The nature is never following the rules of our models

I have studied electronic engineering at University. All the classes and subjects were deeply focused in studying and creating mathematical models representing the world and the nature around us. Every natural phenomenon in general can be modeled with a mathematical function. The function used can be very simple (linear approximations) or very complex (n-grade functions), can be real or complex, with analytic solutions or numeric solutions and so on. In any case we use these models in order to understand better the reality and to predict the future or the outcome of a system when applied a known stimulus.

When we make a project we rely on this knowledge in order to predict the behaviour of the system or the device that we are going to make. Especially in electronic engineering we are plenty of models, simulations and calculations that we must take in consideration even before doing any kind of test or qualification.

Sometimes we are disappointed because the results of our projects are not exactly as we expect them to be. Probably in 90% of the cases it is because we made a mistake. But in some other cases we have to consider that our projects are based on models and not always that models are perfect or accurate. In most of the cases these models are just a simplification of what it is the real world.

I remember my professor when made an example speaking about the property of materials – “if you find a strange materials which doesn’t follow the ohm’s law you can’t say that the material is not following the law, it’s ohm’s law that is not adequate. The nature is never following the rules of our models!”

Our models are just a simplification of the complexity of the nature. The nature is more complex, the world is complex. It is our responsibility to take this into account when we make a new project, a new design or when we implement a new idea. If the result of our project is not exactly as expect to be, then we have to broaden our perspective and consider that the model used is not enough adequate for our problem or even that the model that we really need is a combination of different knowledges and perspectives which could fit better for the solution we are searching for.