Selection process in conditions of certainty and uncertainty

The concept of decision-making model plays a key role in the description and analysis of the decision-making processes. Given the dual meaning of the model, as a reference and as a representation, decision making model can be treated in two ways. In general, the decision model can be regarded as a representation of reality in particular problem area, and is used as key instrument of selection process in various conditions of decision-making.

Typology of selection process models

This model also includes normative elements of the decision, which is a collection of satisfactory decision or a set of optimal decision. There is clear distinction between descriptive models (model mapping) and normative models (standard model).

Often only mathematical models are regarded as a decision-making models. But in general, the modelling of the decision is also used in other fields such as psychology, computer science, or even philosophy.

There are many typologies of decision models. The simplest division is for: qualitative and quantitative models. This division is very simplified, since quantitative models reduces the problems described in a concrete form, which is measurable using the ratio scale, or at least on a scale interval. Sometimes it is used in everyday language to determine the categories of "hard" - Quantitative and "soft" - qualitative problems.

The next division is based on: heuristic models and formal models. Heuristic models contain variables that can not be measured. They are associated with quality models and is used in determining psychological aspects of decision-making processes. Formal models can be divided into two groups - logical models and mathematical models. One example is so-called logic "Decision tree" as a model of the implementation of decision-making. Mathematical models are abstract and often simplified representation of the real decision-making problems.

Mathematical models used in operational research and are determined by the nature of the parameters contained in them. If the parameters are non-random values, then we say that we are dealing with deterministic models. Each possible decision leads to clearly defined results, or in other words, each decision corresponds to one and only one value of the function. In this case, the chosen values ​​of decision variables to the function adopted or maximum values, such as profit, or minimal, such as costs.

Conditions of decision making

The nature of the decision problem is related to the conditions under which decisions are made. Classic interpretation adopted in decision theory includes three types of conditions:

• Certainty - every action invariably leads to a clearly identified as a result,
• Risk - each operation leads to the result from a given set of possible outcomes, each of which has a known probability of occurrence, is assumed that the probability of outcome is known to the decision-maker.
• Uncertainty - if even for a single set of performance measures are unknown probabilities or it does not make sense to talk about the probability calculation.

See also:

• Impact of information on decision-making
• Information and decision making
• Decision tree

Examples of Selection process in conditions of certainty and uncertainty

• In conditions of certainty, decisions are taken using a structured decision making model which includes identifying the decision problem, gathering relevant data, developing alternatives, evaluating options, selecting the best option, implementing the decision and monitoring the outcome. An example of this is when a business is deciding whether to invest in new technology, they would use a structured decision making model to decide if it is the best use of their resources.
• In conditions of uncertainty, decisions are taken using a heuristics-based decision making model, which involves making decisions based on experience, intuition, and rules of thumb. An example of this is when a company is deciding whether to enter a new market, they would use a heuristics-based decision making model to assess the risk and rewards associated with the decision.
• In conditions of risk, decisions are taken using a risk management decision making model which involves assessing the likelihood of certain outcomes, and the potential consequences of those outcomes. An example of this is when a company is deciding whether to enter a new market, they would use a risk management decision making model to assess the potential risks associated with the decision.

Advantages of Selection process in conditions of certainty and uncertainty

A decision-making model is a useful tool for both certain and uncertain scenarios. It can help in the selection process by providing a framework for analyzing and weighing different options, and can be used to help identify the best course of action. Below are some of the advantages of using a decision-making model in conditions of certainty and uncertainty:

• In a situation of certainty, a decision-making model provides a clear structure for selecting the best option amongst several alternatives. It can help to ensure that all relevant aspects of the problem have been considered, and that all options have been weighed objectively.
• In a situation of uncertainty, a decision-making model can help to provide a structure for making decisions based on the available data and information. This can help to reduce the impact of biases and assumptions that may cloud the decision-making process.
• A decision-making model can also help to provide a sense of organization and structure to the decision-making process, which can make it easier to evaluate various options and to select the best course of action.
• Furthermore, a decision-making model can help to inform the decision-maker of potential risks associated with the decision, which can in turn help to reduce the risk of making a poor decision.

Limitations of Selection process in conditions of certainty and uncertainty

In general, the selection process in conditions of certainty and uncertainty can be limited by several factors:

• Limited information: The decision-maker may not have access to all the necessary information, which can lead to decisions being made without a full understanding of the situation.
• Conflicting objectives: Different stakeholders may have different objectives, resulting in a clash of interests which can hinder the decision-making process.
• Cognitive biases: The decision-maker may be influenced by cognitive biases, such as overconfidence or the availability heuristic, which can lead to incorrect decisions.
• Short-term focus: The decision-maker may be focused on short-term gains, rather than long-term objectives, leading to decisions which may not be the most beneficial in the long run.
• Lack of incentives: If the decision-maker does not have any incentives to make the correct decision, then they may not be motivated to make the best choice.
• Groupthink: In a group decision-making process, groupthink can lead to a situation in which the group makes decisions which are not in its own best interest.

Other approaches related to Selection process in conditions of certainty and uncertainty

In regards to the selection process in conditions of certainty and uncertainty, there are several approaches that can be taken. These include:

• Rational Choice Theory: This theory focuses on the notion that individuals will make decisions that are in their best self-interests. It takes into account the notion that people weigh both the costs and benefits associated with a particular decision and choose the option that will bring the most benefit while minimizing the cost.
• Utility Theory: This theory focuses on the idea that individuals will make decisions that maximize their utility or satisfaction. This involves taking into account the individual’s preferences and beliefs in order to determine the best decision.
• Game Theory: This theory focuses on the notion that individuals will make decisions based on the anticipated behavior of others. It takes into account the notion that people make decisions based on the expected responses of others and the potential rewards associated with particular decisions.
• Heuristics: This theory focuses on the notion that individuals will make decisions based on mental shortcuts or rules of thumb. It takes into account the notion that people make decisions based on previously established preferences or beliefs that have been proven to be successful in the past.

In summary, these four approaches to decision-making in conditions of certainty and uncertainty are Rational Choice Theory, Utility Theory, Game Theory, and Heuristics. Each of these theories takes into account different factors to determine the best course of action in any given decision.

Selection process in conditions of certainty and uncertainty — recommended articles

Impact of information on decision-making — Analysis of preferences — Decision making — Rational decision making — Decision process models — Expected utility theory — Contribution analysis — Conditions of decision-making — Risks and uncertainties

References

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• Newell, A., & Simon, H. A. (1972). Human problem solving (Vol. 104, No. 9). Englewood Cliffs, NJ: Prentice-Hall.
• O'Brien, J., Marakas, G. M., Hills, T. M. G., & Lalit, M. R. (2006). Management information systems.
• Simon, H. A. (1971). Designing organizations for an information-rich world. Computers, communication, and the public interest, 37, 40-41.
• Simon, H. A. (1978). Information-processing theory of human problem solving. Handbook of learning and cognitive processes, 5, 271-295.