Definition
Correctness of a model
Refers to how accurately the model represents the real-world system it is designed to simulate.
- To determine correctness, we compare the results generated by the model with data observed in the original problem.
- If the model's predictions align closely with real-world observations, it is considered correct.
Steps to Assess Correctness
- Collect Real-World Data: Gather data from the original problem.
- Run the Model: Use the model to generate results.
- Compare Results: Analyze how closely the model's results match the real-world data.
- Identify Discrepancies: Note any significant differences.
- Refine the Model: Adjust the model to improve its accuracy.
- Traffic Flow Model
- Real-World Data: Average speed of cars on a highway is 60 km/h.
- Model Prediction: The model predicts an average speed of 55 km/h.
- Comparison: The model is close but slightly underestimates the speed.
- Refinement: Adjust the model to account for factors like fewer traffic jams.
