Simulation in Information Systems Research

INTRODUCTION Simulation is a standard research technique of the natural sciences, social sciences, and engineering disciplines. The paradigm of simulation provides an accepted mode of development, validation, and verification by which complex , highly dynamic interactions can be probed and analyzed. The approach enables researchers to phrase experiments in a controlled environment where the concepts , variables, and relationships of the domain can be manipulated. Then, employing standard experimental design techniques, the simulation represents the behavior of the underlying system. The behavior can be analyzed statistically for its regularity and critical values where the dynamics may become unstable. Further, once the simulation model is developed, sensitivity analysis can be done to probe the interdependencies of the elements of the underlying system upon one another. Without the use of computers, the concept of simulation uses physical models to provide the experimental framework examining complex systems. These physical models incorporate dimensions of a real system, abstracted into the model. If one wants to explore a pilot's ability to manage a cockpit's physical controls under different degrees of information loading, one constructs a mock cockpit and offers the pilot different amounts of information to handle. The experiment of the physical simulation relies on the modeler validly abstracting relevant aspects of the real cockpit into the mock cockpit. Computer-realizable simulation has become an option with the development of computers and the representation of models within a computing environment. Programming known mathematical representations of physical behaviors became a standard use of computers with programming languages such as FORTRAN, designed to numerically analyze mathematical functions within acceptable tolerances. In fact, the disciplines of management science and operations research are defined by their various modeling approaches to represent the behavior of physical systems into mathematical and computational frameworks-simulation being one of the frameworks. In fact, the initiation of these fields are usually traced to their successes in the 1940s with military-related research of dynamic systems using computers and simulation. Today, simulation continues to be a tool for understanding complex, dynamic systems—but not for proposing optimal solutions to their interactions. Optimization cannot be a mathematical analysis goal of simulation; other mathematical approaches exist for this analysis goal. Typically, the dynamics and interactions of a simulated system are too complex to be captured into a mathematical structure, lending itself to optimization algorithms. Simulation provides a methodical way to describe and to examine the dynamic space of the system's behavior. …