When designing systems that are complex, dynamic, and stochastic in nature, simulation is generally recognised as one of the best design support technologies, and a valuable aid in the strategic and tactical decision- making process. A simulation model consists of a set of rules that define how a system changes over time, given its current state. Unlike analytical models, a simulation model is not solved but is run and the changes of system states can be observed at any point in time. This provides an insight into system dynamics rather than just predicting the output of a system based on specific inputs. Simulation is not a decision making tool but a decision support tool, allowing better informed decisions to be made. Due to the complexity of the real world, a simulation model can only be an approximation of the target system. The essence of the art of simulation modelling is abstraction and simplification. Only those characteristics that are important for the study and analysis of the target system should be included in the simulation model.
Key Terms in this Chapter
System Dynamics: System dynamics is a top-down approach for modelling system changes over time. Key state variables that define the behaviour of the system have to be identified and these are then related to each other through coupled, differential equations.
Agent-Based Simulation: Agent-based simulation is a bottom-up approach for modelling system changes over time. In an agent-based simulation model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment.
Emergent Behaviour: Emergent behaviour refers to the way complex systems and patterns of behaviour develop out of a multiplicity of relatively simple interactions among agents and between agents and their environment over a certain period of time.
Artificial White-Room: Artificial white-room is a simulation of a laboratory as it is used by social scientists for data gathering under controlled conditions.
Discrete Event Simulation: Discrete event simulation is modelling a system as a set of entities being processed and evolving over time according to availability of resources and the triggering of events. The simulator maintains a queue of events sorted by the simulated time they should occur.