Chapter 5 discusses decision making using system modeling. The author briefly mentions an open source software tool, EMA Workbench, that can perform EMA and ESDMA modeling. Find EMA Workbench online and go to their main website (not the GitHub download site). Then do the following:
1) Under documentation, go to the Tutorials page.
2) Read through the Simple Model (in your chosen environment), and the Mexican Flu example.
3) Decide how you could use this software to create a model to help in developing a policy for a Smart City.
Starting from the present day and current evolutions in the area of device dynamics modeling and simulation, this chapter sketches a doable near term future of the broader discipline of systems modeling and simulation. In the near time period future, special systems modeling schools are predicted to further combine and speed up the adoption of techniques and strategies from associated fields like coverage analysis, data science, computing device learning, and laptop science. The ensuing future country of the artwork of the modeling subject is illustrated by using three recent pilot projects. Each of these tasks required similarly integration of specific modeling and simulation procedures and related disciplines as discussed in this chapter. These examples also illustrate which gaps need to be crammed in order to meet the expectations of real decision makers facing complicated unsure issues (Puliafito, 2019).
Future State of Practice of Systems Modeling and Simulation
These latest evolutions in modeling and simulation together with the current explosive increase in computational power, data, social media, and different evolutions in pc science might also herald the beginning of a new wave of innovation and adoption, transferring the modeling and simulation field from constructing a single model to simultaneously simulating more than one models and uncertainties; from single approach to multi-method and hybrid modeling and simulation; from modeling and simulation with sparse facts to modeling and simulation with (near real-time) big data from simulating and inspecting a few simulation runs to simulating and simultaneously analyzing well selected ensembles of runs; from the use of models for intuitive policy trying out to using fashions as units for designing adaptive strong policies; and from growing educational flight simulators to wholly integrated decision support.
For every of the modeling schools, additional diversifications should be foreseen too. In case of SD, it might also for instance involve a shift from developing in simple terms endogenous to generally endogenous models; from totally aggregated models to sufficiently spatially specific and heterogeneous models; from qualitative participatory modeling to quantitative participatory simulation; and from the use of SD to combining hassle structuring and policy evaluation tools, modeling and simulation, laptop learning techniques, and robust optimization (Thorn, 1999).