Chapter 1 Flashcards
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.
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.
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.
4
Q
Stochastic model
A
One that recognises the random nature of the input components.
5
Q
Deterministic model
A
A model that does not contain any random component.
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.