Classification of Models

Classification of Models

The classification of models may be distinguished as follows:
1. Models by function
2. Models by structure
3. Models by nature of an environment
4. Models by the extent of generality

 1. Models by function

These models consist of (a) Descriptive models (b) Predictive models and (c) Normative models.

Descriptive models: They describe and predict facts and relationships among the various activities of the problem.
These models do not have an objective function as part of the model to evaluate decision alternatives. In these models, it is possible to get information as to how one or more factors change as a result of changes in other factors.

Normative or optimization models: They are prescriptive in nature and develop objective decision-rule for optimum solutions.

2. Models by structure

These models are represented by (a) Iconic models (b) Analogue models, and (c) Mathematical or symbolic models.

Iconic or physical models: They are pictorial representations of real systems and have the appearance of the real thing. An iconic model is said to be scaled down or scaled up according to the dimensions of the model which may be smaller or greater than that of the real item, e.g., city maps, houses blueprints, globe, and so on. These models are easy to observe and describe, but are difficult to manipulate and are not very useful for the purpose of prediction.

Analog models: These are more abstract than the iconic ones for there is no look alike correspondence between these models and real life items. The models in which one set of properties is used to represent another set of properties are called analog models. After the problem is solved, the solution is reinterpreted in terms of the original system. These models are less specific, less concrete, but easier to manipulate than iconic models.

Mathematic or symbolic models: They are most abstract in nature. They employ a set of mathematical symbols to represent the components of the real system. These variables are related together by means of mathematical equations to describe the behaviour of the system. The solution of the problem is then obtained by applying well developed mathematical techniques to the model. The symbolic model is usually the easiest to manipulate experimentally and it is the most general and abstract. Its function is more explanatory than descriptive.

 3. Models by nature of an environment

These models can be further classified into (a) Deterministic models and (b) Probabilistic models.

Deterministic models: They are those in which all parameters and functional relationships are assumed to be known with certainty when the decision is to be made. Linear programming and break-even models are the examples of deterministic models.

Probabilistic or stochastic models: These models are those in which atleast one parameter or decision variable is a random variable. These models reflect to some extent the complexity of the real world and the uncertainty surrounding it.

4.Models by the extent of generality

These models can be further categorized into (a) Specific models (b) General models

When a model presents a system at some specific time, it is known as a specific model.
In these models, if the time factor is not considered, they are termed as static models. An inventory problem of determining economic order quantity for the next period assuming that the demand in planning period would remain same as that of today is an example of static model. Dynamic programming may be considered as an example of dynamic model. Simulation and Heuristic models fall under the category of general models.