic tells us to replace a question we cannot realistically answer with a similar question whose answer is more readily available. Like other heuristics, representativeness and availability usually provide us with a “better than nothing” answer, but sometimes the pull of a heuristic is so strong that we ignore more definitive information that really should form the foundation of our judgment.

Why might people sometimes have difficulty reasoning scientifically? Do you think that if people had more advanced training in areas such as statistics, economics, and psychology, they would make decisions that are more economically rewarding?

You will initially receive full credit for any answer, but your instructor may review your response later.

Submit Answer

LEARNING CHECK

Answer the questions below to complete this ZAPS activity. Your performance in this section accounts for 10% of your grade.

Consider the following gamble. A standard pack of playing cards, including 26 red cards (diamonds and hearts) and 26 black cards (spades and clubs), is shuffled, and the card on top of the deck is turned face up.

· If the face-up card is red, the gambler wins $11

· If the face-up card is black, the gambler loses $9.

Expected value theory predicts which of the following?

Click or tap a choice to answer the question.

a) Most people will accept the gamble.

b) Most people will reject the gamble.

c) People will be equally likely to accept or reject the gamble.

Now consider the same gamble again:

· If the face-up card is red, the gambler wins $11

· If the face-up card is black, the gambler loses $9.

Prospect theory predicts which of the following?

Click or tap a choice to answer the question.

a) Most people will accept the gamble.

b) Most people will reject the gamble.

c) People will be equally likely to accept or reject the gamble.

In a 1997 experiment, participants were first asked whether the Indian leader Mahatma Gandhi died before or after a certain age, then were asked to guess the precise age at which Gandhi died. People who were first asked whether or not Gandhi died at age 9 gave an estimate (50 years) much lower on average than those who were first asked whether or not he died at age 140 (67 years). This experiment is a perfect example of which of the following?

Click or tap a choice to answer the question.

a) loss aversion

b) the anchoring effect

c) the representativeness heuristic

d) the availability heuristic

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