Guide to Advanced Empirical


Quantitative Versus Qualitative Simulation



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2008-Guide to Advanced Empirical Software Engineering
3299771.3299772, BF01324126
4.4. Quantitative Versus Qualitative Simulation
Quantitative simulation requires that the values of model parameters are specified as real or integer numbers. Hence, a major prerequisite of quantitative simulation is either the availability of empirical data of sufficient quality and quantity or the availability of experts that are willing to make quantitative estimates of model parameters. Often, the quantitative modelling approach is costly and time-consuming and might not be appropriate for simulations that aim at delivering simple trend


5. Simulation Methods analyses. Qualitative simulation is a useful approach if the goal is to understand general behaviour patterns of dynamic systems, or when conclusions must be drawn from insufficient data.
QUAF (Qualitative Analysis of Causal Feedback) is a qualitative simulation technique for continuous process systems (Rose and Kramer, 1991). The method requires no numerical information beyond the signs and relative values of certain groups of model parameters. QSIM (Qualitative SIMulation) is another well- established qualitative technique for continuous simulation (Kuipers, 1986). Instead of quantifying the parameters of the differential equations underlying the continuous simulation model, it is only required to specify the polarity (i.e., positive or negative) of model functions, indicating whether they represent an increase or decrease of a quantity over time.
In the case of event-driven simulation, for example, Petri-net based and rule- based simulation can be conducted purely qualitatively, if events (e.g., the activation of transitions in Petri-nets, or the execution of a rule in rule-based systems) are triggered exclusively based on the evaluation of non-quantitative conditions.
4.5. Hybrid Simulation
Dynamic simulation models that combine continuous with event-driven or deterministic with stochastic elements are called hybrid simulation models. One benefit of hybrid approaches is the possibility to combine the advantages of stochastic, continuous and event-driven models. In the case of hybrid models that combine continuous and event-driven simulation, however, the drawback is increased model complexity. An example of a hybrid simulation model that combines continuous with event-driven simulation can be found in (Martin and Raffo, 2001).

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