FAQ

  1. How can I treat multiple model output (such as time series)?
    19/05/2015

    The objective function of a sensitivity analysis exercise is assumed to be a scalar function. When the model has several output variables, the analysis has to be repeated several times, one for each output variable.

    More information: Sensitivity analysis

    Keywords: statistics
  2. How should I choose input distributions for the model inputs?
    19/05/2015

    Sensitivity analysis is not concerned with the choice of the distributions followed by the model inputs. These distributions have to be derived from available sources of information, such as expert opinions or literature. Results of the sensitivity analysis exercise are conditional upon the distributions chosen.

    More information: Sensitivity analysis

    Keywords: statistics
  3. What can I do if my model has a large number of inputs?
    19/05/2015

    You should start by performing a screening exercise. This is a preliminary analysis that allows you to select the subset of the most potentially explanatory factors. Afterwards, a quantitative method is recommended on the subset of pre-selected inputs.

    More information: Sensitivity analysis

    Keywords: statistics
  4. Which is the best method to perform SA on my model?
    19/05/2015

    The choice of which SA method to adopt is difficult as each technique has strengths and weaknesses. Such a choice depends on the problem the investigator is trying to address, on the characteristics of the model under study, and also on the computational cost that the investigator can afford.

    More information: Sensitivity analysis

    Keywords: statistics
  5. What are the general steps needed to perform uncertainty and sensitivity analysis?
    19/05/2015

    There are several possible procedures to perform uncertainty and sensitivity analysis. The most common sensitivity analysis is sampling-based. A sampling-based sensitivity is one in which the model is executed repeatedly for combinations of values sampled from the distribution (assumed known) of the input factors. In general, UA and SA are performed jointly by executing the model repeatedly for combination of factor values sampled with some probability distribution.

    The following steps can be listed:

    1. specify the target function and select the input of interest
    2. assign a distribution function to the selected factors
    3. generate a matrix of inputs with that distribution(s) through an appropriate design
    4. evaluate the model and compute the distribution of the target function
    5. select a method for assessing the influence or relative importance of each input factor on the target function.

    More information: Sensitivity analysis

    Keywords: statistics
  6. What is difference between uncertainty and sensitivity analysis?
    19/05/2015

    Although closely related, uncertainty analysis and sensitivity analysis are two different disciplines. Uncertainty analysis assesses the uncertainty in model outputs that derives from uncertainty in inputs. Sensitivity analysis assesses the contributions of the inputs to the total uncertainty in analysis outcomes.

    More information: Sensitivity analysis

    Keywords: statistics
  7. What are the reasons to conduct sensitivity analysis?
    19/05/2015

    Modellers may conduct SA to determine:

    1. the model resemblance with the process under study,
    2. the quality of model definition,
    3. factors that mostly contribute to the output variability,
    4. the region in the space of input factors for which the model variation is maximum,
    5. optimal regions within the space of factors for use in a subsequent calibration study,
    6. interactions between factors.

    More information: Sensitivity analysis

    Keywords: statistics
  8. Why do I need sensitivity analysis?
    19/05/2015

    A mathematical model is defined by a series of equations, input factors, parameters, and variables aimed to characterise the process being investigated. Input is subject to many sources of uncertainty including errors of measurement, absence of information and poor or partial understanding of the driving forces and mechanisms. This imposes a limit on our confidence in the response or output of the model. Further, models may have to cope with the natural intrinsic variability of the system, such as the occurrence of stochastic events. Good modelling practice requires that the modeller provides an evaluation of the confidence in the model, possibly assessing the uncertainties associated with the modelling process and with the outcome of the model itself. Uncertainty and sensitivity analysis offer valid tools for characterising the uncertainty associated with a model.

    More information: Sensitivity analysis

    Keywords: statistics
  9. What is sensitivity analysis?
    19/05/2015

    Sensitivity analysis (SA) is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation.

    More information: Sensitivity analysis

    Keywords: statistics
  10. What is a composite indicator?
    02/02/2015

    An indicator can be seen as something that provides a clue to a matter of larger significance or makes perceptible a trend or phenomenon that is not immediately detectable. An indicator's main defining characteristics are that it quantifies and simplifies information in a manner that promotes the understanding of complex phenomena, e.g. environmental problems, to both decision-makers and the public. Above all, an indicator must be practical and realistic, given the many constraints faced by those implementing and monitoring projects. They are often a compromise between scientific accuracy and the information available at a reasonable cost.

    A mathematical combination (or aggregation as it is termed) of a set of indicators is most often called an "index" or a "composite indicator": Composite indicators are based on sub-indicators that have no common meaningful unit of measurement and there is no obvious way of weighting these sub-indicators.

    More info: Composite Indicators Research Group (COIN)

    Keywords: indicator