Skip to Main Content
Simulation-based decision support is an important tool in business, science, engineering, and many other areas. Although traditional simulation analysis can be used to generate and test possible plans, it suffers from a long cycle time for model update, analysis and verification. It is thus very difficult to carry out prompt "what-if' analysis to respond to abrupt changes in the physical systems being modeled. Symbiotic simulation has been proposed as a way of solving this problem by having the simulation system and the physical system interact in a mutually beneficial manner. The simulation system benefits from real-time input data which is used to adapt the model and the physical system benefits from the optimized performance that is obtained from the analysis of simulation results. This talk will present a classification of symbiotic simulation systems with examples of applications from the literature. An analysis of these applications reveals some common aspects and issues that are important for symbiotic simulation systems. From this analysis, we have specified an agent-based generic framework for symbiotic simulation. We show that it is possible to identify a few basic functionalities that can be provided by corresponding agents in our framework. These can then be composed together by a specific workflow to form a particular symbiotic simulation system. Finally, the talk will discuss the use of symbiotic simulation as a decision support tool in understanding and steering complex adaptive systems. Some examples of current applications being developed at Nanyang Technological University will be described.