When most people think of simulation, they might think of an expensive pod-like thing, mounted on a few pneumatic rams and filled with computer displays and joysticks – a flight simulator. These are generally used to train pilots to fly an aircraft safely. Modern aircrafts owe a lot to these simulators; pilots are now trained in nearly every adverse event that might occur, including dangerous scenarios that might be difficult to simulate in an actual airplane. Another use is reconstructing adverse events after they have occurred – many air disasters have been recreated in a simulator and have been fully understood as a result.
Simulation is now used far more widely in training across a range of contexts. Brain surgeons have moved from using cadavers to practice operations; now they combine MRI scans of their patients with haptic feedback devices, planning and perfecting the procedure before conducting it on a patient. There are a wide range of simulators buried in the bowels of Nottingham’s engineering department, including a driving simulator, Europe’s first motorbike simulator and a train simulator. These are used for a wide range of experiments that aim to understand human performance, as well as training people prior to getting on the road (or rail).
Science at large has also begun to reap the benefits of simulation. Perhaps the most pervasive example of this is ‘Complex Systems Modelling’, where a computational model is used to simulate anything from cancer tumours to animal social networks. ‘Agent- based modelling’ has led to advances in topics ranging from psychology and neuroscience to physics and chemistry. As computers get more powerful and our knowledge of the relevant algorithms advances, how humans react to these simulators will allow us to run simulations that could revolutionise science and training. Ultimately, the advances will mean that simulation is recognised as a part of everything we do.