Purposes Of Models

By Author: Sherry Roberts
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A model is a depiction of the structure and working of a system of interest. A model is alike to but simpler than the system it corresponds to. A model allows the analyst to predict the effect of changes to the system. (Anu, 1997) To effectively meet its purpose, a model should closely incorporate most of the system’s salient features and be of a close resemblance to the real system. It should also not too complex that it is impractical to experiment with it or understand it. There are various purposes of models:
Study of Transient Behavior Systems
Many real world situations are too complex to evaluate effectively. This calls for an alternative method to be used in the evaluation of the performance of such systems. Models are widely used to design, plan and control a proposed system. In other cases, modeling may be the only reasonable initial approach especially when theoretical relationships are well-known, even though, the system does not yet exist.
Flexibility
With a model, the user is able to study and examine the behavior of a process without the need of experimenting with the actual system. To ...
... effectively identify the bottlenecks or evaluate a system’s performance given various disturbances, models are the best tools. Models may be used in instances where it is exceptionally costly, often impossible or dangerous to make experiments with real systems. Provided that models reflect the real system or are adequate descriptions of reality, experimenting with them can save money, energy and even time. What is more, other alternatives can be evaluated by changing the input data used in the model. Modeling also allows the analyst to capture the necessary information without disturbing the real system.
Communication
Modeling provides insight and a deep understanding of physical processes that are being modeled. Whether a particular experiment can be successful can best be checked by watching a dynamic display, and, therefore, can act as a tool of communication. The modeling of the process under investigation results to beneficial communication between the model builder and model user. It also benefits the model builder and in presentation to users and management. Users of the model can be actively involved with a model throughout its development cycle owing to the increased ease of communication that modeling allows. Through communication, the model can be used for teaching purposes to illustrate the process to enable or to comprehend a process better. The approach also allows compression of time whereby a model accomplishes in minutes what might require several years of actual experimentation. When the process relates to dynamic processes, models provide the only means for detailed and direct observation within specified time limits. (Anu, 1997) The prediction of system behavior by modeling is not only a communication tool but also a tool to support decision making.
Improvements made to your draft qualitative model
I made several improvements to my draft qualitative model as a result of feedback from the class. From the feedback, I recognized that a good model should be a judicious trade-off between realism and simplicity. First, I ensured that the model is a close approximation to the real system through incorporating more of its salient features. Secondly, I reduced its complexity to make it more understandable and easier to use in the experimentation.
Methods to Build Confidence in models
Confidence in models is build through thorough validation and verification of the models. Without thorough Validation and Verification, there are no grounds on which to place confidence in a study’s results. Verification and validation are, therefore, significant elements of any study that incorporates modeling. (Sargent, 2013)
Validation involves ensuring that the model is sufficiently accurate for the study purpose at hand. Validation, therefore, entails building the right model. No model is ever a hundred percent accurate. A key concept is the idea of sufficient accuracy; certainly, there are good reasons for not having absolutely accurate models. On the other hand, Verification involves ascertaining that the conceptual model or model design has been transformed with sufficient accuracy. Verification, therefore, involves, building the model right. (Sargent, 2013)
The aim of validation and verification is to build confidence by ensuring that models are sufficiently accurate. Additionally, the accuracy is with relates to the purpose for which the models are to be used. A demonstration model that is highly inaccurate can still be described as valid because it is built with the sole purpose of demonstrating. As a result, the objectives or purpose of a model should be known before it can be validated. The purpose may be determined at the beginning of the study or may be an alternative use for an existing model
Conceptual model validation
Conceptual model validation is involves determining that the underlying assumptions and theories used in the development of the model design are consistent with those of the system it is essential for the modeler to obtain an in-depth understanding of the problem to be tackled and the real world system. This requires a lot of interaction with those who have in-depth understanding of the system. (Stewart, 1997)
Data Validation
Inaccurate data could be a substantial source of inaccuracy in any model. It is, therefore, imperative that extra effort is made to ensure that input data is as accurate as possible. The data should be analyzed for reliability and inconsistencies. (Stewart, 1997)
Visual Checks
The logic and behavior of the model and can be checked against the real world by running the model and watching how each element behaves. (Stewart, 1997)
Verification and White-box Validation
Verification and White-box Validation are both micro checks of the model’s content. White- Box validation ensures that the content of the model is true to the real world while Verification ensures that the model is true to the design. (Stewart, 1997)
Inspecting Output Reports
The performance of individual elements, from input distributions, can aid in checking if they were modeled correctly. By inspecting the reports from a run, the actual and expected results can be compared. (Stewart, 1997)
Comparison with Other Models
Models can be compared against other models especially when no real system data is available. Using this method together with real world comparison can only serve to increase confidence. (Stewart, 1997)
Comparison with the Real System
Data collected from the real system can be compared to the results of the model when it is run under the same conditions. This is done by judging how closely the averages from the model and the real world. (Stewart, 1997)

References
Anu Maria (1997) Introduction to Modeling
R G Sargent(2013) Verification and validation of simulation models Journal of Simulation (2013) 7, 12–24. doi:10.1057/jos.2012.20;
Stewart Robinson (1997) Simulation Model Verification and Validation: Increasing the users confidence

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