Chaos Simulation is a term given to a specific area of computer simulations, which focuses primarily on the creation and presentation of theoretical situations and scenarios. However, what makes Chaos simulations stand apart from all other computer simulation fields, is the vast scope of variables that are taken into account.

In a recent interview with a simulations developer who primarily worked on chaos simulations, they explained the difference between regular computer simulations - whose variables take into account random events and chaos simulations - whose variables theoretically take into account every possible event. “Randomness is like picking a number between one and ten. There is a definite amount of possibility. Chaos is like picking a number between one and ten, however it can include 1.1, 1.11, 1.111 and so on – an indefinite amount of solutions can happen.”

You would think that such a glitch is negligible in the larger scheme of things but the devil is in the details. The developer continued to explain, “Where chaos simulation becomes useful is when you begin to see little things that you wouldn’t think would have made a big difference in the outcome of a situation. But when played over and over and over again, it begins to stick out.” In chaos theory, this is termed the Butterfly Effect where there is an interdependence of initial conditions that influences the outcome.

Figure 1: Screen shot from simulation

These ‘little things’ will vary from situation to situation, but can include factors from how much petrol a car had in the tank, before leaving, down to the current wind speed at the location. By incorporating this simulation system with active artificial intelligence outputs, one can easily create a real-time strategy platform with amazingly detailed variables for almost any situation imaginable.

Jumping from theoretical scenarios, to situation forecasting, simulations are currently being developed that, by using satellite imagery, remote sensing and climate databases, are able to predict the areas at risk from situations such as flooding, or similar disaster scenarios.

These systems would simulate rain density and water flows, to estimate various areas that could face damage from the disaster. One major use of this program is set to be focussed on helping to reduce or eliminate false or excessive damage claims to insurance companies. Similarly, it is set to assist authorities evaluate damage more efficiently.

Should such a system be integrated into the wealth of programs currently used by such authorities, we can expect to see much more accurate forecasting and as a result, better preparedness to situations such as flooding. However, the next hurdle in the ever winding path to development for this system would be collecting the information from the various departments involved and compiling it all into an accessible database. Not an easy task, by any means.