Chapter 1 Flashcards

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1
Q

14 Key steps in a modelling process

A
  • 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.
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2
Q

Advantages of models

A
  • 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.
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3
Q

Disadvantages of models

A
  • 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.
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4
Q

Stochastic model

A

One that recognises the random nature of the input components.

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5
Q

Deterministic model

A

A model that does not contain any random component.

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6
Q

Considerations with regard to the suitability of a model:

A
  • 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.
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