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Chapter 12 - Page

Chapter 12 - Descriptive Approaches to Decision Making
THIS CHAPTER WILL DISCUSS:

1. The difference between "optimizing" and "satisficing" models of individual decision making.

2. The effect of "decision heuristics" on individual and group decision making.

3 . The utilization of information during group discussion.

4. The meaning of the term "groupthink."

INTRODUCTION

In this chapter, we return to the issue of decision making. This chapter discusses how people and groups make decisions. Then, in Chapter 13, "Formal Procedures for Group Decision Making," we shall describe how some theorists think groups should make decisions. Thus we can say that this chapter contains a description of how decisions are made, and Chapter 13 contains a prescription for how decisions perhaps should be made.

This chapter is a necessary stepping-stone to Chapter 13. Before scientists can create theories about how groups should make choices, they must have knowledge about how people tend to approach decisions. In essence, they need to know what group members are capable of doing. What decision-making capabilities do humans have? As we shall see, there is much disagreement about the answer to this question.

THEORETICAL APPROACHES TO INDIVIDUAL DECISION MAKING


Optimizing Decision Theory

We will begin with a discussion of two different types of theories about how individual people make decisions. Some scientists have adopted one of these, the "optimizing" type of theory. Optimizing theories make a number of assumptions about how people make decisions. First, decision makers are believed to consider all possible decision options. Second, decision makers are seen as assessing all of the available information when making their choice. Third, decision makers are seen as choosing that option that provides them with the best possible outcome.



The Subjective Expected Utility Model
To begin our discussion, we will examine the "subjective expected utility", or "SEU" model. What is this model? It is an equation that allows us to predict the decision that individual people will make when faced with a number of options. Use of the SEU model implies that people act as if they had calculated the "expected utility" of each option. When people do this, they choose the alternative that they believe has the highest expected utility.

Demonstration of the SEU model. Let us go through a demonstration of how a person could use the SEU model from start to finish as he or she chooses a course of action. Fischoff, Goitein, and Shapira (1982) presented this full-blown example. Let us say that you are the person making a decision.

You are home, and you must decide how to get to class on a nice spring day. The classroom is five miles away, and you have an hour to get there. The SEU model requires that you take the following steps:

1. You must list all feasible options. It turns out that you can walk, take a bus, ride your bicycle, or drive.

2. You next need to enumerate all the possible consequences of each action. For this example, you can list two consequences. One is getting to class on time and the other is getting some needed exercise in the bargain.

3. Imagine that each option has occurred and assess the attractiveness or averseness of each of its consequences. For instance, how attractive is getting to class on time by walking? How attractive is getting exercise through walking? You can use a scale of one to ten to decide the attractiveness, or "utility," of your consequences. On this day, you decide that both consequences to walking are attractive. Hence, for the option of walking, getting to class on time gets a utility rating of nine and getting sufficient exercise also receives a nine.
Other similar evaluations can follow. Imagine that you give almost every option the utility of nine for each consequence. The only exception is the attractiveness of bicycling as an exercise. It is lowered to six by the prospect of having to sit in class while sweating.

4. Evaluate the probability that the consequences of each option will actually occur. For instance, will walking to class really get you to class on time? Will walking also truly lead to exercise? Again, using a scale of one to ten, you can assess this probability. You feel that walking is decent exercise, so it probably gets a probability rating of six regarding your desired consequence of getting exercise. You will not get to class on time, however, so walking gets a probability rating of only one for this consequence.

You can rate other options the same way. The bus is very reliable transportation (probability = nine), but the only exercise you would get is walking to the bus stop (prob. = two). Bicycling is good exercise (prob. = eight), and barring a flat tire, you will get to class on time (prob. = eight). Driving is dependable if you can find a parking space. If you cannot find a space, you will be late for class (prob. = five). Also, unless you park far from class, you will get no exercise (prob. = one).

5. You need to compute the expected utility, or "EU," of each option. You do so by multiplying how much each consequence of the option is worth to you (which is its basic utility rating) by the probability that it will occur. The product of this multiplication is the EU for the consequence. The EU of the entire option is the sum of the EU of all possible consequences within that option. For example, consider the option of riding your bicycle. To find out if you should or not, you want to know the EU of that option. You believe that riding your bicycle is associated with two consequences: being on time and getting exercise. Each of these consequences has its own EU. To find out just what should happen if you ride your bike, you need to examine the EU of both its consequences. Table 12.1 shows these calculations.

6. Finally, you choose the outcome with the greatest EU. As you can see, you should ride your bicycle to class today.

The SEU model is thus an optimizing decision model that is based on a person's own personal estimates of probability and value. We can use it in circumstances in which it is difficult to obtain objective estimates of decision-making outcomes. This is often true with decisions that people make in the "real world." For example, how can a person place an objective value on a scenic view? Yet millions of people decide every year to visit the Grand Canyon.





Table 12.1

On Time







Exercise










Means

(Prob X

Utility)

Plus

(Prob X

Utility)

Equals

EU

Walk

(1 X

9)

Plus

(6 X

9)

Equals

63

Bus

(9 X

9)

Plus

(2 X

9)

Equals

99

Bicycle

(8 X

9)

Plus

(8 X

6)

Equals

120

Drive

(5 X

9)

Plus

(1 X

9)

Equals

54

Using the SEU model, we can assume that people make their best decision when they try to get the best results for themselves or for whomever the decision should benefit. This idea fits in with optimizing decision theory. Remember though that the judgments of probability and utility are made from each individual's standpoint. Therefore, the option that is chosen as the "best" is likely to vary from person to person.



Criticisms of the SEU model. However, the model falls prey to other criticisms. First, it assumes that decision making is in some sense "compensatory." This means that a good subjective estimate can counterbalance a bad subjective estimate. In our example, bicycling received a bad utility rating because of the inconvenience of becoming sweaty. However, it also received a good estimate for the probability of getting to class on time. Thus, bicycling was the best choice.
The problem is that some circumstances clearly cannot meet this compensatory assumption. For instance, a situation can be "conjunctive." When this happens an option that fails in one criteria cannot make up for that failing. All other criteria are immaterial. Fischoff et al. (1982) used the example of a couple planning a vacation to illustrate the idea of the conjunctive situation. The couple wishes to travel to a place that is reasonably priced, available, sunny, and quiet. They say they will stay at home if no place can meet all four criteria. For instance, if they arrive at a place that is cheap, available, and sunny, their whole vacation will be ruined if their hotel is close to a noisy highway.
Other situations may be "disjunctive." This means a person will choose an option if it is adequate on any criterion. Fischoff et al. used an investment opportunity to illustrate this idea. The investment is acceptable if it is a good speculation, a good tax shelter, or a good hedge against inflation. The person will make the investment if it is any of these three things. The point that Fischoff et al. make is that different circumstances require different procedures for decision making.
Second, scientists have criticized the model because they are not sure that it accurately reveals the steps that people take as they make decisions. For example, assume that we have seen Janet bicycling to class. We wish to discover how she made her decision to use her bicycle. We ask Janet to tell us of the various alternatives she had available to her, as well as the possible consequences of each. We further ask her to tell us the probability and utility of each consequence, in relation to every possible action. We then compute the expected utility of each option. The model predicts that Janet would have bicycled. We conclude that Janet used the SEU model to make her decision.
Our conclusion could easily be wrong. It may be that Janet only considered the probabilities of getting sufficient exercise and of arriving at class on time. To make her decision, she simply added the probabilities together. A model for her decision is illustrated in Table 12.2.



Table 12.2




Probabilities










Means

On Time

Plus

Exercise

Equals

EU

Walk

1

Plus

6

Equals

7

Bus

9

Plus

2

Equals

11

Bicycle

8

Plus

8

Equals

16

Drive

5

Plus

1

Equals

6

As you can see, Janet made the same decision that the SEU model predicted she would make. However, she did not consider the utility of each consequence. Janet was only concerned with the probability of whether the consequence would occur. It was true that Janet could report the utility of each consequence when we asked her. Still, she did not use these utility ratings when she originally made the choice to bicycle to class.


We can propose many other models that would make the same prediction. Each would show that bicycling would be the best course of action for Janet, based on her situation. Note, for example, the probability ratings for getting sufficient exercise. These alone could lead to a prediction that bicycling was the best option for Janet.

Thus, many models can predict decisions as well as the SEU model. This means scientists must turn to other evidence to discover how people make decisions. Researchers have done just that. Some evidence has even cast doubt on the theory behind the SEU model. These findings suggest that people may not naturally optimize when they make decisions, even when scientists can predict their decisions by using the SEU model.



Satisficing Decision Theory

Simon (1955) was the first prominent theorist to doubt that people are able to calculate the optimal choice. He believed that it is impossible for people to consider all the options and all the information about those items that the SEU and similar models assume. Simon proposed his own model of decision making as an alternative to the optimizing approach. He called his proposal the ''satisficing'' decision model. It implies that people think of options, one by one, and choose the first course of action that meets or surpasses some minimum criterion that will satisfy them.


Simon believed that decision makers establish a criterion (their "level of aspiration") that an alternative must meet in order to be acceptable. People examine possible options in the order that they think of them. Eventually, they accept the first option that meets their criterion. To illustrate Simon's idea, we shall return to the example of choosing how to get to class.

Example


Suppose four possible courses of action will help you get to class. Each has a number that represents its subjective value. One of the possibilities is walking, which has a value of 6. The others are taking the bus (10), bicycling (12), and driving (5). Keeping these subjective values in mind, you begin the process of deciding on a course of action.
First, you establish a level of aspiration. You decide, for example, that an option must have the value of at least 8 before it will satisfy you. Next, you evaluate your options. You first think of walking. It has a value of 6. This does not meet the level of aspiration. Therefore, you reject it as a possibility. The second option that comes to your mind is taking the bus. It is worth 10. This meets the level of aspiration, so you accept it.

Satisfactory versus optimal. You may wonder why our example above did not once again lead to the decision to bicycle to class. We know that bicycling is the optimal decision, because it has a value of 12. However, Simon believed that you would never consider bicycling. The idea of taking the bus came into your head before you had a chance to think about bicycling. Once you found the satisfactory option of taking the bus, you never thought of any other possibilities. Hence, you end up with a satisfactory option, but not the optimal one.
Despite the example above, Simon believed that, in the long run, the satisficing process leads to the optimal decision more often than not. He believed that a person's level of aspiration can rise and fall over time. This fluctuation depends on the respective ease or difficulty of finding satisfactory options. In our example, you were able to find a satisfactory option fairly easily. Taking a bus was only the second alternative you considered. Perhaps you will become more demanding the next time you wonder how to get to class. You reached a decision so easily the first time you may feel more confident that there is an even better option available to you.

In this situation, you will probably raise your level of aspiration. It is hoped that the criterion will continue to shift upward over time. Ideally, it should reach the point where only the optimal choice will be satisfactory. If this happens, the results of the satisficing model will approximate the outcome of an optimizing model. People will make their best choice despite their inability to optimize.



Decision Heuristics

Simon's satisficing model is an example of a "heuristic." A heuristic is a simplified method by which people make judgments or decisions. These methods approximate the results of more complex optimizing models, but they are easier for people to use. Many studies have shown that people usually use heuristics when they make judgments and decisions. This evidence continues to mount.




Tversky and Kahneman Heuristics

In a classic article in 1974, Tversky and Kahneman proposed three heuristics that people seem to use when they estimate the probabilities of events. As with Simon's satisficing model, these heuristics are far simpler than analogous optimizing methods. They also usually lead to the optimal judgment, as Simon's methodology does.


However, heuristics do have a negative side. When they backfire, the errors that result are not random. Thus, the results will not cancel each other. Instead, when people follow a heuristic model, their errors will be biased in ways that are harmful to decision making. This is an important aspect of the heuristics that we shall examine.

Representativeness heuristic. The first heuristic that Tversky and Kahneman proposed was the representativeness heuristic. The representative heuristic is relevant when people attempt to estimate the extent to which objects or events relate to one another. The representativeness heuristic maintains that, when people do this, they note how much objects or events resemble one another. They then tend to use this resemblance as a basis for judgment when they make their estimates.
As with other heuristics, the representativeness heuristic usually leads to correct judgments. Nisbett and Ross (1980) provide an example of this. Someone asks Peter to estimate how clearly an all-male jury relates to the United States population as a whole. He needs to decide how representative of the population the jury is. He will no doubt give the jury a low estimate, and he would be correct. Clearly, the population of the United States is made up of both men and women. Therefore, an all-male jury does not "look like" the general population. Peter notes this and makes the correct, low estimate.
However, in many circumstances basing judgments on resemblance leads to error. For instance, people may have additional information that can help them find out the probability that the objects or events they consider are related. In these situations, people are incorrect if they use resemblance as the sole basis for judgments.

In one of Tversky and Kahneman's studies, the researchers gave participants a personality description of a fictional person. The scientists supposedly chose the person at random from a group of 100 people. They told participants that 70 people in the group were farmers and 30 were librarians. They then asked the participants to guess if the person was a farmer or librarian. The description of the fictional person was as follows:

Steve is very shy and withdrawn. He is invariably helpful, but he has little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail.

Most people in the experiment guessed that Steve was a librarian. They apparently felt that he resembled a stereotypical conception of librarians. In so doing, the participants ignored other information at their disposal. They knew that Steve was part of a sample in which 70 percent of the members were farmers. Thus, the odds were that Steve was a farmer, despite his personality. The participants should have taken these odds into account when they made their decision.



Cause and result. Individuals may also err when they judge whether an event is the result of a certain cause. This might happen if they look for the extent to which the event resembles one of its possible causes. If people use this resemblance, they may choose an incorrect cause.
For example, imagine being shown various series of the letters "H" and "T." You are told that each series came from tossing a coin. One side of the coin was "H" ("Heads") and the other side was "T" ("Tails"). Many people think that a series similar to HTHTTH is most likely caused by a random tossing of the coin. This is because the series looked random to them. In contrast, they do not think that a series such as HHHHHH or HHHTTT resulted from a random process. They are wrong. A random process can cause all of the different series.

Many people misunderstand random processes. They think the result of a random cause should "look" random. This is not necessarily true. We can see how a random process would lead to results that look rather unrandom. On the first toss of a coin, for example, there is a 50 percent chance of H and a 50 percent chance of T. No matter what the result of this first flip is, the second toss will have the same odds. There will again be a 50 percent chance of either H or T. Thus there is a 25 percent chance of any of the following combinations: HH, HT, TH, or TT. Continuing this logic, for six tosses there is a 1.5625 percent chance of HTHTTH, HHHTTT, HHHHHH, and all of the other 61 possible series combinations of coin flips. As you can see, all the different series combinations have the same odds, and all have a random cause.

A similar error is the "gambler's fallacy." This is the feeling that, for instance, after a series of HHHHHH, the next flip ought to be a T. The "gambler" believes this because a T would "look" more random than another H would. However, as long as the coin is fair, there is still a 50-50 chance that the next flip will be an H.

Hence, the representativeness heuristic often leads to correct answers, but it can also cause people to err in their judgments. Outcomes that resemble one another are not necessarily related.



Availability heuristic. Tversky and Kahneman's second proposal was the availability heuristic. This heuristic maintains that the ease with which examples of an object or an event come to mind is important. People tend to estimate the probability that an event will occur or that an object exists, based on whether they can think of examples easily.
As with the representativeness heuristic, this strategy usually leads to satisfactory decisions. For example, someone may ask you if more words in the English language begin with "r" or with "k." You can think of words at random, tallying them up as they come into your mind. You are then able to figure out the percentage of words that begin with each letter. In this way, you could no doubt correctly decide which letter starts the most words. Similarly, availability helps the satisficing model work as well. One reason satisficing usually results in the optimum choice is that the best option usually comes to mind quickly.
However, as with the representativeness approach, the availability heuristic can easily lead people astray. There are many factors that bring an object to our attention. Some of these factors are not conducive to good judgment.
One study revealed that the factor of how well known something is can cause people to make incorrect decisions. In the experiment, participants heard a list of names of men and women. The researchers then asked them to judge if the list had more men's names or more women's names. The list actually had an equal number of names from each gender. However, some of the names were more well-known than others. The well-known names were mainly from one gender, and the participants tended to choose that gender as the one that supposedly dominated the list.
In another study, experimenters asked participants which English words were more common, those with "r" as their first letter or those with "r" as their third letter. Most people said that words that begin with "r" are more numerous. They probably did so because it is easy to think of relevant examples, such as "rat," "rabbit," "really," etc. However, this was the wrong answer. You need only look at any random piece of writing to see this. In fact, you can look at the words in the sentence that described this experiment: "participants," "words," "were," "more," and "first." However, in comparison with words that begin with "r," it is relatively difficult to think of examples in which "r" is the third letter in a word. This is because we tend to use first letters to organize words in our minds.
Thus, the availability heuristic often leads to correct conclusions. However, it can also create errors. People may think quickly of well-known or vivid examples. It may be, however, that the more well-known options are not the best decisions that people can make.



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