Simulation and Model-Based Methodologies: An Integrative View

Simulation like a gem is multi-faceted. Several subfields of simulation have emerged based on the characteristics of models used in a simulation study on the nature and the generation characteristics of model behavior also on the agent soch as a computer which generates model behavior. For example one distinguishes: - deterministic simulation stochastic simulation stiff simulation based on functional rela- tionships of descriptive vuriables of models used; - combined simulation continuous simulation discrete simulation process simulation dis- crete event simulation activity-scanning simulation based on characteristics of descrip- tive variables of modelS; variable topology simulation soch as moving boundary simulation cellular simulation and fixed topology simulation soch as boundary-value simulation and network simulation (network flow simulation Petri-net simulation bond-graph simulation) based on spatial distribution of models; - simulation with fixed organization models (soch as simulation with hierarchical models) and simulation with variable organization models (i.e. autopoietic simulation) soch as metamorphic simulation simulation with self-organizating models simulation with self- learning models evolutionary simulation based on orgCllization of component models; - state-maintaining simulation behaviorally adaptive simulation goal-seeking simulation purposive simulation purposeful simulation ideal-seeking simulation based on goal(s) to be pursued by the model; - trajectory simulation stroctural simulation real-time simulation predictive simulation prescriptive simulation intermittent simulation (such as regenerative simulation opti- mizing simulation gaming simulation conferencing simulation on-line simulation) based on nature md generation characteristics of model behavior; and - simulators such as aircraft simulator earthquake simulator where physical analog can be