Brief overview of steps for tackling modelling process.

  1. What precisely is our problem and what do we want the model to do?
  2. Do we need a model at all? Perhaps it is too simple a task and we would sooner not waste time.
  3. Are similar models available? Can we buy one or use a pre-existing in-house one? Danger is discovering too late that some of its features were note accurate enough for current purpose.
  4. Choosing granularity and scope of models scale.
  5. What constitutes key part of system we are interested in?
  6. What constitutes basic time quanta of simulation (seconds, years, centuries) and how far in future do we want to predict behaviour we are investigating?
  7. Determine abstraction depth we want to model.
  8. Boundaries:
    1. How many aspects of reality should we include?
    2. How detailed will their description be?
    3. Also start with simple models and gradually add new features.
    4. Without simplicity, get stuck in too much data.
    5. Too simple a model loses realism and may miss peaks/troughs in performance.
  9. What are key processes and parameters of model?
  10. Choose correct modelling tools.
  11. Verification:
    1. Check if model behaves as expected and does what it is supposed to do.
  12. Validation:
    1. Check if model behaves realistically,
  13. For stochastic models, conduct statistical analysis of results.

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