To start with, let me give a brief introduction of what is meant, in this case at least, by the word ‘Simulation’. More specifically, this refers to computer simulations which, in an easy to understand context, describes a program or system that attempts to replicate a situation in digital form so that it may be studied, amended and ‘tweaked’ to give a desired outcome or prediction.

So, let’s take an example.  A military council debates a force-strike on a location they know to be occupied by hostile forces. By using simulation science, variables may be inputted into the system, such as the strength of the hostile force, their numbers, the terrain of the location, etc. The system will then run a ‘simulation’ of the situation, based on the variables inputted at the start. By doing this, users can see the most likely outcome of the situation. In this case, the military council will be able to see the outcome of the engagement. What casualties, damage, effects etc. the battle will produce. All of this can then be taken into account, before the decision to give the order is made. In essence, the system has allowed the users to predict the future, based on the variables they have provided.

In addition to hypothetical situations, such as the previous example, computer simulations have become increasingly useful in modelling natural systems in science, economics, sociology and engineering. This can be used to gain insight into the operations of various systems. A useful application of this is for educational purposes where formerly long, difficult or dangerous exercises are now being converted into simulations which can present the situation in quick and safe outcomes with easy to understand visualisations.

This practice is becoming more common in the field of medicine, for a variety of different focuses. One example of this is medical training, where instructors are increasingly turning to simulation models to assist in the teachings of their classes. Having explained the symptoms of a variety of different ailments, and the processes required to treat them, an instructor can present a student with a simulation exercise. This would give the student a patient complaining of various symptoms. The student can then use their knowledge from the class to diagnose the patient, and attempt to cure the ailment. However, as the student attempts to do so, the variables of the simulation will change, either for good or bad, depending on how well the student remembered their class. Should the wrong drugs be issued, or an incorrect procedure be conducted, the model can adapt to show the effects of this, on the patient.

This will help students progress a steep learning curve, with a practical hands-on application of what they are learning, in a risk-free environment. As with this example, should a student mis-diagnose the patient and issue potentially life-threatening drugs by mistake, one can just hit the reset button, and try again.

In this field, models can be used to predict anything from the reaction of one chemical with another, to the effects of chemical mixes on people. However, these systems can also be used for a wider scope of use. By accessing a database of chemicals, and their medical properties, a model can retrieve a list of required substances to use in medicines to combat various symptoms, but still assessing the chemicals known reactions to one another, and omitting results where chemicals may conflict. This system can save time, money and energy in medical research by effectively doing the work in a ‘virtual’ environment.

Another application in medical research is a simulation’s ability to model the behaviour and biological structure of a living being. This has particular use in the field of medical testing, where scientists are able to simulate the reaction or effect of new drugs and procedures digitally and accurately and see the outcome in a risk-free environment. This particular use of simulation science has had a lot of interest from animal activist groups, opposed to the laboratory testing of drugs on animals. Their argument in favour of this case is that the technology now exists to replace animals in that situation.

Generally, simulation systems have been created via a mathematical model, “which attempts to find analytical solutions enabling the prediction of the behaviour of the system from a set of parameters and initial conditions.”

Computer simulation is basically creating a “What If?” environment whereby decisions can be made based on the scenarios tested through the simulation. This allows for a shrinkage in the margin of error. In a sensitive - and life or death - field such as medicine, the need for such simulations becomes even more pronounced.

Nowadays, where almost all information is digitally stored and processed, it makes perfect sense to use a simulation to understand events and situations, but more importantly to try to control the outcomes. Instead of dumping facts and statistics onto a spreadsheet - which is still a favourite among many an industry - and have that information reside in a static state, a simulation - through its visual aspect -  brings it to life. Information is no longer stagnant and boring but rather life-like and interactive. Computer simulation is similar to creating a parallel universe where you can step into and have much more freedom in experimenting. You can make mistakes with no consequences so that when you step back into your actual universe, you will be able to recreate the most favourable scenario. It is almost like playing a video game where you can practise to get a high score. It is, by far, the best tool out there to help you gain insight and get things right!