When a problem is made of many independent, diverse and interconnected parts, itâ€™s hard to grasp it in our head. The simple solution is to depict the problem, so people can grasp the problem (and sometimes the solution) from the visualization. But, how do we know that the model depicts reality?

If the model is not true or close to reality, the amazing looking PowerPoint or Visio is just a twisted depiction of reality and therefore can lead to a wrong conclusion. Models can be seen as completely different by people, especially if they are using different points of reference. People might look at the model and agree with it, but each one sees the problem, and the solution completely different.

One way to address this issue is by using simulation. Simulation enables to put number and logic into the model and see how the model behaves over time. Simulation can validate the model. As long as the simulation numbers are not at least 75% as the numbers we see in reality, the model has not depicted reality.

Itâ€™s not just the model is wrong, itâ€™s also common that the data that the simulation is using is not real data. Itâ€™s crucial when running a quantitive simulation to make sure that the data we are using is real data taken from systems (preferred) or at least from several interviews with people.

Once a quantitative simulation produces results close to reality, it can be used to validate and assess different proposed solutions. To use a model and simulation for assessing new solutions, the model logic is the only one that can be a change. Any change to the data input to the model makes the quantitative simulation a worthless tool for assessment.

A quantitive simulation also makes models more accurate and easy to understand. Running numbers as simulation enforce dealing with issues that wonâ€™t surface without simulating with numbers. The complexity makes it hard to think about all the details. The numbers a quantitative model produces enforce us to think deeper and produce better models.

The depth of thinking required by quantitative simulation enforces thinking and dealing with all the intricacies of complexity. Quantitative simulations wonâ€™t ever reduce complexity, but their result is a better understanding of the system and its limitations, which decrease uncertainty in organizations.

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