2.1.6: Output Analysis and Knowledge Synthesis

Deal with the issue of output analysis of a policy model and, at the same time, of feedback analysis in order to incrementally increase and synthesise the knowledge of the system.

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Inputs driving a simulation are often random variables. For example, in a simulation of a manufacturing system, the processing times required at a station may have random variations or the arrival times of new tasks may not be known in advance. In a bank, customers arrive at random times and the amount of time spent at the counter is not known beforehand. In financial simulations, future returns are unknown. Because of the randomness in the components driving simulations, the output from a simulation is also random, so statistical techniques must be used to analyse the results. However, output is obviously related with the input, according to the assumption that it is basically the structure of a system to drive its behavior. In particular, the output processes are often non- stationary and auto- correlated and classical statistical techniques based on independent identically distributed observations are not directly applicable. In addition, by observing a simulation output, it is possible to infer the general structure of a system, so ultimately gaining insights on that system and being able to synthesise knowledge on it. There is also the possibility to review the initial assumptions by observing the outcome and by comparing it to the expected response of a system, i.e. performing a modelling feedback on the initial model. Finally, one of the most important uses of simulation output analysis is the comparison of competing systems or alternative system configurations. Visualisation tools are essentials for the correct execution of this iterative step. The present research challenge deals with the issue of output analysis of a policy model and, at the same time, of feedback analysis in order to incrementally increase and synthesise the knowledge of the system.

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