Modelling

A model. We all have heard that word; moreover nearly all of us have actually used models in school, at home and generally in life to solve certain problems. A model simplifies the situation it is applied to by removing all the “junk” from it. And even though simplification can sometimes stand in the way by producing certain limitations, I think that modeling as an absolutely necessary tool in acquiring the knowledge. Out of all the areas of knowledge, models are barely used in such areas as ethics, history and arts, with the exception of those who pose for an artist. It is most widely used in sciences and mathematics and as I take physics at higher level, where we deal a lot with models during the course, I will use my experience to argue whether a model helps or distracts us from acquiring knowledge. Even though in our lives the word “model” can be applied to many different things, such as business model, or the car model, or the science model, they all virtually mean the same. According to William Mueller, “A completed model may share many of the essential characteristics of the object it models. It may look similar, and it may move in similar ways. The most detailed models allow us to imagine how the modeled object itself might behave.” However that forces a model to have one disadvantage: it will always have limitations. Since it is not the real thing, but “almost” one there will always be an extend, to which it will represent the reality, or a certain object in the real life.



This is an example of a scientific model related to climate.

How do we tackle problems in science? First of all we gather the information through different ways of knowing, mostly through reason perception and language, because science tends to exclude the emotion. Having done the information gathering, we need to apply that information to the real life situation in order to produce some results, which would let us draw a conclusion. The major difficulty is that in the real life there are way to many uncontrolled variables; so many that any attempts to control or take into account all of them is already doomed to failure. Lets take a bouncing tennis ball as an example. We have a task to find out it rebound factor. At the first glance it seems easy to take the readings, which would be hundred percent applicable to any real life situation: take the ball, drop it at different heights and measure the rebound height and then find out the relationship by plotting a graph. As we look deeper nothing seems as easy anymore. To be absolutely exact we need to take into account absolutely everything. The person performing the drop cannot be act exactly the same in every way each drop, the height could vary slightly, each time the ball might have slightly different initial acceleration. The ball spin during the fall and nap on the balls surface cause changing air resistance as the ball falls. During the interaction with the ground, the ball compression will not be identical for all the drops due to the manufacture flaws on micro level for any ball. As we can see it is impossible to take the full control of every one of those factors. Therefore the model is much quicker, simpler and easier way to go. Yes, it is not ultimately perfect, but the limitations are reasonable and do not, if carefully considered, alter the course and the results of the test. If for all the past centuries the primary goal of science was 100% accuracy in any experiment or investigation, our lifestyle probably would not differ much from the one in stone age by now. Now how does science come to new discoveries, or generally takes a leap forward? Firstly someone makes an assumption, and developing and finalizing it further creates a scientific theory. No matter how realistic or unrealistic the theory will be, it would have to be tested. Unfortunately nearly all the time there are too many things to consider for the real life testing, or the real life conditions are impossible to recreate in the controlled lab environment. Therefore the only way to perform the testing is to use the scientific or mathematical model, describing the environment as closely as possible, ignoring the insignificant bits. Yet some people might argue that this is not enough for them, and they need a solid proof, not a simulation. Yes indeed, this is the nature of humans; for us to be completely sure in something we need to see it with our eyes most of the times. But unfortunately due to some factors it is virtually impossible to check some theories in practice. For example the latest String Theory operates on such small scales that are impossible to zoom in to, due to certain properties of light. Therefore in this case our only way to keep moving forward is to operate with models, which means pretty much putting the numbers in the mathematical equation and looking whether the left hand side is equal to the right hand side. So far the model seems to be a really good way of acquiring knowledge, making it quicker, easier and more convenient, with one main disadvantage however. Oversimplification leads to unreliability and hinders our search for knowledge by sending us wonder in a different direction after receiving uncertain or wrong information. Sometimes, where we don’t have enough information and we need to find out something, researchers, and especially students might neglect something that should not be neglected when creating a particular model. It is like saying “Oh, well, we don’t know what the air resistance is, so lets say it doesn’t play a role here” when it is actually crucial to achieve correct results and draw a correct conclusion, which would correspond to real situations. Oversimplification seems to be the main flaw of the mathematical and scientific modeling, but if in the case of unknown air resistance, it can be measured, in some situations it is impossible with our current technology to obtain certain information. This way ignoring a certain variable would indeed hinder the research, therefore the only relatively harmless way is to create a model with some unavailable pieces of information missing and filling them in as we find necessary information with our scientific developments. Alternatively another way is to create another model and take a different approach. All in all models play a great deal in our search for the knowledge, by leaving out only the relevant bits of information for considering, and making it easier to solve a simplified problem or situation, because the extra complexity does not stand in the way anymore. However whenever using models one should consider its limitation in a real life situation, that the simplification causes the model to differ from reality, otherwise if one does not consider that, it might just slow down the quest for knowledge, instead of making it easier. When used carefully the modeling ultimately boosts the research, and even though there are some situations where it is no help, but it doesn’t hinder our search for knowledge this way – it just encourages to try a different approach to the problem.