Judgement in managerial decision making pdf

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TEFL courses in person and tutored those taking distance your lesson plan so that they can talk to you Putting Your Le Expertise in judgment and decision. Library of Congress Cataloging-in-Publication Data Bazerman, Max H. Judgment in managerial decision making/Max H. Bazerman, Don Mooreth ed. p. em. JUDGMENT IN. 'MANAGERIAL. DECISION MAKING. EIGHTH EDITION. Max H. Bazerman. Harvard Business School. Don A. Moore. The University of California .

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Judgement In Managerial Decision Making Pdf

Judgment In Managerial Decision Making Max H Bazerman pdf, Free Judgment In. Managerial Decision Making Max H Bazerman Ebook Download, Free. Behavioral decision research provides many important insights into managerial behavior. From negotiation to investment decisions, the authors weave. module provides an overview of decision making and includes discussion of many of the common biases .. Judgment in managerial decision making (8th ed .).

Anthony b, Pamela L. This is particularly so under crisis conditions, where there is little time and information available for choice consideration. While the management literature has recently seen more empirical and theoretical support for intuition and tacit knowledge in the decision process, the role of emotion has not played a prominent role. This article advances management decision theory by proposing a conceptual model of managerial decision-making that underscores the role of emotions in an intuitive decision process under crisis conditions. The article holds particular salience to human resource managers, as they are the custodians of decisions made about people in the organization. These decisions, by definition, carry much weight in both ethical and financial terms, and their importance is magnified immeasurably under crisis conditions. D Elsevier Inc.

Chapter 7: Fairness and ethics in decision making. When do people care about fairness? When will individuals accept suboptimal outcomes in order to maintain fairness? This chapter examines how we think about fairness and explores inconsistencies in our assessments of fairness. Chapter 8: Common investment mistakes. Perhaps the domain that has been most inRuenced by decision research has been behavioral finance. In the last decade, we have learned a great deal about the mistakes that investors commonly make.

This chapter will explore these mistakes and apply the messages of this book to help readers become wiser investors. This chapter outlines a framework to help the reader think about two-party negotiations. The focus is on how you can make decisions to maximize the joint gain available to both sides, while simultaneously thinking about how to obtain as much of that joint gain as possible for yourself.

Common Biases Chapter Negotiator cognition. This chapter looks at the judgmental mistakes we make in negotiations.

The resulting framework shows how consumers, managers, salespeople, and society as a whole can benefit simultaneously from less biased negotiations. Chapter Six strategies for improved decision making. The final chapter evaluates six explicit strategies for improving judgment: 1 use prescriptive decisionmaking procedures, 2 acquire expertise, 3 debias your judgment, 4 reason analogically, 5 take an outsider's view, and 6 understand biases in others.

This chapter will teach you how to use the information in the book to permanently improve your decisions. If you guessed that there are more American firms on the list, you are in the majority.

Most people at least, most Americans polled estimate that there are more American companies than foreign companies on the list. Most people also guess that the American firms are larger than the foreign companies listed. However, this majority response is incorrect. In fact, there are thirteen American firms on the list and fourteen based outside of the United States. What's more, the nonU.

Why do most people overestimate the frequency of American firms on the list? Because the American company names are more familiar, more recognizable, and more memorable to Americans than the foreign company names. This problem illustrates the availability heuristic, which we introduced in Chapter 1.

For Americans, the names of American firms are more available in our memories than the names of foreign firms after reading the list. We err in assuming that the prevalence of American firms in our minds mirrors the real world. Awareness of the bias resulting from the availability heuristic should inspire us to question our judgments and adjust them accordingly.

As we noted in Chapter l , individuals develop rules of thumb, or heuristics, toreduce the information-processing demands of making decisions. By providing managers with efficient ways of dealing with complex problems, heuristics produce good decisions a significant proportion of the time. However, heuristics also can lead managers to make systematically biased judgments. Biases result when an individual inappropriately applies a heuristic when making a decision.

This chapter is comprised of three sections that correspond to three of the general heuristics we introduced in Chapter l: the availability heuristic, the representativeness heuristic, and the confirmation heuristic. We will discuss a fourth general heuristic, the affect heuristic, in Chapter 5.

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The three heuristics covered in this chapter encompass twelve specific biases that we will illustrate using your responses to a series of problems. The goal of the chapter is to help you "unfreeze" your decision-making patterns by showing you how easily heuristics become biases when improperly applied. Once you are able to spot these biases, you will be able to improve the quality of your decisions.

Before reading further, please take a few minutes to respond to the problems presented in Table 2. Problem 1. Please rank order the following causes of death in the United States between and , placing a 1 next to the most common cause, 2 next to the second most common, etc.

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Problem 2. Estimate the percentage of words in the English language that begin with the letter "a. Estimate the percentage of words in the English language that have the letter "a" as their third letter. Problem 4. Lisa is thirty-three and is pregnant for the first time. She is worried about birth defects such as Down syndrome. Her doctor tells her that she need not worry too much because there is only a 1 in 1, chance that a woman of her age will have a baby with Down syndrome.

Nevertheless, Lisa remains anxious about this possibility and decides to obtain a test, known as the Triple Screen, that can detect Down syndrome.

The test is moderately accurate: When a baby has Down syndrome, the test delivers a positive result 86 percent of the time. There is, however, a small "false positive" rate: 5 percent of babies produce a positive result despite not having Down syndrome.

Lisa takes the Triple Screen and obtains a positive result for Down syndrome. Given this test result, what are the chances that her baby has Down syndrome? A certain town is served by two hospitals.

In the larger hospital, about forty-five babies are born each day. In the smaller hospital, about fifteen babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage of boys born varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of one year, each hospital recorded the days on which more than 60 percent of the babies born were boys.

Which hospital do you think recorded more such days? The larger hospital b. The smaller hospital c. About the same that is, within 5 percent of each other Problem 6. You and your spouse have had three children together, all of them girls. Now that you are expecting your fourth child, you wonder whether the odds favor having a boy this time. What is the best estimate of your probability of having another girl?

A percentage that falls somewhere between these two estimates 6. You are the manager of a Major League Baseball team, and the season has just ended. One of your most important jobs is to predict players' future performance. Currently, your primary interest lies in predicting batting averages for nine particular players.

A measure of a player's performance, batting averages range from 0 to 1. Larger numbers reflect better batting performance. You know the nine players' batting averages, and must estimate each one's batting average. Please fill in your guesses in the right-hand column. Player Linda is thirty-one years old, single, outspoken, and very smart. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and she participated in antinuclear demonstrations.

Rank the following eight descriptions in order of the probability likelihood that they describe Linda: a. Linda is a teacher in 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 who is active in the feminist movement. Problem 9. Take the last three digits of your phone number. Add the number one to the front of the string, so that now you have four digits. Think of that number as a year. Now try to estimate the year that the Taj Mahal was completed. Was it before or after the date made by your phone number?

Which of the following instances appears most likefy? Which appears second most likely? Drawing a red marble from a bag containing 50 percent red marbles and 50 percent white marbles.

Drawing a red marble seven times in succession, with replacement i. Drawing at least one red marble in seven tries, with replacement, from a bag containing 10 percent red marbles and 90 percent white marbles.

Problem Ten uncertain quantities are listed below. Do not look up any information about these items. For each, write down your best estimate of the quantity. Next, put a lower and upper bound around your estimate, so that you are confident that your 98 percent range surrounds the actual quantity.

Estimate Lower Upper a. Wal-Mart's revenue b. Microsoft's revenue c. World population as ofJuly d.

Market capitalization price per share times number of shares outstanding of Best download as of July6, e. Market capitalization of Heinz as of July 6, f. Rank of McDonald's in the Fortune g. Rank of Nike in the Fortune h.

Number of fatalities due to motor vehicle accidents in the United States in i. The national debt of the U. The U. If you had to describe the relationship between baseball players' batting averages in one season and their batting averages in the subsequent season, which of the following four descriptions would yoH pick? Zero correlation: Performance is entirely unpredictable, in the sense that knowing how well a player hits one year does not help you predict how well he is going to hit the next year.

Weak correlation of about. Strong correlation of about. Perfect correlation of 1. The player with the highest batting average in one season always has the highest batting average the next season. Even if you got the order right or came close, you probably underestimated the magnitude of difference between the first two causes and the last three causes. Vivid deaths caused by cars, guns, and drugs tend to get a lot of press coverage.

The availability of vivid stories in the media biases our perception of the frequency of events toward the last three causes over the first two. As a result, we may underestimate the likelihood of death due to tobacco and poor diet, while overestimating the hazards of cars, guns, and drugs. Many life decisions are affected by the vividness of information.

In the fall of , however, sexual behavior in Dallas was dramatically affected by one vivid piece of data that may or may not have been true. In a chilling interview, a Dallas woman calling herself C.

Although C. There are many more important reasons to be concerned about AIDS. However, C. The availability heuristic describes the inferences we make about event commonness based on the ease with which we can remember instances of that event.

Tversky and Kahneman cite evidence of this bias in a lab study in which individuals were read lists of names of well-known personalities of both genders. One group was read a list in which the women listed were relatively more famous than the listed men, but the list included more men's names overall.

The other group was read a list in which the men listed were relatively more famous than the listed women, but the list included more women's names overall. After hearing their group's list, participants in both groups were asked if the list contained the names of more women or men. In both groups, participants incorrectly guessed that the gender that included the relatively more famous personalities was the more numerous.

Participants apparently paid more attention to vivid household names than to less well-known figures, leading to inaccurate judgments. While this example of vividness may seem fairly benign, it is not difficult to see how the availability bias could lead managers to make potentially destructive workplace decisions. The following came from the experience of one of our MBA students: As a downloading agent, he had to select one of several possible suppliers.

He chose the firm whose name was the most familiar to him. He later found out that the salience of the name resulted from recent adverse publicity concerning the firm's extortion of funds from client companies!

Managers conducting performance appraisals often fall victim to the availability heuristic. Working from memory, vivid instances of an employee's behavior either positive or negative will be most easily recalled from memory, will appear more numerous than commonplace incidents, and will therefore be weighted more heavily in the performance appraisal.

Judgment in Managerial Decision Making, 8th Edition

The recency of events is also a factor: Managers give more weight to performance during the three months prior to the evaluation than to the previous nine months of the evaluation period because it is more available in memory.

In one clever experiment that illustrates the potential biasing effect of availability, Schwarz and his colleagues asked their participants to assess their own assertiveness. Some participants were instructed to think of six examples that demonstrated their assertiveness-a fairly easy assignment.

Other participants were instructed to come up with twelve instances of their own assertiveness-a tougher task. Those who were supposed to come up with twelve instances had more trouble filling out.

Consistent with the predictions of the availability heuristic, those who were asked to generate rrwre examples actually wound up seeing themselves as less assertive, despite the fact that they actually listed more instances of their own assertiveness. Because it was more difficult for them to come up with examples demonstrating their assertiveness, they inferred that they must not be particularly assertive. This pattern may be sensible for some types of risks.

After all, the experience of surviving a hurricane may offer solid evidence that your property is more vulnerable to hurricanes than you had thought or that climate change is increasing your vulnerability to hurricanes. This explanation cannot account for trends in the download of earthquake insurance, however. Geologists tell us that the risk of future earthquakes suBsides immediately after an earthquake occurs.

The risk of experiencing an earthquake becomes more vivid and salient after one has experienced an earthquake, even if the risk of another earthquake in the same location diminishes. Perhaps it ought not to be surprising that our memories and recent experiences have such a strong impact on our decisions.

Nevertheless, it can be fascinating to di? Bias 2: Retrievability based on memory structures Problem 2. The recommendations that come from people in a manager's network are more likely to be of a similar background, culture, and education as the manager who is performing the search.

As these first two biases ease of recall and retrievability indicate, the misuse of the availability heuristic can lead to systematic errors in managerial judgment.

We too easily assume that our available recollections are truly representative of the larger pool of events that exists outside of our range of experience. As decision makers, we need to understand when intuition will lead us astray so that we can avoid the pitfall of selecting the most mentally available option. Problem 3. Most people estimate that there are more words beginning with "a" than words in which "a" is the third letter.

In fact, the latter are more numerous than the former. Words beginning with " a" constitute roughly 6 percent of English words , whereas words with "a" as the third letter make up more than 9 percent of English words. Why do most people believe the opposite to be true? Due to the relative ease of recalling words starting with "a," we overestimate their frequency relative to words that have "a" as a third letter.

Tversky and Kahneman demonstrated this retrievability bias when they asked participants in their study to estimate the frequency of seven-letter words that had the letter "n" in the sixth position.

Their participants estimated such words to be less common than seven-letter words ending in the more memorable three-letter "ing" sequence.

However, this response pattern must be incorrect. Since all words with seven letters that end in "ing" also have an "n" as their sixth letter, the frequency of words that end in "ing" cannot be larger than the number of words with "n" as the sixth letter. Tversky and Kahneman argue that "ing" words are more retrievable from memory b ecause of the commonality of the "ing" suffix, whereas the search for words that have an "n" as the sixth letter does not easily generate this group of words.

Sometimes the world structures itself according to our search strategies. Retail store location is influenced by the way in which consumers search their minds when seeking a particular commodity. Why are multiple gas stations at the same intersection? Why do "upscale" retailers want to be in the same mall? Why are the biggest bookstores in a city often located within a couple blocks of each other?

An important reason for this pattern is that consumers learn the location of a particular type of product or store and organize their minds accordingly. To maximize traffic, the retailer needs to be in the location that consumers associate with this type of product or store.

Other times, the most natural search strategies do not serve us as well. For instance, managers routinely rely on their social networks to identifY potential employees. Given this test result, what are the chances "that her baby has Down syndrome? How did you reach your answer?

If you are like most people, you decided that Lisa has a substantial chance of having a baby with Down syndrome. The test gets it right 86 percent of the time, right? The problem with this logic is that it ignores the "base rate"-the overall prevalence of Down syndrome.

For a thousand women Lisa's age who take the test, an average of only one will have a baby with Down syndrome, and there is only an 86 percent chance that this woman will get a positive test result. The other women who take the test will have babies who do not have Down syndrome; however, due to the test's 5 percent false positive rate, just under 50 Therefore, the correct answer to this problem is that Lisa's baby has only a 1.

Due to the simplifYing guidance of the representativeness heuristic, specific information about Lisa's case and her test results causes people to ignore background information relevant to the problem, such as the base rate of Down syndrome.

This tendency is even stronger when the specific information is vivid and compelling, as Kahneman and Tversky illustrated in one study from Some participants were told that this description was selected from a set of seventy engineers and thirty lawyers.

Others were told that the description came from a list of thirty engineers and seventy lawyers. Next, participants were asked to estimate the probability that the person described was an engineer. Even though people admitted that the brief description did not offer a foolproof means of distinguishing lawyers from engineers, most tended to believe that the description was of an engineer. Their assessments were relatively impervious to differences in base rates of engineers 70 percent versus 30 percent of the sample group.

In the absence of a personal description, people use the base rates sensibly and believe that a person picked at random from a group made up mostly of lawyers is most likely to be a lawyer. Thus, people understand the relevance of base-rate information, but tend to disregard such data when individuating data are also available. Ignoring base rates has many unfortunate implications.

Entrepreneurs think that the base rate for failure is not relevant to their situations; many of them lose their life savings as a result. Similarly, unnecessary emotional distress is caused in the divorce process because of the failure of couples to create prenuptial agreements that facilitate the peaceful resolution of a marriage.

The suggestion of a prenuptial agreement is often viewed as a sign of bad faith. However, in far too many cases, the failure to create prenuptial agreements occurs when individuals approach marriage with the false belief that the high base rate for divorce does not apply to them.

About the same that is, within 5 percent of each other Most individuals choose C, expecting the two hospitals to record a similar number of days on which 60 percent or more of the babies born are boys. People seem to have some basicjdea of how unusual it is to have 60 percent of a random event occurring in a specific direction.

However, statistics tells us that we are much more likely to observe 60 percent of male babies in a smaller sample than in a larger sample.

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This effect is easy to understand. Half of the time, three flips will produce more than 60 percent heads. However, ten flips will only produce more than 60 percent heads about 17 percent of the time.

Three thousand flips will produce more than 60 percent heads only. However, most people judge the probability to be the same in each hospital, effectively ignoring sample size. Although the importance of sample size is fundamental in statistics, Tversky and Kahneman argue that sample size is rarely a part of our intuition.

When responding to problems dealing with sampling, people often use the representativeness heuristic. For instance, they think about how representative it would be for 60 percent of babies born to be boys in a random event. As a result, people ignore the issue of sample size-which is critical to an accurate assessment of the problem.

Consider the implications of this bias for advertising strategies. Market research experts understand that a sizable sample will be more accurate than a small one, but use consumers' bias to the advantage of their clients: "Four out of five dentists surveyed recommend sugarless gum for their patients who chew gum.

If only five or ten dentists were surveyed, the size of the sample would not be generalizable to the overall population of dentists. Bias 5: Misconceptions of Chance Problem 6.

The problem with this reasoning is that the gender determination of each new baby is a chance event; the sperm that determines the baby's gender does not know how many other girls the couple has.

This question parallels research by Kahneman and Tversky showing that people expect a sequence of random events to "look" random. Simple statistics, of course, tell us that each of these sequences is equally likely because of the independence of multiple random events.

Problem 6 triggers our inappropriate tendency to assume that random and nonrandom events will balance out. Will the fourth baby be a boy? But your earlier success producing girls is irrelevant to its probability. Tversky and Kahneman note: "Chance is commonly viewed as a self-correcting process in which a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium. In fact, deviations are not corrected as a chance process unfolds , they are merely diluted.

In some situations, our minds misconstrue chance in exactly the opposite way. In sports such as basketball, we often think of a particular player as having a "hot hand" or being "on fire. Most sports fans, sports commentators, and players believe that the answer is "higher.

However, it is wrong! In an extensive analysis of the shooting of the Philadelphia 76ers and the Boston Celtics, Gilovich, Vallone, and Tversky found that immediately prior shot performance did not change the likelihood of success on the upcoming shot.

Out of all of the findings in this book, this is the effect that our managerial students often have the hardest time accepting. We can all remember sequences of five hits in a row; streaks are part of our conception of chance in athletic competition.

However, our minds do not think of a string of "four in a row" shots as a situation in which "he missed his fifth shot. Carsten K. First published: December Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Volume 8 , Issue 4 December Pages Related Information.

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