“INSENSITIVITY TO PRIOR PROBABILITY” BIAS IN OPERATIONS
MANAGEMENT CONTEXT
Mohammed AlKhars*, Robert Pavur, and Nicholas Evangelopoulos
College of Business
University of North Texas
Denton, TX 76203-5249
940 565-3107
[email protected]
[email protected]
[email protected]
ABSTRACT
Behavioral operations management (BOM) is a relatively new stream line of research that have
emerged for the last 2 decades. Its primary concern is to put the true human behavior into
consideration when conducting research. BOM is divided into 3 categories: cognitive, social and
cultural. In this paper, the focus is on the first category namely cognitive category. Since managers
sometimes use some heuristics in their decisions’ making, they may commit cognitive biases. The
cognitive bias that is discussed in this paper is called “Insensitivity to Prior Probability” bias. A
scenario, called Restaurant Scenario, has been developed to study this bias. The survey was
distributed to students in a business college. There were two groups of students. The first one
received the survey without training about this bias and the second group received training about
it. The results show that many students chose the biased decision. Moreover, the group which
received training made less biased decision compared with the group which received no training.
Finally, the study shows that delayed gratification can be used to partially predict this cognitive
bias.
*Author for correspondence
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INTRODUCTION
Behavioral Operations management (BOM) gained popularity in the last few years. The main
theme in BOM is to explicitly consider human behavior in OM models. Traditional OM models
use a simplified set of human behavior assumptions. For example, people are assumed to be
deterministic and predictable. Therefore, the dynamic nature of humans is not taken into account.
Another assumption is that OM models usually deal with organizations’ objects such as machines,
materials, cars and trucks (Boudreau et al., 2003). These models ignore the humans who operate
these objects. Because OM models often overlook people, it is estimated that the models developed
in OM literature are applied 50% of the time (Loch and Wu 2007). In order to increase the
practicality of OM models, scholars in OM have advocated research in BOM (Gino and Pisano
2008; Bendoly et al. 2010).
BOM is defined as “OM is concerned with the study of the design and management of
transformation processes in manufacturing and service organizations, building mathematical
theory of the phenomena of interest and testing the theory with field data (derived from surveys,
databases, experiments, comparative case studies, ethnographic observations, etc). Behavioral
Operations Management is a multi-disciplinary branch of OM that explicitly considers the effects
of human behavior in process performance, influenced by cognitive biases, social preferences, and
cultural norms” (Loch and Wu 2007). This definition classifies BOM into 3 categories: cognitive
biases, social preferences and cultural norms. It can be inferred that the scope of BOM is very wide
and deep and research can be performed in different dimensions and contexts to better understand
how humans take decisions related to OM.
The objective of this paper is to develop a scenario that shows how the “Insensitivity to Prior
Probability” cognitive bias can arise in decision-making in an OM context.
LITERATURE REVIEW
Some scholars in decision-making have realized that many people take biased decisions in business
contexts. Instead of focusing their attention on how people should take their decisions, the focus
of these scholars is how people actually make their decisions. Tversky and Kahneman (1974) are
considered pioneers in this field. They conducted a series of experiments especially in gambling
to help them understand how people take their decisions. They observed that people usually use
certain heuristics in their decision-making process. The use of such heuristics may lead to some
cognitive biases. A heuristic is defined as “a rule of thumb used by people to make decisions.” A
cognitive bias is defined as “an observed systematic deviation in decision making.” Tversky and
Kahneman (1974) identified 3 heuristics usually used to make decisions. These heuristics are
representativeness, availability and anchoring. In this paper, only representativeness heuristic will
be discussed.
The representativeness heuristic is used to answer a question of the form “what is the probability
that event A belongs to category B.” In such a case, the decision maker may use the
representativeness heuristic to solve this problem. If event A highly represents category B, the
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decision maker usually assigns high probability to event A. Conversely, if event A is not highly
representative of category B, the decision maker usually assigns a low probability to event A.
The use of representativeness heuristic may lead to 6 cognitive biases:
1.
2.
3.
4.
5.
6.
Insensitivity to prior probability of outcomes
Insensitivity to sample size
Misconception of chance
Insensitivity to predictability
The illusion of validity
Misconception of regression
In this paper, the first cognitive bias will be considered.
Insensitivity to Prior Probability of Outcomes
This bias usually occurs when people are asked to estimate the probability of an outcome. In this
case, the right way is to consider the prior probability of such event. However, some people would
use representativeness heuristic to estimate this probability and ignore the prior probability in their
estimation. A classic example is given by Tversky and Kahneman (1974): “Steve is very shy and
withdrawn, invariably helpful, but with 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.” People are asked
whether Steve is more probably a librarian or a farmer. Since the description of Steve is a
stereotype of a librarian, many people would consider Steve to represent a librarian more than a
farmer. However, a key piece of information that should be used in this example is that there are
20 farmers for each 1 librarian. So, it is more probable that Steve is a farmer and not a librarian.
Debiasing Strategies
Cognitive biases are strongly imbedded in the human mind. It is difficult to completely remove
such cognitive biases. However, literature shows that the negative impact of cognitive biases could
be reduced through the use of debiasing strategies (Kaufmann, Michel and Carter 2009). Debiasing
strategies are defined as “the approaches and sets of actions aimed at reducing the detrimental
influence of decision biases and as such to enhance the rationality and effectiveness of decisions”
(Kaufmann, Michel and Carter 2009). Training is considered one effective strategy to counteract
the negative impact of cognitive biases. By making people aware of the existence of cognitive
biases, people may try to avoid committing such bias. Since OM practitioners would improve the
quality of their decisions with experience, the objective of the training is to make an effective
decision more effective.
Delayed Gratification
Literature shows people who are patient tend to think more about their decisions and therefore they
usually take informed ones. Conversely, people who are less patient usually use their intuition and
therefore may take biased decisions. A good instrument to measure the patience of the person is to
measure his or her delayed gratification. Those who delay their immediate gratification hoping to
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get more reward in the future tend to score high in intelligent tests such as IQ or SAT math.
Therefore, it is expected that those people will take less biased decisions. Fredrick (2005)
developed 17 questions measuring the person’s level of delayed gratification. They include
monetary, massage, tooth pulled, pay of overnight shipping, level of impulsivity, etc. In this paper,
only monetary questions will be used to measure the delayed gratification level of the decision
maker.
RESEARCH METHOD
The objective of this paper is to examine the impact of awareness and delayed gratification on
making biased decision. So, experimental design will be used. One group will be presented with a
scenario without providing awareness of the cognitive bias. The second group will be provided the
scenario with awareness of the cognitive bias. Moreover, the decision maker’s level of patience
would be assessed using delayed gratification questions.
Scenario Used in the Experiment
In order to run this experiment, a scenario called “Restaurant Scenario” has been developed to urge
the respondent to use the representativeness heuristic to take a decisions about the cause of a
problem. The scenario has been designed to see if respondents would commit the “insensitivity to
prior probability of outcomes.” The scenario is described as:
ABC is a chain of buffet-style restaurants. Assume you are the new assistant store manager. Part
of your duties is to maintain food safety procedures. The restaurant offers a soup bar, with six
different types of soup available to the customers. You are aware that, according to the U.S.
Department of Agriculture, thousands of deaths and millions of illnesses each year are directly
linked to foodborne bacteria and other microorganisms. To control bacteria growth in your soups,
it is important to keep their temperatures outside of the so-called danger zone, a range of
temperatures from 40 to 140 0F (5 to 60 0C). Keeping soups at a safe temperature can be
challenging, since they need to be heated when they are cooked, chilled when they are stored, and
reheated when they are about to be consumed by the customers. Therefore, soups pass through the
danger zone twice.
Throughout the day, soups are stored in the refrigerator inside plastic bags. Four times a day,
cold plastic bags are opened and soup is quickly heated on a stove. When offered to the customers,
the six types of soup are kept warm inside six metal containers (bain-maries). Soup temperature
at ABC restaurants is monitored every half hour during the period 11:30am – 10pm, for a total of
22 measurements per day, which are entered into a soup temperature log.
One morning, as you review the previous day’s soup temperature log, you are puzzled ―and
concerned― by a few temperature entries that were around 120 0F (49 0C). When this problem
occurs, the most likely cause is human error related to the handling of the refrigerator (e.g. the
refrigeration temperature setting is too cold) or the stove (e.g. the heating temperature setting is
not hot enough).
While refrigerator problems generally occur six times more frequently than stove problems, you
can recall many recent instances when the soup temperature was around 120 0F toward the end
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of the day and the cause was the stove. When this type of problem can be traced to the refrigerator,
about two-thirds of the time the problem occurs toward the beginning of the day, and only about
one third of the times the problem occurs toward the end of the day. When the stove causes the
problem, the problem tends to occur almost exclusively toward the end of the day. In fact, your
records verify that, among the 12 occurrences of a temperature problem caused by the stove in the
past six months, all 12 (100%) occurred toward the end of the day. Looking at the temperature
log, you see that the problem this time occurred toward the end of the day. You now need to
establish the most likely cause and take specific action.
Q. Given that the problem occurred toward the end of the day, what is the most likely cause of the
low temperature in soups?
1. The refrigerator
2. The stove
In order to solve this problem, it is expected that many respondents will choose the stove as the
most likely cause of the problem. Since the problem occurred toward the end of the day and as the
scenario states if the stove is the cause, then the problem exclusively occurs toward the end of the
day. However, the right way is to consider the prior probability of the outcomes. Since the
refrigerator cause the problem 6 times more than the stove and one third of the time it occurs
toward the end of the day, then the refrigerator causes the problem two times more often than the
stove if the problem occurs toward the end of the day.
Training
One group will be presented the scenario without warning and the other group will be given the
warning. The warning given for the second group is shown below:
As you consider your choice between the refrigerator and the stove, please note that such choices
are sensitive to a well-known cognitive bias, called “Insensitivity to prior probability of
outcomes.” In this bias, the decision maker will jump to an intuitive choice after recognizing a
familiar situation, without properly assessing an underlying probability.
For example, suppose they give you a person’s description as follows: “Steve is very shy and
withdrawn, invariably helpful, but with 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.” Then they ask you:
is Steve more likely to be a farmer or a librarian? You will be tempted to select librarian, due to
the resemblance of the description to a stereotypical librarian. However, there are many more
farmers than there are librarians. Therefore, the description is actually more likely to correspond
to a farmer, even though the percentage of people who fit the description is a minority among
farmers.
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Delayed Gratification
There are 9 questions aiming to measure the delayed gratification of respondents. The 9 questions
will be given for all respondents. These 9 questions are
Q. How likely are you to agree with each of the following statements?
Very Likely A
Equally likely
Very Likely B
A or B
1. Receive (A) $3400 this month or
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[1]
[2]
[3]
[4]
[5]
[6]
(B)$3800 next month
2. Receive (A) $100 now or
(B) $140 next year
3. Receive (A) $100 now or
(B) $1100 in 10 years
4. Receive (A) $9 now or
(B) $100 in 10 years
5. Receive (A) $40 immediately or
(B) $1000 in 10 years
6. Receive (A) $100 now or
(B) $20 every year for 7 years
7. Receive (A) $400 now or
(B) $100 every year for 10 years
8. Receive (A) $1000 now or
(B) $100 every year for 25 years
9. Lose (A) $1000 this year or
[7]
(B) $2000 next year
ANALYSIS AND DISCUSSION
The survey was distributed to students studying at University of North Texas (UNT). 211 students
participated in this survey. Tables 1 and 2 show age distribution and gender distribution of the
sample.
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Table 1. Age distribution of sample
Age
Group
No.
%
18-20
21-25
26-35
36-50
51-older
Missing
Total
27
12.80
145
68.72
34
16.11
3
1.42
1
0.47
1
0.47
211
99.99
The majority of respondents are in the range 18-35 years. This sums up to 98% of the respondents.
The percentages sum up to 99.99% because of rounding error.
Table 2. Gender distribution of sample
Gender
No.
%
Male
123
58.29
Female
83
39.34
Missing
5
2.37
Total
211
100.00
Table 3 shows the results of committing the “Insensitivity to Prior Probability” bias:
Table 3. Distribution of incorrect (biased) responses
0 (No Warning)
1 (Warning)
Total
Training
#
103
108
211
%
49%
51%
Incorrect Answers
#
%
78
76%
68
63%
146
69%
Among 211 participants, 103, which account for 49%, received no training and 108, which
accounts for 51%, received training. Among 103, who received no raining, 78 participants chose
the wrong answer. This is equal to 76%. This percentage drops to 63% for those who receive
training. The overall percentage of people choosing the incorrect decision is 69%
In order to analyze the data further, logistic regression has been used. The dependents variable is
whether the respondent answer is correct or false. So, the dependent variable is binary variable.
The value of the binary variable is 1 if the answer is wrong and therefore there is cognitive bias.
If the answer is right, the variable value would be 0 and there is no cognitive bias. There are two
sets of independent variables. The first one is binary variable measuring the existence of training.
If the respondent is given a training, the variable value will be 1. If there is no training, the variable
value will be 0. The second set of independent variables is the 9 questions measuring the delayed
gratification of the respondents. These variables are continuous variables measured using LikertScale with 7 points. Moreover, there will be interaction variables formed by multiplying the 9
questions of delayed gratification with training. The results of the logistic regression using SPSS
is shown in table 4. Interactions are denoted by underscore. For example, Training_by_Q1 is the
interaction of Training and Q1
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Table 4. Logistic regression analysis predicting an incorrect response
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Training
Training_by_Q1
Training_by_Q2
Training_by_Q3
Training_by_Q4
Training_by_Q5
Training_by_Q6
Training_by_Q7
Training_by_Q8
Training_by_Q9
Constant
B
.262
-.542
.167
-.124
.231
.232
-.233
.178
.092
2.098
-.364
.635
-.289
.190
-.403
-.357
.380
-.303
.040
-.348
S.E.
.124
.211
.222
.190
.183
.182
.194
.180
.150
1.148
.162
.242
.255
.233
.222
.212
.234
.219
.176
.731
Wald
4.496
6.564
.567
.426
1.589
1.616
1.445
.986
.370
3.340
5.030
6.877
1.280
.665
3.285
2.823
2.644
1.915
.050
.226
df
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Sig.
.034
.010
.451
.514
.208
.204
.229
.321
.543
.068
.025
.009
.258
.415
.070
.093
.104
.166
.822
.635
Exp(B)
1.300
.582
1.182
.883
1.259
1.261
.792
1.195
1.096
8.148
.695
1.887
.749
1.210
.668
.700
1.462
.739
1.040
.706
Significant factors are shaded using gray color. The p value for such factors is below 0.10. for
example, training is a significant factor because its P value is 0.068. Q1 is a significant factor with
p value of 0.034. Moreover, the interaction term of Q1 and training is significant with p value of
0.025. Similarly, Q2 and the interaction term of Q and training are significant as their p Value is
less than 0.10. Finally, the interaction terms of Training_by_Q5 and Training_by_Q6 are
significant although Q5 and Q6 are insignificant.
This study shows that the majority of students will commit the “Insensitivity to Prior Probability”
bias. Literature states that cognitive biases are embedded in human mind and cannot be removed
completely. This statement is supported in this research. Although providing training about the
nature of such cognitive bias has improved the accuracy of answering the question correctly, still
some people make wrong decision. So, by providing people with simple training about cognitive
biases, it is expected that the rate of making biased decisions would decrease. Moreover, this
research shows that delayed gratification can partially be used to predict the occurrence of
“Insensitivity to Prior Probability” bias.
LIMITATIONS AND CONCLUSION
This study has two major limitations. The first one is that the sample consists primarily of students
in a business college. Although these students eventually would graduate and work in business,
we should be cautious not to generalize based on this sample. The second limitation is that the
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“Insensitivity to Prior Probability” bias is researched based on only one scenario. This scenario
shows that people will ignore the prior probability of outcomes and would choose the wrong
answer. However, in order to make conclusions of such cognitive bias, more than one scenario
with different OM contexts should be developed to make sure that this cognitive bias is so
common.
Based on the authors’ knowledge, this the first study of cognitive biases applied to an OM context.
Researchers in OM field can extend this study by developing scenarios in the “Insensitivity to
Prior Probability” bias to verify the results shown in this study. Moreover, they can develop
scenarios that tackles the other 12 cognitive biases proposed by Tversky and Kahneman (1974).
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Boudreau, J., Hopp, W., McClain, J. O., & Thomas, L. J. (2003). On the interface between
operations and human resources management. Manufacturing & Service Operations Management,
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Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic perspectives,
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Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service
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Kaufmann, L., Michel, A., & Carter, C. R. (2009). Debiasing strategies in supply management
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