What methodologies are there for validating computer models?

There are several methodologies for validating computer models, including face validation, sensitivity analysis, and cross-validation.

Face validation is a simple and straightforward method of validating computer models. It involves comparing the output of the model with the expectations of an expert in the field. If the model's output aligns with the expert's expectations, it is considered valid. However, this method is subjective and relies heavily on the expert's knowledge and experience. It is often used as a preliminary validation method before more rigorous methods are applied.

Sensitivity analysis is another method used to validate computer models. This involves changing the input parameters of the model and observing the effect on the output. If the model behaves as expected when the inputs are changed, it is considered valid. This method is particularly useful for complex models with many input parameters, as it can help to identify which parameters have the most significant impact on the model's output.

Cross-validation is a more rigorous method of validating computer models. It involves dividing the data into two sets: a training set and a test set. The model is trained on the training set and then tested on the test set. The model's predictions are compared with the actual values to determine its accuracy. This method is commonly used in machine learning and statistical modelling.

Another method is the use of historical data for validation. This involves using past data to test the model's predictions. If the model can accurately predict past events, it is likely to be able to predict future events. This method is often used in financial and economic modelling.

Lastly, there's the method of independent validation. This involves having a separate team or individual, who was not involved in the development of the model, test the model. This can help to eliminate any bias that may have been introduced during the development of the model.

Each of these methods has its strengths and weaknesses, and often a combination of methods is used to thoroughly validate a computer model. The choice of validation method depends on the type of model and the data available.