OLEAD465:

Introduction

Lesson Overview

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As mentioned in the material from Lesson 1, decision making often suffers from the presence of certain types of biases that lead to questionable, if not erroneous, judgments of two kinds: probability (or likelihood) and value. For instance, the CEO of a company interested in a possible acquisition might seriously overestimate the likelihood that the acquisition would have a positive payoff for his or her company (probability), as well as what expanding operations would do for its long-range economic health (value). The CEO enters into negotiations, makes the acquisition, and later discovers that the action has proved to be a boondoggle--a complete waste of time. This lesson and the next one are both concerned with the first type of judgment (probability) and the mental shortcuts decision makers indulge in; these heuristics can distort estimates of the likelihood of particular outcomes the choices made. Lesson 2 deals specifically with a variety of biases stemming from the two categories of such shortcuts: the “Availability Heuristic” and the “Representativeness Heuristic.”

Reading Assignment

• Judgment in Managerial Decision Making, "Common Biases," (pp. 31-46).

Lesson Objectives

The reading, overview of key ideas, and thought questions in this lesson will help you to:

• understand how the availability and representativeness heuristics can lead to biased estimates of probability, or systematic deviations from warranted estimates;
• identify several species of biases that stem from the two types of heuristics;
• appreciate the connection of the biases to problematic decision making; and
• relate your understanding of the forms to your own decision making, as well as to that of others.

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Biases Relating to the Availability Heuristic

Bazerman and Moore identify as biases associated with the availability heuristic what they refer to as “ease of recall” and “retrievability.” In the first bias, one can overestimate the probability of an outcome because of recent and/or particularly vivid experiences. In the second bias, one can underestimate the likelihood of an outcome because it is difficult to come up with pertinent information. Following is an example of each:

• Ease of Recall. A person might decide to follow a standard operating procedure because it is easier to remember it even though, in fact, it has a lower likelihood of producing a desired result than some other approach or procedure. Ease of recall has no necessary relationship to the likelihood of the future occurrence of a decisional outcome.


• Retrievability. Because a manager is more aware of the shortcomings of subordinates he or she sees frequently, he or she might underestimate their overall contributions, assume that they will not improve in the future, and give such individuals less favorable performance appraisals than others who are performing no better or even not as well. Simply because some types of information are easier to acquire than others, it does not follow that they are, therefore, more accurate gauges of what is probable or likely.

For more information concerning the availability heuristic, see this site: Changing Minds' Theory of Availability Heuristic

Biases Relating to the Representativeness Heuristic

Biases associated with the representativeness heuristic can and frequently do distort decision makers’ perceptions of probability. We rely on consistency and superficial resemblances with stereotypic notions that we all carry around in our heads. Therefore, certain types of occurrences appear to be more or less likely in our perceptions than they are in reality. Bazerman and Moore identify five such biasing influences: “insensitivity to base rates, insensitivity to sample size, misconceptions of chance, regression to the mean, and the conjunction fallacy.” Following is an illustration for each type in Table 2.1:

Biases on Perceptions of Probability

Insensitivity to Base Rates	A person starts a new business because of a success story in a related area. The individual feels the description of the other business founder matches his or her personality (both are intelligent, committed, and go-getters). Without due consideration of failure rates among start-ups in general, the person launches a business and later fails. The stereotype for what contributes to success in the example is overpowering the judgment of likelihood and obscures the relevance, as well as utility, of other potentially helpful information.
Insensitivity to Sample Size	A store manager receives a complaint about a given employee on three consecutive days and decides that something has to be done about said employee because “there seems to be a pattern emerging” even though over, say, a 30-day period, the likelihood of a “pattern” of upsetting customers might be quite small. The smaller the sample on which one bases a probability estimate, the more likely we are to see systematic events that are, in fact, random and well within the realm of chance.
Misconceptions of Chance	A gambler who is losing badly decides to continue gambling because his/her odds of winning “just have to improve.” Chance is not self-correcting. For instance, the odds of coming up with heads in flipping a coin on any given occasion are 50% each time. Those odds do not change simply because one might flip tails five or six times in a row.
Regression to the Mean	A firm fires a former top salesperson because his/her figures for the last quarter are down, and the person must, therefore, “be slipping.” However, it gives a bonus to another person whose performance had been repeatedly poor, but is “surprisingly good” because he or she recently improved. What those involved fail to realize is that extreme deviations from the average tend not to remain at the extremes over time, but instead revert toward the average, as when a basketball player who typically scores, say, 5 to 10 points a game seems to catch on fire and scores 20 points in one contest. It would be much too soon to begin celebrating the “dramatic change” in the player’s performance.
The Conjunction Fallacy	A champion athlete receives an offer to take a position as a coach on the assumption that being good in athletic performance makes it likely that s/he will also be good in coaching. The odds that one can be good in both coaching and athletic performance are worse than the odds that one will be good in either activity alone. Joint probabilities cannot exceed the value of what is the most probable of the items being conjoined. For instance, if the odds that an employee at a firm will be happy working there are 80% and the odds that the same person will be productive are 60%, the odds that he or she will be both happy and productive are only 48%.  Joint probability is the product of multiplying one probability by the other--in this case .8 x .6.

Table 2.1. Types of Representativeness Biases

To learn more about the representativeness heuristic, visit: Changing Minds' Representativeness Heuristic.

Thought Questions

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1. Before reading Bazerman and Moore’s discussion in Chapter 3 of biases that result from the inappropriate use of heuristics in making inferential judgments, complete the seven problems that head each of the sections relating to the availability and representativeness heuristics (pp. 34-46). Then read the discussion of each. In how many cases did you make the sort of misjudgment Bazerman and Moore point out in their discussion of each problem? Did the explanations of the appropriate and inappropriate responses make sense to you? Were there any items for which you did not understand the explanation? Are there any items for which you chose the wrong response according to Bazerman and Moore, but for which you continue to think you were correct? Why, in that case, do you think you were correct?


2. Of the seven biases Bazerman and Moore discuss on pp. 34-46, select one that resonates with your own experience or for which you can recall a recent example that you think may have contributed to an erroneous inference that, in turn, led to an unfortunate, or at least unwarranted, decision. An example might be something like a decision not to take a course in math on the grounds that it was likely to be boring--where the inference seems to reflect an overestimate of the percentage of math courses that are boring relative to courses in general--only later to discover that virtually everyone who took the course found it to be quite interesting. Identify the bias leading to the inference, classify it, discuss the reasons why you may have had the bias, and then note how you eventually determined that you had made a poor decision that grew from an inference affected by the bias.

Activities

1. Biases Essay

   For Lesson 2, prepare an essay of approximately 1,000 words in which you address every aspect of Thought Question 2. Describe the decision context clearly before undertaking the analysis of the event. Be sure to ground the analysis in appropriate references to the reading. For instance, “The type of bias that I believe I exhibited is the one that Bazerman and Moore refer to as “...” (p. xx), and by which they mean “...” (p. xx).

   Expected Outcome

   In completing this lesson and preparing the required document for it, you will be better equipped to recognize circumstances in which your reliance on the availability and representativeness heuristics can foster biased estimates of probability that, in turn, may lead to poor decisions. You will also have an enriched vocabulary for distinguishing among different types of specific biases regarding two categories of heuristics.

   Contribution to Course Grade: 8%

   Please submit your essay in the Biases Essay Drop Box.