Essay: Cognitive Biases In Decision-Making

The Impact of Cognitive Biases on Decision-Making in Organizational Management

Question:

Write an essay on "Management and Organization in a Global Environment".
 

Decision-making is the integral part of the business management, which requires the excessive attention and carefulness. The Decision Support System (DSS) is considered as one of the significant information disciplines that supports and improves the human decision-making (Evans and Stanovich 2013). The management decision-making process has the effective influence in the business practices. Croskerry (2013) pointed out that the sound decision-making process is essentially needed for ensuring the primary functions of the management. It is noted that the business managers usually make some of the important decisions consciously or subconsciously. According to Croskerry, Singhal and Mamede (2013), it is a process of determining the managerial activities and signifying the crucial roles made within an organization. The indispensable and continuous process of decision-making is the driving force for the organizational management. However, it has been seen that managers have to face several cognitive biases while undertaking the proper decision for organizational welfare.

As explained by Axelrod (2015), cognitive biases are also known as the psychological biases, which are reflecting the tendency to make decision in an illogical way. Some of the situational incidents make the managers undertake the subconscious decisions, which may lead them towards the illogical way. The behavioral decision theory has been concentrating on the cognitive psychology that has the clear linkage with the decision support system. As stated by Meissner and Wulf (2013), Decision biases refer to the mental behavior or cognitions that represents the unfairness in making decisions for people within an organization. It is also termed as ‘judgment or cognitive biases’. In some of the cases, the decision biases are differentiated from the feature of rational decision-making. The tendency of biases is generally affecting the relevant decision-making process.

Croskerry, Singhal and Mamede (2013) opined that the psychological bias is opposing the term of ‘common sense’ and ‘clear’. The bias leads towards missing the fruitful opportunities. This biasness can specify different aspects, which sometimes cause harm to the organizational practices. In describing the origin of the cognitive biases, it is to be indicated that biases are formed equally and it is difficult to overcome this biasness. It is true that the cognitive biases usually affect the business practices (Betsch and Haberstroh 2014). Humans make the fruitful decision and the emergence of the cognitive biases is interrupting the proper way to make these fruitful decisions. Usually it has been seen that the cognitive biases can result the distortion or the perceptual blindness (Power, Sharda and Burstein 2015). The influence of the cognitive biases on humans creates the significant challenges for the company and the work environment. When a group is formed, the leader has to be much skilled and free from the biasness, or else the team members will be biased as well. It is to be noted that the human biasness can destroy the market opportunity by making the blind decisions (Goschke 2014). During the strategic decision-making process, the extreme stress level can lead towards making the biased decision. The Anchoring biasness defines the tendency to jump to conclusions. The final judgment is always based on the information gathered during the decision-making process.

Types of Cognitive Biases Faced by Managers

The study will be discussing about the different cognitive biases faced by the managers during the organizational decision making process. The study will also discuss the way of misleading the managers towards the ineffective decision making aspects. The topic will be exploring some biases mentioned in the taxonomy formed by Arnott. The contextual elaboration will be provided in keeping focus on the rationale of the study. 

It is to be indicated that one of the major aspects of the behavioral decision theory has the connection with DSS researchers. More specifically, it is seen that some of the system analysis is also involved in order to develop the DSS, which is assimilated with the decision quality that are made for other people (Koch, D’Mello and Sackett 2015). The decision biases are divided into 37 different components and David Arnott classified this. The study will be discussing about two major components related to the decision biases and the preferable examples will be discussed accordingly.

One of the major and significant components of decision biases is Anchoring and Adjustment. In defining the term, it is to be stated that “Adjustments from an Initial position are useful insufficient” (Tamir and Mitchell 2013).  It is to be indicated that the initial human judgment begins with the adjustable opinion. The environment, which is associated with extraction of continuous feedback, is justified strategy in this regard. This strategy is mainly identified by the experiment between two groups of the subjects that are estimating the quantity (Cen, Hilary and Wei 2013). Each of the groups is thereby given the initial position in such manner. One example regarding this can be mention in this case. If one group is informed the length of a known river as 500 miles while other group is informed as 5000 miles, the first group is estimated for 1000 miles and the other group is for 3000 miles. However, both of these estimations are wrong, because the actual answer would be 2,300 miles. The experiment has been concerning the dominance over the quality judgment after suggesting a reference point. It is noted that the adjustment associated with the reference point is generally insufficient as per the estimation. In concentrating on this example, it can be inferred that the this anchoring or adjustment is such a cognitive bias that is reflecting the human tendency to be reliable on the first information. Heath et al. (2013) commented that the humans make the decision based on the initial information and tends to derive the subsequent judgments. Another example related to such cognitive bias is the first impression syndrome, which is signifying the inability if the humans to make decisions once one instant assessment is made. Therefore, it can be stated that when one anchor is set, the other judgments have to be adjusted accordingly to interpret the other information.

Many of the psychologists have presented the documentation about such cognitive biases. Shaffer et al. (2014) implied that during the formulation of the quantitative estimation, it is much influenced by the previous values. Hence, it is sometimes required to some of the heuristic causes, which makes the investors most vulnerable. In supporting such statement, Lee et al. (2013) notified that this psychological heuristic has been influencing the people for assessing the probabilities. Usually a person starts with fixing the first approximation and makes the incremental judgments by keeping the focus on the probable adjustments. Hence, the adjustments are insufficient in such cases and the anchor can assess the significant influence for the future prospects. However, people face the promising phase in terms of avoiding such anchoring. It is very difficult to ignore the first information received for the initial assessments. However, on the other hand, Shapira and Shaver (2014) argued that elimination of the anchoring is much easier and direct. These contradictory views have been influencing the study in a significant way.

Implications of Cognitive Biases on the Decision-Making Process

In the previous research, Baumann et al. (2014) considered Heuristic as the mental shortcuts, which usually used for simplifying the difficult tasks or problems. The quick made estimation can lead to the inaccuracy, which may determine the uncertain events. It is clear that during the initial stage of fixing the anchor, participants have to deal with the two-step process. In the first stage, they usually make the comparative assessment and ends up with the followed estimate in the next stage. Therefore, it can be indicated that the heuristics generally require the little information, but it has be accurate. More specifically, the heuristic is decreasing the cognitive burdens during making any relevant decision. It is important to note that the heuristic process has been analyzing the subtle information that is useful for the decision-making process. Moreover, the heuristic has been diminishing the work of retrieving, streamlining, and restoring the information by the reduction of integrated information.
 

Figure 1: Anchor and Adjustment Bias

In the year of 1974, Tversky and Kahneman first introduced this concept of anchoring and adjustment (Cheek and Norem 2016). In stating this concept, another most relevant example can be added. When individual needs to purchase a car, it is required to receive the initial information first. People will be paying the cost only if the information is accurate and it has some of the significance. In such cases, the initial information is taken into consideration and the further information is accordingly adjusted. The conservative form of the adjustments tends to be biased in considering the determined anchor (Barberis 2013). In presenting more illustration regarding this particular aspect, Tversky and Kahneman focused on the number generated with the help of spinning wheel. The description of the anchoring and adjustment bias is somewhat associated with conspiracy theories. As per this theory specification, it can be stated that the mechanisms associated with anchoring and adjustment has been projecting the motivations onto others (Prooijen and Jostmann 2013). In such cases, both of the people have the feeling of sharing the similar preferences and motivations.

In the year of 2011, Douglas and Sutton explained the further concept of projection by illustrating a fascinated discovery. More specifically, it has been seen that the people who are much fascinated by the conspiracy theories are usually willing to be involved in such conspiracies. On the other hand, Douglas and Sutton presented another elaboration. They explained that individuals are not fully aware of the preferences and motivations of other people (Sutton and Douglas 2014). However, usually these individuals predict such preferences by focusing on the goals, needs, and values in a proper setting. The projection of the personal needs and values onto others is also taken into consideration in such aspect (Wood, Douglas and Sutton 2012). Hence, if one needs to be conspired, they would wish others to be conspired as well. The conspiracy theory is thus endorsed by following such ways.  

Another component of cognitive biases is the Complexity Biases. In defining the concept, it was stated, “Time pressure, information overload and other environmental factors can increase the perceived complexity of a task”. It is to be indicated that many of the environmental forces have been influencing the decision quality in a negative way. The factors involved with such biases are affecting the decision biases more prominently. Johnson et al. (2013) notified that when the stress level aligned with the decision making process, the complexity bias. It is to be indicated that the major source of task stress is to face the extreme level of time pressure on decision-making aspect (Hilbert 2012). On the other hand, it is noted that the other factors associated with this segment is making a task stress more complex than the warranted volume of presented data. In specifying the market channels, it is usually seen that the decisions are made on daily basis and depends on the invested capitals. Kasprzyk et al. (2013) pointed out that decision is considered as the quicker approaches unlike the short-term scalp that lasts for some seconds and the swings for 2-3 days until it reaches to the extreme level of the potentiality. The relationship between the task stress and decision quality is known as Yerkes-Dodson Law.

The Connection Between DSS and Behavioral Decision Theory

 
Figure 2: The Yerkes-Dodson Law

The above figure is illustrating the linkage between the task stress and decision quality. In the year of 1908, Yerkes-Dodson described the law to define the perspectives (Corbett 2015). As per the specification of Yerkes-Dodson Law, it is noted that the task stress is helpful for making a decision related to any significant aspect. However, it is also to be indicated that extreme level of stress can equally drop the level of decision quality (Newell and Shanks 2014). In time of making any decision related to the trade, it is seen that people undergo an extreme level of pressure to conclude. In addition, if the information is overloaded and the environmental influences are present, the risks increase in a significant manner. Therefore, it is seen that the involvement of the complexity makes the decision negatively impacted. The major result behind such negative approaches in the decision-making process is “stress” (Orquin and Loose 2013). Hence, it can be inferred that the ‘stress’ is one of the major factors of Complexity Bias that hinders the success in the market in a very specific way. Coon and Mitterer (2007) commented on an observation of Yerkes-Dodson Law. They stated, “Some examples of the Yerkes-Dodson law might be helpful. At a track meet, it is almost impossible for sprinters to get too aroused for a race. The task is direct and uncomplicated: Run as fast as you can for a short distance. On the other hand, a basketball player making a game-deciding free throw faces a more sensitive and complex task. Excessive arousal is almost certain to hurt his or her performance” (Coon and Mitterer 2007).  It is notified that the proactive trader is more fruitful than the reactive trader is since; the leader would undertake the less complex decision. The larger feature of the Yerkes-Dodson law should be accommodated with more strategies.

In many of the cases, it is seen that the mechanical system traders are free from the stress since the actual decision is quantified and stress cannot manifest itself (Chaby et al. 2015). However, the discretionary traders are suspected to be more stressful and their decisions are spotted more specifically. In focusing on the law introduced by Robert Yerkes and John Dillingham Dodson, it was discovered that mild level of the electric shocks might motivate to complete a decision. Simultaneously, the increasing rate of the electric shocks might even create a situation of escaping. The wider experiments based on such aspect is implying that the extreme stress can be sometimes fruitful in making the decision-maker more attentive and motivated towards the responsibility (Sieber, O'Neil Jr and Tobias 2013). However, the limited point of stress level is considered in such cases. The anxiety faced by the students before exams are one of the most featured examples for defining the Yerkes-Dodson Law operations. The students can take the stress up to certain level, which is helpful for them to remember the syllabus they had studied for the exams. On the other hand, when the stress level increases, it would be difficult for them to recall the correct answers in the exam (Mellifont, Smith-Merry and Scanlan 2016). Similarly, another example can also be added in this regard. If one of the leaders of a team is asked to define a presentation in front of the higher authority, it sometimes makes them nervous. However, if they can take the limited stress, it would make them more attentive towards the responsibility. Simultaneously, when the stress limit crosses, they might face difficulties in providing some presentation in front of the higher authority.

David Arnott's Classification of Decision Biases

It is to be stated that the complexity bias is sometimes influenced by the complex environmental factors as well. (Quick et al. 2013) put forward the idea that the conditions of the natural environment usually auto-correlated or heterogeneous. In most of the cases, the decision rules are exploiting the statistical structure. In case of simplified environments, these decision rules are usually leading towards the apparent form of irrational behavior. In fact, the influence is often defining the contrast effects, intransitivity, pessimisms, and other biases (Mellifont, Smith-Merry and Scanlan 2016). The experiment is thus providing the complete picture of the decision bias and the effects of the increased stress level in the organizational context. The recognition of such different perspective values related to the complexity biases in decision-making is affecting the careers of the leaders in a very significant way. The theoretical perspectives will be analysed in this study to present the strengthened argument related to the subject matter. The frameworks will also be incorporated to clarify the theoretical analysis. The different examples based on the clear bonding between the decision quality and stresses have been determining the importance of changes. When such situational crisis arises, the employee need to consider the maintenance of these aspects to overcome the obstacles.

In considering the biases in the cognitive decision making process, the procedure of the de-biasing can also be referred accordingly. According to Arnott (1998), the de-biasing is the method of eliminating or reducing the biases related to the cognitive decision making strategies. It is notified that two types of the e-biasing approaches are needed to be achieved to eliminate the biases. Firstly, the development of the general framework for ensuring the cognitive change is needed to be applied to the biases (Chaby et al. 2015). Secondly, the development of the cognitive change strategies is needed to have the linkage with particular bias. These two approaches are completely justified with the development of DSS. In the year of 1990, Keren introduced a framework that signifies the de-biasing process in the medical perspectives. In the first place, he proposed to identify the nature and the existence of potential biases. Understanding the environment and the cognitive triggers of the bias are included in this initial step (Mellifont, Smith-Merry and Scanlan2016). The consideration of the alternative means for eliminating or minimizing the biases is also approached. It is even required to monitor the effectiveness of selected techniques of de-biases. However, some of the negative influences are also assimilated in this particular concern. In describing the second stage, Keren differentiated this de-biasing technique with the procedural techniques. He implied that in some of the cases, the user is unaware of the problem structure and this is the reason behind the operation biases (Corbett 2015). In such scenario, it is required to derive the deeper understanding to restructure the problem statement by using the modified techniques. In fact, the user can even manipulate the internal structure of the task to understand the associated problem (Huber, Hill and Lenz 2012). It is to be noted that most of the de-biasing research is much influenced by the procedural nature, even though the modified structure is leading towards the efficient outcomes.

In the year of 1982, Fischoff proposed an influential works on de-biasing. He presented a classification of a de-biasing method that is mainly focusing on the source of bias. It is noted that these identified sources are often associated with faulty tasks, faulty decision-makers, and mismatches between the tasks and the decision-makers (Orquin and Loose 2013). In the first section, the problems associated with the faulty task have been concerning that; the restructuring of the environmental task may create the significant impact on the decision biases. In supporting such view, another recommendation is also reflecting that the task environment is selected as an alternative method to de-bias the individual decision-maker (Power, Sharda and Burstein 2015). The role of the information system is much significant in this case since the restructuring tasks and processes are associated with the core activities of system designs and analysis (Chaby et al. 2015). More specifically, it can be indicated that the classifications of Fischhoff are mainly attracting the attention of the “perfecting individuals”. It thus can be assumed that the instead of the tasks, the decision maker is considered as the primary source of the biased judgment (Mellifont, Smith-Merry and Scanlan 2016). While in one hand, people lack of competences, the other people can even fail in spite of being competent on the other hand. The occurrence of the application error in the de-biasing strategy is needed to be aligned with the educational purposes of the decision-maker regarding to the decision task, decision rules, and relevant biases.

On the other hand, the comprehension errors are more difficult to overcome in compare to the effects of the application errors. The strategy proposed by Fischhoff for overcoming such errors has been considered as the escalated design, which is evaluating the increasing level of the supports that is provided to the individuals (Commerford et al. 2014). These proposed escalations are included few basic steps, which are essentially needed to be concentrated. As opined by Miller et al. (2013), the first step is providing warning to the decision maker regarding the possibilities of bias, which is free from the nature descriptions. In the next step, the nature of the biases is described and this description is including the positive and the negative influences along with the strengths of the bias. Furthermore, another step is implicating that the necessity of providing feedbacks, which is personalizing the descriptions and warning of the biases (Chaby et al. 2015). In keeping concentration on such aspect, the reaction of the decision maker is needed to be concentrated to target the task. The next step is considering the extended training program facilitated by feedbacks, coaching, and discussions of the interventions. These factors are effective enough in overcoming the negtauve effects of biases.

The third recognized category implied by Fischhoff is indicating the mismatch between task and decision maker (Sieber, O'Neil Jr and Tobias 2013). The application error mentioned by Tversky and Kahneman is determining the requisite cognitive skills, which are ineffectively applied in most of the cases. In focusing on these strategies, Evans also introduced several other strategies related to the de-biasing process in the year of 1989 (Quick et al. 2013). The proposed method of Evans was included with four major categories, such as education, redesign of the task environment, replacement, development of the decision support system, and training. However, the general de-biasing strategy is somewhat associated with the Lewin-Schein model of social change. The description of this model is provided further:

The model of social change proposed by Lewin-Schein is focusing on the modified behavior of the people and this is divided into three specific stages. These three major steps are Refreeze, Unfreeze, and Change. 

Figure 3: Lewin-Schein’s Model of Social Change

The first stage is Unfreeze stage in which people get the feeling of changes and usually deal with several emotions. The emotional state is often associated with impatience, denial, doubts, and uncertainties. It is to be indicated that the business needs to disclose the state of affairs and accordingly needs to explain the forcefulness of such change process (Kasprzyk et al. 2013). In determining the result, it is to be noted that the employees who are much interested about the clear communication are much willing to accept the changes by eliminating the previous process (Commerford et al. 2014). During the implementation of this stage, the constructive process approaches are needed to be associated with the change process.

The second stage is describing the Change, which is needed to be implemented within the short span of time. The time consumed in the change process is influencing the employees to get stick to the old habits and rituals. As stated by Arnott (2002), this particular stage is defined as the “move stage”, which causes movable affects within an organizational scenario. The vigorous actions against the implemented changes within a short span of time are making the employees more knowledgeable about the importance of changes (Orquin and Loose 2013).  The final is Refreeze, which is determining the solidification of the changes. The completion of the implemented change is inclined the employees to revert them back to their old customs. In such cases, the employees are advised to make the required arrangements to carry out the evaluations, adjustments, and monitoring process. The new situation can be controlled if the employees can perform such sequential stages (Sutton and Douglas 2014) Simultaneously, the new situation can also be stabilized, which is necessary to make the employees realize that there is no turning back. It has been seen that eventually, the employees get accustomed with such new scenario and they are even provided with many of the advantages. The development of the Decision Support System requires the formulation of the framework that describes the development procedure of DSS.   

Figure 4: Model of DSS Development

The DSS development model is divided into two different levels, such as major cycle level and development activity level. The above figure is attempting the realistic approaches associated with the realistic indication and it is quite schematics. In the first generation of the Decision Support System is reflecting the activities, which are overlapping the nature and time. This development procedure is associated with the system construction, designing, and use, which are featuring the rapid changes at the same time (Commerford et al. 2014). As per the specified process in the above figure, it is seen that the major cycles are aligned with several activities (Baumann et al. 2014). It is necessary to link the initiations with the analysis cycles by considering the planning and resourcing process. Moreover, this design links are ensuring the delivery cycles.

It is to be indicated that the Decision Support System is usually feature the unconstructed decision, which is affecting the system requirements. The system analysis is helpful enough in adopting the evolutionary development strategy to ensure matches with the environment. In case of the initial version of the system, the clarifications of the functionality as well as the requirement are essential (Miller et al. 2013). The initiation cycles are considered as the unfreezing process, which is determining the idea about the importance of change. The realization is necessary for understanding the necessity of changes that are needed to be evaluated.  On the other hand, the analysis cycles are associated with the diagnosis activity that develops the understanding of the decisions with the help of sufficient details (Power, Sharda and Burstein 2015). The final stage is delivery cycles, which are involved with the parallel application of the system construction, designs, and uses. Shaffer et al. 2016) implied that the decision support system is an integral part that is associated with the small-scale of information system. However, the implementation of such process cycles has been influencing the elimination of the decision biases.

Conclusion

The study is concluding with the featured recognition of the decision biases. It is to be indicated that the decision-making process is considered as one of the major responsibilities. The efficient decision-making process influences the organizational functionality and performance attributes. However, the leaders or the business dealers often face several ineffective biases while undertaking any relevant decision. The two of the major biases from the taxonomy of Arnott were selected in this study. The first biases have been focusing on anchoring and adjustments, which determines the initial focus on the first information. This information is received initially and the latter information is needed to be adjusted for making any decision. In such cases, the adjusting behavior is much prominent with the former decision making process and it tends towards the biases. The second component is featuring the complexity biases, which is focusing on the complex world of the business decisions. It has been seen that the environmental influences are substantiated with the decision making process, which is leading towards biases. The Yerkes-Dodson law has been focusing on the increasing rate of the stress level. Limited stress is good for being attentive towards the responsibility while extreme level of stress can lead towards acquiring the bias decisions. However, the featured processes of de-biasing are also reflecting the reduction of the stress level. The elimination of the biases is fruitful for improvising the organizational functionality. Accordingly, the development of the Decision Support System would be essential for determining the decreasing level of biases. Hence, the recognition of these beneficial skills will be determining the improvisation of the decision-making process within an organization. More specifically, it can be inferred that the identification of major biases can be fruitful enough in understanding the procedure of reducing such decision biases and improving the organizational functionality. 

The featured forms of the decision-making process are required for the development of the organizational functionality. The study has been focusing on the decision biases that are associated within an organizational decision-making process. The collaboration of the psychological attributes is much prominent feature in such cases. The study attempts to provide the generalize idea of the Decision Support System, which is helpful enough in categorizing the diversified steps of decision-making process. The decision biases are somewhat associated with the mental behavior of the leader who is in charge of such decision-making aspects. Usually, the decision biases are leading towards acquiring the wrong decision. However, the study focuses on the diversified taxonomies related to the decision biases presented by David Arnott. The study has been focusing on two of these selected taxonomies. The study has been discussing about two types of the decision biases. The first one is Anchoring and Adjusting, which is much depended on the initial information received for making any organizational decision. It has been seen that in the initial information, which is collected for coming to the conclusion. Once the anchor is set, the other information has to be adjusted accordingly in keeping focus on the previous decisions. Another component is used here and it is the Complexity Biases associated with the decision-making process. The Yerkes-Dodson Law is supporting the complexity biases. The law has been formulated to represent the linkage between stress task and decision quality.

The next segment is associated with the implementation of de-biasing process. De-biasing process generally refers to the reduction or the elimination process of decision biases. The application of Lewin-Schein’s Model of Social Change is indicating the skilful process of debiases. The development of the Decision Support System is also presented here for determining the mitigation of the decision making process. The applied Model of DSS Development is featuring different cycles, which are associated with the reduction of such problematic situation. However, the recognition of these segments would be fruitful enough for the organization to improvise the organizational performance.  

References

Arnott, D. (2002). Decision biases and decision support systems development Melbourne, Australia: Decision Support Systems Laboratory, Monash University.

Arnott, D., 1998. A Taxonomy of Decision Biases. School of Information Management & Systems: Monash University, pp.4-10.

Axelrod, R. ed., 2015. Structure of decision: The cognitive maps of political elites. Princeton university press.

Barberis, N., 2013. The psychology of tail events: Progress and challenges.The American Economic Review, 103(3), pp.611-616.

Baumann, D.J., Fluke, J.D., Dalgleish, L. and Kern, H., 2014. The decision-making ecology. In From evidence to outcomes in child welfare: An international reader (pp. 24-40). Oxford University Press New York, NY.

Betsch, T. and Haberstroh, S. eds., 2014. The routines of decision making. Psychology Press.

Cen, L., Hilary, G. and Wei, K.J., 2013. The role of anchoring bias in the equity market: Evidence from analysts’ earnings forecasts and stock returns. Journal of Financial and Quantitative Analysis, 48(1), pp.47-76.

Chaby, L.E., Sheriff, M.J., Hirrlinger, A.M. and Braithwaite, V.A., 2015. Can we understand how developmental stress enhances performance under future threat with the Yerkes-Dodson law?. Communicative & integrative biology, 8(3), p.e1029689.

Cheek, N.N. and Norem, J.K., 2016. Holistic thinkers anchor less: Exploring the roles of self-construal and thinking styles in anchoring susceptibility.Personality and Individual Differences.

Commerford, B.P., Hatfield, R.C., Houston, R. and Mullis, C., 2014. Debiasing Auditor Judgments from the Influence of Information Foraging Behavior.

Coon, D. and Mitterer, J. O. 2007. Introduction to psychology. Belmont, CA: Thomson Wadsworth.

Corbett, M., 2015. From law to folklore: work stress and the Yerkes-Dodson Law. Journal of Managerial Psychology, 30(6), pp.741-752.

Croskerry, P., 2013. From mindless to mindful practice—cognitive bias and clinical decision making. N Engl J Med, 368(26), pp.2445-2448.

Croskerry, P., Singhal, G. and Mamede, S., 2013. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ quality & safety, 22(Suppl 2), pp.58-64.

Croskerry, P., Singhal, G. and Mamede, S., 2013. Cognitive debiasing 2: impediments to and strategies for change. BMJ quality & safety, pp.bmjqs-2012.

Evans, J.S.B. and Stanovich, K.E., 2013. Dual-process theories of higher cognition advancing the debate. Perspectives on psychological science,8(3), pp.223-241.

Goschke, T., 2014. Dysfunctions of decision‐making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research. International journal of methods in psychiatric research, 23(S1), pp.41-57.

Heath, L., Tindale, R.S., Edwards, J., Posavac, E.J., Bryant, F.B., Henderson-King, E., Suarez-Balcazar, Y. and Myers, J. eds., 2013.Applications of heuristics and biases to social issues (Vol. 3). Springer Science & Business Media.

Hilbert, M., 2012. Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making. Psychological bulletin, 138(2), p.211.

Huber, G.A., Hill, S.J. and Lenz, G.S., 2012. Sources of bias in retrospective decision making: Experimental evidence on voters’ limitations in controlling incumbents. American Political Science Review, 106(04), pp.720-741.

Johnson, D.D., Blumstein, D.T., Fowler, J.H. and Haselton, M.G., 2013. The evolution of error: Error management, cognitive constraints, and adaptive decision-making biases. Trends in ecology & evolution, 28(8), pp.474-481.

Kasprzyk, J.R., Nataraj, S., Reed, P.M. and Lempert, R.J., 2013. Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, pp.55-71.

Koch, A.J., D’Mello, S.D. and Sackett, P.R., 2015. A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making. Journal of Applied Psychology, 100(1), p.128.

Lee, K., Miller, S., Velasquez, N. and Wann, C., 2013. The effect of investor bias and gender on portfolio performance and risk. The International Journal of Business and Finance Research, 7(1), pp.1-16.

Meissner, P. and Wulf, T., 2013. Cognitive benefits of scenario planning: its impact on biases and decision quality. Technological Forecasting and Social Change, 80(4), pp.801-814.

Mellifont, D., Smith-Merry, J. and Scanlan, J.N., 2016. Pitching a Yerkes–Dodson curve ball?: A study exploring enhanced workplace performance for individuals with anxiety disorders. Journal of Workplace Behavioral Health,31(2), pp.71-86.

Miller, A.K., Markman, K.D., Wagner, M.M. and Hunt, A.N., 2013. Mental simulation and sexual prejudice reduction: The debiasing role of counterfactual thinking. Journal of applied social psychology, 43(1), pp.190-194.

Newell, B.R. and Shanks, D.R., 2014. Unconscious influences on decision making: A critical review. Behavioral and Brain Sciences, 37(01), pp.1-19.

Orquin, J.L. and Loose, S.M., 2013. Attention and choice: A review on eye movements in decision making. Acta psychologica, 144(1), pp.190-206.

Power, D.J., Sharda, R. and Burstein, F., 2015. Decision support systems. John Wiley & Sons, Ltd.

Power, D.J., Sharda, R. and Burstein, F., 2015. Decision support systems. John Wiley & Sons, Ltd.

Prooijen, J.W. and Jostmann, N.B., 2013. Belief in conspiracy theories: The influence of uncertainty and perceived morality. European Journal of Social Psychology, 43(1), pp.109-115.

Quick, J.C., Wright, T.A., Adkins, J.A., Nelson, D.L. and Quick, J.D., 2013. Preventive stress management: Challenge and opportunity.

Shaffer, D.M., McManama, E., Swank, C., Williams, M. and Durgin, F.H., 2014. Anchoring in action: Manual estimates of slant are powerfully biased toward initial hand orientation and are correlated with verbal report. Journal of Experimental Psychology: Human Perception and Performance, 40(3), p.1203.

Shaffer, V.A., Focella, E.S., Scherer, L.D. and Zikmund-Fisher, B.J., 2016. Debiasing affective forecasting errors with targeted, but not representative, experience narratives. Patient Education and Counseling.

Shapira, Z. and Shaver, J.M., 2014. Confounding changes in averages with marginal effects: How anchoring can destroy economic value in strategic investment assessments. Strategic Management Journal, 35(10), pp.1414-1426.

Sieber, J.E., O'Neil Jr, H.F. and Tobias, S., 2013. Anxiety, learning, and instruction. Routledge.

Sutton, R.M. and Douglas, K.M., 2014. 14 Examining the monological nature of conspiracy theories. Power, Politics, and Paranoia: Why People are Suspicious of Their Leaders, p.254.

Tamir, D.I. and Mitchell, J.P., 2013. Anchoring and adjustment during social inferences. Journal of Experimental Psychology: General, 142(1), p.151.

Wood, M.J., Douglas, K.M. and Sutton, R.M., 2012. Dead and alive beliefs in contradictory conspiracy theories. Social Psychological and Personality Science, 3(6), pp.767-773.