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Hausarbeit (Hauptseminar), 2022
10 Seiten, Note: 1.3
Cognitive and Motivational Biases
Less relevant Biases (easy to correct)
More relevant Bisases (Difficult to correct)
To successfully realise large-scale projects, the responsible decision-makers use project management instruments. The goal is to realise the project and its previously defined functionalities within a given time and budget (Heinrich & Winkelhofer, 2004). Although in project management, all the individual specifications of a project must be taken into account, generally applicable strategies have been established in recent years that can reduce the complexity, uncertainty and risks of a project. The process model, according to Hobel and Schutte, describes the project process in three phases: Project definition, project implementation, and project completion (Hobel & Schutte, 2014). The first phase is about project definition. Based on an analysis of the initial conditions, i.e. of the project object and environment, a clear, complete, measurable, realisable and scheduled project goal is defined. In a risk analysis, the likelyhood of occurrence and expected of damage of certain risks are identified, and preventive and countermeasures for the event of occurrence are defined. In the second phase, individual contracts for the project realisation are awarded and carried out. The project's progress is constantly monitored by project controlling and risk management using an internal control system. The project conclusion marks the last phase of a project. After completing all work, the project manager presents the work result to the client. The client reviews it with regard to the achievement of objectives and formally accepts it. The experience and knowledge gained are saved and flow back into new project processes in the form of suggestions for improvement and lessons learned. The project's outcome depends on the management decisions of the project participants in the individual project phases. Decisionmaking in large-scale projects is shaped by the complexity of the project itself, dynamic relationships between the individual actors, the market, the environment and, above all, by the systematic nature of the decision-making processes in the face of limited rationality. Thus, a project's success depends mainly on the quality of the decisions. The influences on human judgments and decisions and how to reduce or avoid them are described in the following chapters.
Two directions can be distinguished in decision theory:
(1) Descriptive decision theory describes the individual phases of the decision-making process.
(2) Prescriptive decision theory, on the other hand, attempts to use decision models to provide decision rules, i.e. orientation aids for better decision-making.
A decision is generally considered to be a "purposeful selection from several alternative courses of action". (Bitz, 1994). Within a decision model, the decision process is the path of a decision maker towards the realisation of a desired goal. To select the optimal course of action from at least two alternatives for achieving this goal, the decision-maker must solve the mathematical- statistical decision problem between these alternatives. This requires that the decision-maker knows his goal, sets up the right decision problem, forms his expectations based on objective data relevant to the future, has all relevant information, and processes it correctly. Until the 1950s, the literature assumed a rational decision-maker. This homo economicus is supposed to optimize decision problems perfectly thanks to complete information, i.e. with certain expectations and completely known consequences, using the rules of probability calculation without error, and therefore always be able to select the right option to achieve his goal. (Wohe, et al., 2016) Today we know that the assumption of an entirely rational decision-maker is unrealistic. In decision-making, the decision-maker does use rationality. However, inappropriate resources and information lead often to faulty decisions. Within this limited rationality, decisions are made at risk. Moreover, the human subconsciousness uses two different cognitive systems which complement each other (Tversky & Kahneman, 1974). System 2 performs exact calculations slowly and in a controlled manner to define precisely the best way to achieve the goal. However, due to the increased workload, the brain uses this pathway predominantly for complex problems. In contrast to system 2, system 1 uses heuristics. These are simple and mostly intuitive defaults which are based on experiences and produce more simple solutions to solve the problem. Because system 1 requires less energy than system 2, it will be more used to when making simple or repetitive decisions. Usually, both heuristics and exact optimization procedures lead to goal-directed decisions. However, the heuristics of system 1 can lead to errors and thus to lower decision quality due to biases, i.e. systematic and predictable perceptual distortions (Tversky & Kahneman, 1974).
In general, there are three causes of biases; the first two are in System 1: (1) In the psychologybased bias, the non-linear translation of a stimulus when evaluating and weighing an alternative leads to an inaccurate decision. (2) The association-based error, caused by reference to information already in memory. (3) The strategy-based error, which refers to system 2, incorrect decision strategies lead to a bias. (Arkes, 1991)
Biases in this perspective are faults and disturbances of perception or behaviours that lead to the application of less goal-oriented alternatives and, thus, to decision errors. Arnott lists 37 known biases (Arnott, 2005) and distinguishes six commonly known bias categories: First, memory biases, which arise in the storage and retrieval of information in memory. As an example, Arnott (2006) mentions the hindsight bias, i.e. the subsequent overestimation of the prediction of an event that has occurred. Another example is the misconception that an event seems to occur more frequently because it can be remembered more often (recall bias). Secondly, statistical biases, which influence the processing of probabilities. One example is the chance bias, because of which chance events are perceived as actual attributes of projects or processes. Another example is the sample bias, where the sample size is neglected in the evaluation of alternatives. Third, is the confidence bias, which overestimates the thrust in one's own abilities. An example is confirmation bias, whereby decision-makers seek confirmation rather than disconfirmation when presented with new information. The overconfidence bias, i.e. the overestimation of a decision maker's ability to solve a complex or new problem only because of his or her abilities. Fourth, adjustment biases, which lead to unplanned adjustments in decisions. One example is conservatism. This bias leads to old estimates not being updated with new significant data. Fifth, presentation biases, which lead to information processing and reception errors due to the presentation of the basis for decision-making. Another example is the order bias, where the information presented first, or last is overvalued. Sixth, situation biases, which lead to decision errors due to a particular decision situation. One example is the Escalation of Commitment, which leads to the further pursuit of an action that was already unsatisfactory. Another example is habit bias, whereby an alternative is chosen because it has already been selected in the past. The most comprehensive overview of biases in large-scale projects is provided by Shore. He distinguishes nine biases: Available Data, Conservatism, Escalation of commitment to a failing course of action, Groupthink, Illusion of control, Overconfidence, Recency, Selective Perception and Sunk Cost (Shore, 2008).
Debiasing is understood as all measures and activities that avoid or reduce systematic and predictable perceptual distortions in the optimisation phase of the decision-making process, thus enabling more accurate optimisation procedures in decision-making processes as well as more goal-oriented decisions (Arnott, 2005). Debiasing always requires an intervention. This can be either an internal or an external debiasing approach. The internal debiasing approach is the strategic modification of the individual's cognitive strategies. This is about identifying and selecting the right internal strategy for the individual and thus increasing the rationality of the decision. An external debiasing approach is a technological approach. It is about using external tools to increase the rationality of the individual's decision-making. Such tools can be, for example, the use of decision-making aids, better methods for providing information, statistical methods or decision-making in groups (Koehler & Nigel, 2007). The literature distinguishes between motivational, cognitive and technological strategies that can be used to avoid or reduce biases (s. Appendix). Motivational strategies include incentives and increasing accountability. Cognitive strategies include four debiasing possibilities: (1) Consider the-Opposite, where the decision-maker directs his attention to other strategies by asking himself for possible reasons why his original approach was wrong. This strategy successfully works against many biases, such as overconfidence or hindsight bias. (2) Training in decision-making can increase rationality by activating both system 2 and system 1. (3) Training in representativeness rather than probabilities can improve decision-making because people can deal more easily with frequency information. (4) Training in biases can also increase rationality. In the case of technological strategies, three debiasing options can increase rationality: (1) Group decisionmaking is preferable to individual decision-making because diverse experiences and different perspectives come together in the group. (2) Decomposing complex problems into smaller, more easily solvable components can rationalise decisions. (3) software-based decision support systems can compensate for the low human cognitive capacities thanks to their higher data processing capacities and thus make more rational decisions (Koehler & Nigel, 2007). Debiasing approaches applied in practice are, for example, greater stakeholder participation, reference class forecasting for predictions, behaviour-based controlling with the help of incentives and increased responsibility, the use of a project team that is as experienced as possible, regular exchange of decision-makers and training (Flyvbjerg, et al., 2013).
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