2. Predictive validation
• Predictive validation is a crucial aspect of modeling and simulation,
particularly in assessing the model's ability to accurately predict real-
world outcomes.
• It involves comparing the model's predictions to actual observed data
to determine how well the model performs in predicting events or
behaviors. Predictive validation assesses the model's credibility and
its capacity to make reliable forecasts or estimations.
3. Define the Validation Criteria
• Before performing predictive validation, you must establish clear
criteria for what constitutes a successful validation. This includes
defining the specific aspects of the model's behavior or outputs that
you intend to validate. For instance, you may be interested in
validating the model's predictions of a particular variable or event.
4. Collect Real-World Data
• To validate the model, you need actual data that corresponds to the
events or behaviors you're modeling. This real-world data should be
collected from observations, measurements, or historical records. It
serves as the ground truth against which you'll compare the model's
predictions.
5. Divide Data into Training and Validation Sets:
• It's common to divide the real-world data into two sets: a training set
and a validation set. The training set is used to develop or calibrate
the model, while the validation set is reserved for assessing its
predictive performance. This separation helps prevent overfitting
(where the model fits the training data too closely and performs
poorly on new data).
6. Perform Predictive Validation:
• Apply the Model to the Validation Data: Use the calibrated model to
make predictions on the validation dataset. This is where the model is put
to the test.
• Calculate Validation Metrics: Compare the model's predictions to the
actual data from the validation set. Common metrics include:
• Mean Absolute Error (MAE): Measures the average absolute difference between
predicted and actual values.
• Root Mean Square Error (RMSE): Calculates the square root of the average
squared differences between predicted and actual values.
• Coefficient of Determination (R²): Indicates the proportion of the variance in the
data that is explained by the model.
• Assess Model Performance: Analyze the validation metrics to determine how well
the model's predictions align with actual observations. A lower MAE and RMSE
and a higher R² value indicate better predictive performance
7. Iterate and Improve:
. Based on the results of predictive validation, you may need to make
adjustments to the model. This could involve further calibration,
refining model parameters, or exploring different modeling approaches
to improve predictive accuracy.
• Document Validation Results: It's essential to document the results
of predictive validation, including the metrics, any adjustments made
to the model, and conclusions regarding its predictive performance.
Transparent documentation enhances the model's credibility.
• Predictive validation is a critical step in ensuring that your model
provides reliable predictions and can be trusted for decision-making.
It helps assess the model's suitability for its intended purpose and
identifies areas for improvement if necessary.
8. Parameter Variability and Sensitivity Analysis
• Parameter Variability and Sensitivity Analysis in the realm of
modeling and simulation are indispensable techniques for assessing
the robustness and reliability of models, especially when faced with
variations in input parameters. These methodologies are essential for
understanding how sensitive model outputs are to changes in model
parameters.
9. Parameter Variability
• In complex models, various input parameters represent critical
aspects of the system being simulated. These parameters might not
remain constant in real-world scenarios; they can vary due to
uncertainties or fluctuations.
• Parameter Variability acknowledges this inherent variation by
allowing these parameters to take on different values within specified
ranges or distributions.
• It is often used to account for the uncertainty that exists in real-world
systems, helping models provide more realistic and robust results.
10. Sensitivity Analysis:
• Sensitivity Analysis is a systematic study of how variations in input
parameters impact the model's outputs. It quantifies the relationship
between changes in parameters and corresponding changes in model
outcomes.
• This analysis helps in identifying which parameters have the most
significant influence on the model's results and which ones have a
lesser impact.
• There are various methods for performing Sensitivity Analysis, such as
one-at-a-time (OAT) analysis, factorial design, and Monte Carlo
simulations. Each method has its strengths depending on the
complexity of the model and the objectives of the analysis.
11. The Process of Performing Sensitivity Analysis:
• Parameter Identification: First, identify the parameters in your model that exhibit
variability or uncertainty. These parameters should be carefully selected based on
their relevance to the system and available data.
• Parameter Variation: Define a range or distribution for each selected parameter
that represents its variability or uncertainty. This might involve specifying
minimum and maximum values or probability distributions.
• Model Execution: Run the simulation model repeatedly, each time with a
different set of parameter values sampled from the defined ranges or
distributions. This creates a series of model runs, each producing different output
results.
• Outcome Analysis: Analyze the results obtained from each model run. Calculate
how changes in input parameters correspond to changes in output metrics of
interest. Common sensitivity metrics include partial derivatives, correlation
coefficients, and regression analysis.
12. • Interpretation: Interpret the sensitivity analysis results to understand
which parameters have the most substantial impact on the model's
outcomes and which are relatively less influential. This insight is
invaluable for decision-making, risk assessment, and model
refinement.
• Reporting and Documentation: Properly document the sensitivity
analysis process, including parameter selections, variation ranges,
analysis methods, and results. Transparent reporting is crucial for
maintaining the credibility of the analysis.
• In professional practice, Parameter Variability and Sensitivity Analysis
play critical roles in quantifying uncertainty, guiding decision-making,
and enhancing the reliability of models used in various fields,
including engineering, economics, environmental science, and more.
