# 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[edit]

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[edit]

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:**

## References[edit]

- McLeod, R., & Schell, G. (2001).
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*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.