Systems are often difficult to run experiments on. Therefore, it is beneficial to model the systems to learn more about how they behave and so you can experiment with them. The System Modeling Institute uses computer models to learn about systems. Models do not necessarily have to be good at predicting to be useful. One is able to learn about assumptions during model development. In addition, model behavior can demonstrate parameter sensitivity, key phase transitions, and chaotic attractors.
Types of modeling used at the Institute
Agent based modeling
Independent agents interacting with their own rules
Good for modeling interactions, especially if not all agents behave the same
Often see emergent behavior that isn’t obvious from the rules that the agents use
Discrete Event Modeling
Uses Transactions, service times, queues. Time is discrete and the system is modeled in events
Good for performance modeling, finding bottlenecks and transaction based systems
Efficient modeling when there are long periods of time with out events followed by a large amount of events
Dynamic System Modeling
Uses Stocks, Flows and Feedbacks and time is continuous
Good at modeling large systems at a high level
People interacting in virtual worlds
Good for when agent (people) behavior is difficult to predict or quantify
You can create a virtual economy or political system and do things to the system that you wouldn’t want to do in the real world.