What are effective ways to validate and verify operations research models and assumptions?

1 Validation vs Verification

Validation and verification are two related but distinct concepts in OR. Validation is the process of checking whether the model accurately represents the problem and its objectives, while verification is the process of checking whether the model is correctly implemented and free of errors. Validation is more subjective and requires comparing the model outputs with the actual or expected outcomes, while verification is more objective and requires testing the model logic and functionality.

2 Face Validation

One of the simplest and most common ways to validate a model is to use face validation, which involves asking experts or stakeholders to evaluate the model based on their experience and intuition. Face validation can help identify obvious flaws, inconsistencies, or unrealistic assumptions in the model. However, face validation is also prone to biases, preferences, or opinions that may not be supported by data or evidence. Therefore, face validation should be complemented by other methods that are more rigorous and quantitative.

3 Sensitivity Analysis

Another way to validate a model is to use sensitivity analysis, which involves changing the values of the model parameters or inputs and observing how the model outputs or outcomes change. Sensitivity analysis can help assess how robust the model is to variations in the data or assumptions, and how sensitive the model is to certain factors that may affect the problem. Sensitivity analysis can also help identify the most critical or influential parameters or inputs that need more attention or accuracy.

4 Statistical Validation

A more advanced way to validate a model is to use statistical validation, which involves comparing the model outputs with empirical data or observations using statistical tests or measures. Statistical validation can help quantify the degree of agreement or discrepancy between the model and the reality, and evaluate the significance or confidence of the results. Statistical validation can also help determine the validity of the assumptions or hypotheses that underlie the model.

5 Code Review

One of the simplest and most common ways to verify a model is to use code review, which involves inspecting the code or algorithm that implements the model for errors, bugs, or defects. Code review can help ensure that the code follows the standards, conventions, or best practices of programming, and that the code matches the logic and structure of the model. Code review can also help improve the readability, maintainability, or efficiency of the code.

6 Debugging and Testing

Another way to verify a model is to use debugging and testing, which involves running the code or algorithm that implements the model with different inputs or scenarios and checking the outputs or outcomes for errors, anomalies, or deviations. Debugging and testing can help identify and correct any errors or issues that may arise during the execution of the code or algorithm, and ensure that the code or algorithm produces the expected or desired results. Debugging and testing can also help optimize the performance or functionality of the code or algorithm.

7 Simulation and Experimentation

A more advanced way to verify a model is to use simulation and experimentation, which involves creating and running multiple instances or variations of the model with different inputs or parameters and observing the outputs or outcomes. Simulation and experimentation can help explore the behavior or characteristics of the model under different conditions or situations, and compare the model results with other models or methods. Simulation and experimentation can also help validate the model by comparing the simulation results with empirical data or observations.

8 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?