Chapter 1

Question 1

14 Key steps in a modelling process

Answer 1

• Develop a well-defined set of objectives
• Plan the process and how it will be validated
• Collect and analyse the necessary data for the model
• Define the parameters for the model and consider appropriate parameter values
• Define the model initially by capturing the essence of the real world system.
• Involve experts on the real word system you are trying to imitate so as to get feedback on the validity of the conceptual model.
• Decide on whether a simulation package or a general purpose language is appropriate for the implementation of the model.
• Write the computer program for the model.
• Debug the program to make sure it performs the intended operations in the model definition.
• Test the reasonableness of the output from the model.
• Review and carefully consider the appropriateness of the model in light of small changes in input parameters.
• Analyse the output from the model.
• Ensure that any relevant professional guidance has been complied with.
• Communicate and document the results and the model.

Question 2

Advantages of models

Answer 2

• Systems with long time frames can be studied in compressed time
• Complex systems with stochastic elements cannot be properly described by a mathematical or logical model that is capable of producing results that are easy to interpret.
• Different future strategies or possible actions can be compared to see which best suits the requirements or constraints of a user.
• In a model of a complex system we can usually get control over the experimental conditions so that we can reduce the variance of the results output from the model without upsetting the mean values.

Question 3

Disadvantages of models

Answer 3

• Require a considerable investment of time and expertise.
• Stochastic model: a given set of inputs each run gives only estimates of a model’s outputs. So to study the outputs for any given set of inputs, several independent runs of the model are needed.
• In general, models are more useful for comparing the results of input variations than for optimising outputs.
• Danger of getting lulled into a false sense of confidence.
• Models rely heavily on the data input. If the data quality is poor or lacks credibility, then the ouput from the model is likely to be flawed
• It is important that the users of the model understand the model and the uses to which it can be safely put. Danger of using a model as a “black box” - using the model without considering the appropriateness thereof.
• It is not possible to include all future events in a model,
• It may be difficult to interpret some of the outputs.

Question 4

Stochastic model

Answer 4

One that recognises the random nature of the input components.

Question 5

Deterministic model

Answer 5

A model that does not contain any random component.

Question 6

Considerations with regard to the suitability of a model:

Answer 6

• The time and cost of constructing and maintaining the model.
• The objectives of the modelling exercise
• The validity of the model for the purpose to which it is to be put.
• The validity of the data to be used.
• The validity of the assumptions.
• The possible errors associated with the model or parameters used not being a perfect representation of the real world situation being modelled.
• The impact of correlations between the random variables that “drive” the model.
• The current relevance of models written and used in the past.
• The credibility of the data input.
• The credibility of the results output.
• The dangers of spurious accuracy.
• The ease with which the model and its results can be communicated.
• Regulatory requirements.