Output Analysis and Knowledge Synthesis Sample Clauses

Output Analysis and Knowledge Synthesis. Introduction and definition Inputs driving a simulation are often random variables, and because of this randomness in the components driving simulations, the output from a simulation is also random, so statistical techniques must be used to analyse the results. 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. Why it matters in governance Output analysis is a specific aspect of simulation. According to the general need for policy assessment and evaluation, there are some specific issues stemming from the output analysis, which are strongly related to governance: Acceleration of policy assessment process: automated output analysis tools would help policy makers to efficiently and effectively analyse the impacts of a policy even if the large number of simulation data must be taken into account Citizen engagement: user-friendly automated tools for output analysis can be offered to citizens in order to share the simulation results and better engage them in policy-making process. Current Practice and Inspiring cases In the current practice a large amount of time and financial resources are spent on model development and programming, but little effort is allocated to analyse the simulation output data in an appropriate manner. As a matter of fact, a very common way of operating is to make a single simulation of somewhat arbitrary length run and then treat the resulting simulation estimates as being the "true" characteristics of the model. Since random samples from probability d...