computer science

Verification and validation of computer simulation models

Verification and validation of computer simulation models is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Verification and validation of computer simulation models rather than just read about it. In short: Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. "Simulation models are increasingly being used to solve problems and to aid in decision-making. The developers and users of these models, the decision makers using information obtained from the results of these models, and the individu…

Key takeaways

Verification and validation of computer simulation models belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Verification and validation of computer simulation models to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Verification and validation of computer simulation models from memory before moving on to harder problems.

Reference excerpt

Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. "Simulation models are increasingly being used to solve problems and to aid in decision-making. The developers and users of these models, the decision makers using information obtained from the results of these models, and the individuals affected by decisions based on such models are all rightly concerned with whether a model and its results are "correct". This concern is addressed through verification and validation of the simulation model. Simulation models are approximate imitations of real-world systems and they never exactly imitate the real-world system. Due to that, a model should be verified and validated to the degree needed for the model's intended purpose or application. The verification and validation of a simulation model starts after functional specifications have been documented and initial model development has been completed. Verification and validation is an iterative process that takes place throughout the development of a model.

Verification In the context of computer simulation, verification of a model is the process of confirming that it is correctly implemented with respect to the conceptual model (it matches specifications and assumptions deemed acceptable for the given purpose of application). During verification the model is tested to find and fix errors in the implementation of the model. Various processes and techniques are used to assure the model matches specifications and assumptions with respect to the model concept. The objective of model verification is to ensure that the implementation of the model is correct. There are many techniques that can be utilized to verify a model. These include, but are not limited to, having the model checked by an expert, making logic flow diagrams that include each logically possible action, examining the model output for reasonableness under a variety of settings of the input parameters, and using an interactive debugger. Many software engineering techniques used for software verification are applicable to simulation model verification.

Validation Validation checks the accuracy of the model's representation of the real system. Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". A model should be built for a specific purpose or set of objectives and its validity determined for that purpose. There are many approaches that can be used to validate a computer model. The approaches range from subjective reviews to objective statistical tests. One approach that is commonly used is to have the model builders determine validity of the model through a series of tests. Naylor and Finger [1967] formulated a three-step approach to model validation that has been widely followed: Step 1. Build a model that has high face validity. Step 2. Validate model assumptions. Step 3. Compare the model input-output transformations to corresponding input-output transformations for the real system.

Face validity A model that has face validity appears to be a reasonable imitation of a real-world system to people who are knowledgeable of the real world system. Face validity is tested by having users and people knowledgeable with the system examine model output for reasonableness and in the process identify deficiencies. An added advantage of having the users involved in validation is that the model's credibility to the users and the user's confidence in the model increases. Sensitivity to model inputs can also be used to judge face validity. For example, if a simulation of a fast food restaurant drive through was run twice with customer arrival rates of 20 per hour and 40 per hour then model outputs such as average wait time or maximum number of customers waiting would be expected to increase with the arrival rate.

Validation of model assumptions Assumptions made about a model generally fall into two categories: structural assumptions about how system works and data assumptions. Also we can consider the simplification assumptions that are those that we use to simplify the reality.

Structural assumptions Assumptions made about how the system operates and how it is physically arranged are structural assumptions. For example, the number of servers in a fast food drive through lane and if there is more than one how are they utilized? Do the servers work in parallel where a customer completes a transaction by visiting a single server or does one server take orders and handle payment while the other prepares and serves the order. Many structural problems in the model come from poor or incorrect assumptions. If possible the workings of the actual system should be closely observed to understand how it operates. The systems structure and operation should also be verified with users of the actual system.

Data assumptions There must be a sufficient amount of appropriate data available to build a conceptual model and validate a model. Lack of appropriate data is often the reason attempts to validate a model fail. Data should be verified to come from a reliable source. A typical error is assuming an inappropriate statistical distribution for the data. The assumed statistical model should be tested using goodness of fit tests and other techniques. Examples of goodness of fit tests are the Kolmogorov–Smirnov test and the chi-square test. Any outliers in the data should be checked.

Simplification assumptions Are those assumptions that we know that are not true, but are needed to simplify the problem we want to solve. The use of this assumptions must be restricted to assure that the model is correct enough to serve as an answer for the problem we want to solve.

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Worked examples

Example 1 — a first encounter with Verification and validation of computer simulation models

Start with the simplest possible case. Write down what Verification and validation of computer simulation models claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Verification and validation of computer simulation models before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Verification and validation of computer simulation models ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Verification and validation of computer simulation models

In research: Verification and validation of computer simulation models appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Verification and validation of computer simulation models in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Verification and validation of computer simulation models is common in secondary-school and first-year university syllabi. It links to neighbouring topics Formal methods, Simulation software, so understanding it makes those chapters shorter.
In everyday life: Look for Verification and validation of computer simulation models outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Verification and validation of computer simulation models in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Verification and validation of computer simulation models means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Verification and validation of computer simulation models out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Verification and validation of computer simulation models in simple terms?

Why does Verification and validation of computer simulation models matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Verification and validation of computer simulation models?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Verification and validation of computer simulation models.