Decision Making Biases and Heuristics

This assignment delves into the realm of decision-making biases and heuristics, examining how these cognitive shortcuts can influence judgments and choices. Students will analyze various types of biases, such as availability heuristic, confirmation bias, and anchoring bias, using provided research articles and real-world examples like the Volkswagen Dieselgate scandal. The goal is to understand the mechanisms behind these biases and explore strategies for mitigating their negative effects on decision-making.

Running head: EXPLANING BIASES IN DECISION MAKING
Explaining biases in decision-making
Name of the University
Name of the Student
Author note

1
EXPLAINING BIASES IN DECISION MAKING
Executive Summary
The chief purpose of the report was to provide a critical evaluation of the concepts that explain
bias in making decisions in an organizational context. The report was divided in two parts – Part
1 and Part 2 - with Part 1 being an essay explaining the quote by Herbert Simon and Part 2 being
the report giving examples of biases from real world application. Three concepts majorly,
availability, bounded rationality and confirmation help explain biases in decision-making. These
concepts have been explained in Part 1 while Part 2 has provided a case study to analyze real
world situations where biases affect decision-making. The report concludes with an overall
summary of the findings and analysis and further suggestions into this field.

2
EXPLAINING BIASES IN DECISION MAKING
Table of Contents
Part 1: Essay: Critical examination of three concepts explaining bias............................................3
Introduction......................................................................................................................................3
Explaining Simon’s quote................................................................................................................3
Concepts explaining bias in decision-making.................................................................................5
Conclusion.......................................................................................................................................6
Part 2: Report: Case Study...............................................................................................................7
Introduction......................................................................................................................................7
Scenarios of decision making from real world................................................................................7
Recognizing bias..............................................................................................................................8
Methods by which bias may be measured or evaluated in the scenario..........................................9
Strategies applied to overcome bias..............................................................................................10
Conclusion.....................................................................................................................................11
References......................................................................................................................................12

3
EXPLAINING BIASES IN DECISION MAKING
Part 1: Essay: Critical examination of three concepts explaining bias
Introduction
Management and business demands managers to make decisions every day on a wide
range of issues. Humans make decisions regularly and these decisions are influenced some or the
other factor. These factors in addition are the results of the biases that affect the decision-
making. Decision-making is a process that is continuous and has to be performed either with own
choice or without. Maine, Soh and Dos Santos (2015), describe decision making as the right
choice at the right time for achieving excellence in organizational management.
According to Montibeller and Winterfeldt (2015), biases often hamper decision-making
that further leads to failure of the organization. The author further points out different kinds of
biases that include self-interest bias, social harmony bias, action-oriented bias and stability bias.
The following essay, while introducing Herbert Simon’s quote, discusses three chief
concepts that may help in explaining bias in decision-making. The essay elaborates Simon’s
views on rational human choices and the influences of external forces on these choices.
Explaining Simon’s quote
“The capacity of the human mind for formulating and solving complex problems is very
small compared with the size of the problems whose solution is required for objectively rational
behavior in the real world—or even for a reasonable approximation to such objective rationality ”
(Simon, 1957).
In the above quote, Herbert Simon speaks about the limited capability of human mind to
make plans to solve complex problems. He states that the problems are bigger in size that the
human mind capability to solve them. The solutions, states the author, are vital to humans as
these define rational behavior or even for roughly acquiring the rationality to make decisions.
The above quote is cited as the ‘principle of bounded reality’. According to the author, limited
information restricts the rationality of individuals in decision-making. The limitations are not
confined to information only, these go beyond cognition and time. He explains that if the
principle turns out to be true then the objective of classical economic theory cannot be attained.

4
EXPLAINING BIASES IN DECISION MAKING
The objective here is to predict a rational human’s behavior without having to do practical
research of his psyche.
The consequences of this principle have also been described by the author. As the first
consequence, the rational human would try to create a reality that involves simple models in
which he acts and sustains. This simple model created by the rational man is not even
approximately favorable to the model that real world has. In order to understand the rational
human’s behavior, says the author, one must comprehend the means that led to the creation of
the simplified model. In this way, one can decipher the psychological traits of the human that
pertain to his intellect, emotion and logical thinking. Foss and Weber (2016) argue that in the
present form of transaction economics, bounded rationality principle takes a back seat giving
way to opportunism. However, the authors also state that an augmented form of bounded reality
does mitigate problems in transaction cost economy, while opportunism takes a back seat.
In the second consequence of the bounded rationality principle, Simon explains that there
has to be restrictions to human rationality otherwise; there would be no existence of
administrative theory. Administrative theory rests in the fact that human rationality has practical
limitations and these limitations are dynamic, depending upon the organizational setting where
the individual’s decisions occur.
This paper explains the decision-making application on human mind’s rational behaviour
which is used to formulate and solve any complex problems in the context of the real world. In
explaining the concept of rationality by Simon, there is a need of examining rationality in terms
of heuristics that are of four types. Availability, confirmation, bounded rationality and
representativeness. It can be said that the theory of organization can never subsist without the
rational choice of theory (Maitland & Sammartino, 2015). It is best described to be intendedly
rational which is based on two species that is entrepreneur and consumer.
Heuristics are psychological decisions made by the individual to get solution of the most
complicated problems. These factors help the individuals to make decisions faster but do not
follow any regulations therefore have a lot of decision errors associated with the judgement
conducted by the individuals. The rational behaviour relates to the decision making process
based on the choices. These selections or choices are proved to be most beneficial for the

5
EXPLAINING BIASES IN DECISION MAKING
individual though they do not always prove to be beneficial from monetary aspect. Simon’s
research chiefly focus on the utility factor which can be emotional or other kind that assist people
in their decisionmakingprocess. Simon has built his theories on these utility factors and described
them in explaining common biases leading to judgmental error.
Concepts explaining bias in decision-making
Availability Heuristic has been a mentalshortcut which depends on immediate instances
coming in the mind of the people while they evaluate some particular topics, concepts, methods
as well as decision. In this context, the research states that the Availability Heuristic is heavily
weighed by the people (Beach & Lipshitz, 2017). For them, if something can be easily recalled,
that it must be more important than the other alternative solutions because other solutions were
not recalled at the time of necessity. It seems to the easiest way to form judgment for complex
problems. These are basicallydone through percentage basis and probability basis. Due to this
reason, these are not always accurate and lead to error in judgement. Therefore, it can be said
that this kind of decision-making process is manipulative as people tend to use the readily
available facts to found their beliefs hence completely biased. This concept illustrates that
external manipulations anticipated to escalate the subjective involvement of simplicity of
remembrance are proved to affect the extent of recall (Shepherd, Williams & Patzelt, 2015). In
addition to this, these factors make the decision making process difficult to regulate the obtained
approximations of likelihood or frequency. These are typicality based on the phenomenal
experiences of people or on the biased samples of evoked information.
The participants while making decision excessivelyrely on the various heuristics
for aptitude and speed. Representative heuristic (RH) is important as it is very economical. At
the time of decision making, there may be one of the two things identifiable in that case people
definitely select the recognisable or known one. Here they utilize as well as reacha decision with
the slightest amounts of information which they possess about the recognisable one. The research
reveals that the recognized memory is perceptive, reliable and proved to be more accurate than
the unknown one because even a smaller extent of recognition effects in more correct decisions
(Montresor et al., 2015). In such cases, even a smaller amount of biasness can be present in
judgement. Some people useadditional information simultaneously withusing their

6
EXPLAINING BIASES IN DECISION MAKING
Representative Heuristics because they cannot rely only on the recognisable factors alone while
making decision.
According to Harrison, Mason and Smith (2015), Simon’s theorydefines Bounded
rationality to create a way between the pre-established ends with the paths to reach the decided
ways. It is explained as an assumption which points out the limits to the reasoning powers of the
agents yet looks at the concept of judiciousness within the existing constraints (Toplak, West &
Stanovich, 2017). However, these have specifications in terms of value therefore, these are
beyond the extent of science. The relation drawnby the factors of bounded reality between pre-
established and desired goals, completely depends on theevaluation of the decision. This concept
requires to point out all possible options, predicting of consequences followed by these possible
options and finally measuring or evaluating all sets of options with consequences. From all these
sets of alternative options including their consequences, one is selects in the decision making
procedure.
This concept has proved to be vital for any organisation, which requires proper
acknowledgement of all the possible options. It also needs contemplation about the outcomes of
the all alternative options and the valuation and measurement of the outcome of the
consequences of each of the options but in doing so it often makes biased decisions (Elbanna,
Kapoutsis & Mellahi, 2017). People often tend to make approximations which leads to fall
towards the anchor parts whereas the real values tend to be far away from the primarily planted
anchors. Through bounded rationality, it can overcome natural bias and ascribe to perfectly
rational decisions.
Conclusion
Therefore, it can be concluded that the concept of rational behaviour gets biased by the
human skills as well as limited knowledge. To achieve the essentialobjectives and decided goals,
these conceptsby Simon are vital to be discussed. The notion of rational behaviour as associated
biases described under the light of heuristics namely availability, representativeness and bounded
awareness. These have been considered with the limitations of influences that are commonly
external to human beings. The essay highlights these various concepts in the light of arguments
from different scholars who have significant contributions to this domain. In addition, the essay

7
EXPLAINING BIASES IN DECISION MAKING
presents a detailed discourse on the statement made by Herbert Simon on the principle of
bounded rationality. He made a significant contribution to the world of decision-making by
proposing this concept. The essay tries to establish a connection between heuristics and biases in
decision-making by explaining Simon’s quote. A systematic approach has been maintained in the
essay to explain the quote and the concepts related to it.
Part 2: Report: Case Study
Introduction
The research conducted by Herbert Simon to delve into human cognition to dig out the
possible reasons for biases in decision-making yielded fruitful results. However, it did not
influence the community at large. He rejected the perfect rationality outright and invented a new
principle of bounded rationality. His approach further accentuated the confines of the cognitive
system, the transformation of practices due to proficiency, and the direct experimental study of
cognitive processes included in decision-making (Matters, 2018). The report shall analyze the
effectiveness of his principles and concepts by presenting scenarios of decision-making from the
real world. Further, the report will examine the relevancy of Simon’s concepts of rationality by
applying these to the given scenarios. In addition, it will analyze the methods used to recognize
bias, the strategies to overcome these and improved decision-making in the future. The two
scenarios chosen for the analysis include Volkswagen’s disastrous decision to rig emission test
and Motorola’s negligence to trace market development.
Scenarios of decision making from real world
Launching a new product in the market could be a possible scenario where decision-
making is crucial. It is vital for any company to carry out proper research and survey before
launching a new product. The project manager’s role here becomes extremely important as he or
she is the individual who has the responsibility to collect relevant information (York & Danes,
2014). The manager has to take several critical decisions in order to ensure successful launch of
the product. The launching of a new product involves complex processes beginning with
marketing strategies. Development of a new product or technology involves decisions that are

8
EXPLAINING BIASES IN DECISION MAKING
extremely unpredictable and demand ample strategic intercessions and productions (Sok &
O'Cass, 2015).
Volkswagen, one of the largest car-selling companies in the world, decided to launch new
cars providing low carbon emitting vehicles in 2015 for the American market Ferrell et al.,
(2016). The decision was made by the higher management without taking due consent or advice
from the other team members. With a view to take over the US automobile market, the VW
Company overlooked serious ethical issues. In 2015, it was found that the cars launched in the
American market failed in emissions test. Reportedly, the automobile company illicitly installed
a device that evaded emissions test required to meet the EPA standards. This massive breach of
ethics cost the automotive giant heavily as it had to see the back of its former CEO Martin
Winterkorn who was forced to resign, following the scandal (Reuters.com, 2018). The company
was accused of following a culture of silence where the subordinates kept mum on the issue and
allowed the top officials to make irrational decisions that led to the company’s inevitable
downfall. Volkswagen rationalized its decision to rig emissions test by pointing out the
extremely competitive market and the strategies to survive in it. The concept of bounded
rationality helps explain this bias in decision-making by the VW officials. The company officials
tried to rationalize their wrong decision by putting the blame on external factors like market
competition and strategy for survival (Fortune.com, 2018).
Another scenario that may explain bias in decision-making in the real world is that of
Motorola, the cell phone manufacturing company. The company was high on profits with over
22% market share during the 2006-2007 periods (Albrecht, 2015). However, it failed to
recognize the ongoing developments in the cell phone market and lagged behind in launching
new brand of smart phones. By 2010, brands like Apple had already taken over the market
leaving Motorola with no choice but sell its old phones at a low price. The decision to delay the
launch resulted from the inability of the management team at Motorola to use information
correctly.
Recognizing bias
Herbert Simon proposed the concept of ‘satisficing’, which is formed by combining the
words ‘satisfy’ and ‘suffice’ to explain bias in decision-making. According to him, humans tend

9
EXPLAINING BIASES IN DECISION MAKING
to look for options that are satisfactory enough to suffice their desire, without even trying to
search through other viable options. In this way, they tend to make decisions that often yield
unexpected and unwanted results. In technical terms, this concept refers to bounded awareness or
bounded rationality. The concept is useful to explain the bias that occurred in the case of
Volkswagen. The company had all the relevant information about the US market and its rules
and regulations yet it decided to go ahead with the launch of low carbon emitting cars. The kind
of bias that occurred in this decision-making scenario is anchoring bias. Montibeller and
Winterfeldt (2015) define anchoring bias as fixation on initial information and inability to
regulate for consequent information. Anchoring bias can be attributed to the concept of bounded
rationality where individuals fail to notice valuable information. The case of Volkswagen can
also be understood from the viewpoint of the availability concept. This explains that people take
decisions based on the availability or ease of executing an idea (Taylor et al., 2017). Volkswagen
targeted the US market, observing customer’s tendency to opt for vehicles that were affordable
as well as environment friendly. They found it easier to lure the target market by claiming to
produce cars with low carbon emissions. The decision to cheat emissions test by installing
devices to evade EPA standards was a result of this availability heuristic.
In case of Motorola, the bias that affected the decision-making process is the framing
bias. Phillips et al., (2016) argues that framing bias in decisions occurs when individuals or
companies fail to analyze developments in the market and make escalating commitments.
Motorola made commitments to introduce advanced smart phones to meet the demands of the
changing market dynamics but it was too late. Here, the concept of representativeness can be
used to explain the bias. Representativeness refers to the choice made by people based on the
similarities of behaviors or objects (Bordalo, Gennaioli & Shleifer, 2017). However, they fail to
realize that behaviors that represent something majorly do not always imply likelihood. The
makers of Motorola mobile phones relied heavily on the customer behavior and thus paid the
price.
Methods by which bias may be measured or evaluated in the scenario
Biases in decision-making occur frequently and none can evade or protect themselves
from these biases. Aczel et al., (2015) present various methods to measure biases in decision-

10
EXPLAINING BIASES IN DECISION MAKING
making. Precisely, the methods include individualized scores, constructing validity, motivation
and comparability. In this case, the evaluation or measurement of bias is applied to two
scenarios, one of Volkswagen and the other of Motorola. The Volkswagen case involved
anchoring bias that prompted leaders to resort to unethical ways in order to attract profit.
Individualized scores can be given to individuals at the company who were more susceptible to
bias. Comparability refers to variations in vulnerability to bias in decision-making (Rezaei,
2015). The decision of employees at Volkswagen to stay silent on the issue despite knowing
everything can be measured by this method. Decision-making biases at Motorola can be assessed
by the method of constructing validity. People at Motorola suffered from framing bias that can
be measured by analyzing their behavior of neglecting ongoing developments by being over
dependent on instincts (Morewedge et al, 2015).
Strategies applied to overcome bias
According to a report published in the Harvard Business Review, companies need to
make five important steps to overcome bias. First is to comprehend the systematic errors that can
occur while making decisions. Second, decide whether behavioral factors are the main causes of
poor decisions. Third, identify the specific primary causes. Fourth, restructure the entire process
to alleviate the biases and fifth, assess the solutions thoroughly (Hbr.org, 2018). Applying theses
strategies can largely help organizations overcome biases while making decisions. Helfat and
Peteraf (2015) are of the view that managers must possess the cognitive capacity to make
decisions by sensing, seizing and reconfiguring the potential impact of change on organizations.
A comprehensive study and research on the methods and strategies of decision-making
can greatly help individuals overcome biases in decision-making in the future. This would in turn
assist managers and leaders to make improved decisions that involve thorough analysis of all the
factors of the surrounding. It is however important to mention that decisions will be biased no
matter how careful individuals are while making those. It is because individuals do not possess
the capability to be perfectly rational. It can be observed that the later decisions taken by the
VW Company were the reflections of the outcomes of the previous decisions (Aurand et al.,
2017). The culture of silence that was prevalent in the company and that nobody confessed about
was brought to the fore by the new CEO, Matthias Mueller.

11
EXPLAINING BIASES IN DECISION MAKING
Conclusion
In view of the discussion above, it is imperative to state that Herbert Simon’s take on
biases that affect decision-making process is concrete and can be applied to any situation. His
rejection of perfect rationality was not accepted initially by many theorists, as they believed it to
be unscientific. However, Simon’s bounded rationality principle did create ripples in the
academic world concerning economics. The above report argues in favor of Simon’s concepts
and principles while evaluating his statement on bounded rationality. In the report, focus has also
been put on the three specific concepts namely availability, bounded awareness rationality and
representativeness that explain bias in decision-making. Two real world scenarios – Volkswagen
diesel scandal and Motorola’s product launch failure – have been discussed to explain biases.
Methods to measure bias have also been mentioned in the report. In addition, the report
highlights the strategies that may help individuals overcome bias in future.

12
EXPLAINING BIASES IN DECISION MAKING
References
Aczel, B., Bago, B., Szollosi, A., Foldes, A., & Lukacs, B. (2015). Measuring individual
differences in decision biases: Methodological considerations. Frontiers in psychology, 6,
1770.
Albrecht, D. J. (2015). Product Standardization and Product Design Modularity: A Case Study of
Motorola and the Mobile Handset Market. Browser Download This Paper.
Aurand, T. W., Finley, W., Krishnan, V., Sullivan, U. Y., Bowen, J., Rackauskas, M., ... &
Willkomm, J. (2017). The VW Diesel Scandal: Engaging Students via Case Research,
Analysis, Writing, and Presentation of Findings. Journal of Higher Education Theory and
Practice, 17(7), 10-21.
Beach, L. R., & Lipshitz, R. (2017). Why classical decision theory is an inappropriate standard
for evaluating and aiding most human decision making. Decision Making in Aviation, 85.
Bordalo, P., Gennaioli, N., & Shleifer, A. (2017). Diagnostic expectations and credit cycles. The
Journal of Finance.
Elbanna, S., Kapoutsis, I., & Mellahi, K. (2017). Creativity and propitiousness in strategic
decision making: The role of positive politics and macro-economic
uncertainty. Management Decision, 55(10), 2218-2236.
Ferrell, A., Ondracek, J., Saeed, M., & Bertsch, A. (2016). Failed Decision-making at
Volkswagen. In Annual Eurasian Business Research Conference. Manila, Philippines.
Foss, N. J., & Weber, L. (2016). Moving opportunism to the back seat: Bounded rationality,
costly conflict, and hierarchical forms. Academy of Management Review, 41(1), 61-79.

13
EXPLAINING BIASES IN DECISION MAKING
Harrison, R. T., Mason, C., & Smith, D. (2015). Heuristics, learning and the business angel
investment decision-making process. Entrepreneurship & Regional Development, 27(9-
10), 527-554.
Hbr.org. (2018). Leaders as Decision Architects. Harvard Business Review. Retrieved 5
February 2018, from https://hbr.org/2015/05/leaders-as-decision-architects
Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the
microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831-
850.
Maine, E., Soh, P. H., & Dos Santos, N. (2015). The role of entrepreneurial decision-making in
opportunity creation and recognition. Technovation, 39, 53-72.
Maitland, E., & Sammartino, A. (2015). Decision making and uncertainty: The role of heuristics
and experience in assessing a politically hazardous environment. Strategic Management
Journal, 36(10), 1554-1578.
Matters, W. H. S. (2018). Algorithmic Social Sciences Research Unit.
Montibeller, G., & Winterfeldt, D. (2015). Cognitive and motivational biases in decision and risk
analysis. Risk Analysis, 35(7), 1230-1251.
Montresor, A., Addiss, D., Albonico, M., Ali, S. M., Ault, S. K., Gabrielli, A. F., ... & Levecke,
B. (2015). Methodological bias can lead the Cochrane Collaboration to irrelevance in
public health decision-making. PLoS neglected tropical diseases, 9(10), e0004165.

14
EXPLAINING BIASES IN DECISION MAKING
Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H., & Kassam, K. S.
(2015). Debiasing decisions: Improved decision making with a single training
intervention. Policy Insights from the Behavioral and Brain Sciences, 2(1), 129-140.
Phillips, W. J., Fletcher, J. M., Marks, A. D., & Hine, D. W. (2016). Thinking styles and decision
making: A meta-analysis. Psychological bulletin, 142(3), 260.
Reuters.com. (2018). Timeline: Volkswagen's long road to a U.S. Dieselgate settlement. U.S..
Retrieved 4 February 2018, from https://www.reuters.com/article/us-volkswagen-
emissions-timeline/timeline-volkswagens-long-road-to-a-u-s-dieselgate-settlement-
idUSKBN14V100
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Shepherd, D. A., Williams, T. A., & Patzelt, H. (2015). Thinking about entrepreneurial decision
making: Review and research agenda. Journal of management, 41(1), 11-46.
Simon, H. A. (1957). Models of man; social and rational.
Sok, P., & O'Cass, A. (2015). Examining the new product innovation–performance relationship:
Optimizing the role of individual-level creativity and attention-to-detail. Industrial
Marketing Management, 47, 156-165.
Taylor, J. P., Ashworth, S. L. J., Petrovich, S., & Young, C. A. (2017). Inducing an availability
heuristic on the Wason selection task overrides the matching bias. Journal of Cognitive
Psychology, 29(4), 508-519.

15
EXPLAINING BIASES IN DECISION MAKING
Toplak, M. E., West, R. F., & Stanovich, K. E. (2017). Real‐World Correlates of Performance on
Heuristics and Biases Tasks in a Community Sample. Journal of Behavioral Decision
Making, 30(2), 541-554.
York, J. L., & Danes, J. E. (2014). Customer development, innovation, and decision-making
biases in the lean startup. Journal of Small Business Strategy, 24(2), 21.