--- title: "Naylor and Finger validation approach" subject: "Simulation and Modeling" chapter: 7 key: "semester_8/simulation_and_modeling:naylor-finger-validation" source: https://www.bctengineer.com/q/semester_8/simulation_and_modeling/naylor-finger-validation site: BCT Engineering --- # Naylor and Finger validation approach Simulation and Modeling · Chapter 7: Verification and validation of simulation models - Asked in: 2073 Bhadra, 5 marks; 2070 Bhadra, 5 marks (asked 2 times) - Video: [03 SMS Module 5 Naylor finger approaches by Rahul](https://www.youtube.com/watch?v=8laO8JQihEc) — Vanishree Arun - Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14 - Page: https://www.bctengineer.com/q/semester_8/simulation_and_modeling/naylor-finger-validation - Whole chapter: https://www.bctengineer.com/md/semester_8/simulation_and_modeling/chapter-7 ## Question Explain Naylor and Finger's validation approach (steps used in validation). ## Short answer Naylor and Finger's three-step approach: (1) Build model with high face validity through expert review. (2) Validate structural and data assumptions using goodness-of-fit tests. (3) Compare model input-output transformations with the real system using statistical tests on historical data. ## Answer Naylor and Finger (1967) proposed a **three-step approach** to validate a simulation model. It combines rationalism (the model must make sense), empiricism (the assumptions must agree with data) and positive economics (the model must predict the real system's output well). Banks, Carson, Nelson & Nicol use it as the standard framework. #### Step 1: Build a model that has high face validity Face validity means the model looks reasonable to people who know the real system. To achieve it: - Discuss the model with managers, operators and users of the real system. - Use all known facts: observations of the system, past studies, relevant theory, and the experience of experts. - Show the model's structure, inputs and results to users and ask whether they are believable. - Check that results change in the expected direction when inputs change (more arrivals should give longer queues). #### Step 2: Validate the model assumptions Assumptions are of two kinds. - **Structural assumptions**: how the system works and is abstracted, such as queue discipline, number of servers, balking or breakdowns. These are checked by observing the real system and discussing with staff. - **Data assumptions**: the input data and the distributions fitted to it. Check the data collection method, test independence and stationarity, and use goodness-of-fit tests (chi-square, K-S) on the fitted distributions. #### Step 3: Compare input-output transformations The model is treated as a black box that turns input data into output measures. Run the model with the same input conditions as the real system and compare the outputs. - Use historical input data and compare model output with the real output. - Use statistical tests, such as a t-test on the difference of means, or a confidence interval for the difference. - If the real system does not exist, compare against another validated model or expert judgement. - A "Turing test" can also be used: experts try to tell real reports from model reports. #### Example For a bank teller model, step 1 is confirming with the manager that tellers, one queue and lunch breaks are modelled correctly. Step 2 checks that the inter-arrival times really fit an exponential distribution. Step 3 runs the model with last week's arrivals and compares the average customer delay with the measured delay. ```text Real system --> observe --> Step 1: face validity Data --> test --> Step 2: assumptions Inputs --> [Model] --> Outputs vs real outputs (Step 3) ``` ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. What are the three steps of Naylor and Finger's approach? A. Verify code, run replications, delete warm-up B. Collect data, fit distributions, run model C. Face validity, validate assumptions, compare input-output transformations D. Build, calibrate, terminate Answer: C. The steps are: high face validity, validation of assumptions, and comparison of input-output transformations. (Key point: Three steps) 2. Which is a data assumption rather than a structural assumption? A. Number of servers B. Queue discipline C. Balking behaviour D. Inter-arrival times fitting an exponential distribution Answer: D. Data assumptions concern input data and fitted distributions, tested by chi-square or K-S; the others are structural. (Key point: Assumption types) 3. How is Step 3 carried out? A. Run the model with the same inputs as the real system and compare outputs statistically B. Ask managers whether the model looks reasonable C. Read the code line by line D. Delete the initial transient Answer: A. The model is treated as a black box; outputs under the same inputs are compared using t-tests or confidence intervals. (Key point: Input-output step) 4. Which check supports face validity? A. Chi-square test on service times B. Results move in the expected direction when inputs change, e.g. more arrivals give longer queues C. Batch means confidence interval D. Using a debugger to trace the clock Answer: B. Face validity means the model looks reasonable to experts, including sensible response to input changes. 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