Introduction
Although you may not know it, models are used everywhere we go every day to assist in the search for knowledge. What makes models different from the aspects they represent, however, is that they are created with the human element. By being created in this way, models are susceptible to human perception of the aspect which they are attempting to represent. As a result, models can become entirely different from the aspects they imitate due to this human perception.
In addition, since models are simplified, they are generally made up of less matter, and, combined with human perception, much detail of the aspect they imitate can be lost. Through losing these details, the search for knowledge and truth of the aspect is greatly hindered. Despite this, models have been the keys to the search for knowledge in many aspects of the world and human history, but have also shadowed the truth of some of these aspects.
Human perception is relevant to our “worlds.” Our perception creates familiarity and sees the world through a specific, unique lens. By creating a model of something we perceive in the world, we recreate the simplified version of the aspect through our perception. Without the objectiveness of the aspect, the search for knowledge based on the model is lost, due to the change in perception on the aspect. For example, by taking a model of macroeconomics such as the Keynesian model, the perception of the aspect will influence the production of the model, ultimately changing it. When the model is changed, the search for knowledge and truth about the aspect it represents is hindered because they are no longer accurately representative of each other.
Models Effect in the Natural Sciences
Although the natural sciences depend heavily on the effect of models, users often find themselves in difficult circumstances when employing them. For instance, if a model is made too complicated, then its applicability is hindered and the acquisition of essential information is therefore hindered. Conversely, when the model is too simple, then chances are that very many assumptions will have been made and this undermines the value of the model in the first place.
Scientists must therefore strike the right balance to avoid problems that emanate from both excesses. However, not all scientists can achieve this, and the capacity of the model to propagate the pursuance of knowledge can therefore be dramatically impeded when other experts disapprove of that theory. For instance, in understanding the universe, analysts proposed the big bang model. However, other astrologists have asserted that the model is too simplistic and that it cannot be applied to such a complex process. There are several controversial issues to this model that have never really been resolved and the same may be said for several models in the natural sciences. The applicability of such models, therefore, makes them highly restricting. (Harris, 77)
For a model to be truly effective in pursuance of knowledge then it must possess a high degree of accuracy concerning the phenomenon of interest. However, in the natural sciences, numerous models have been postulated concerning particular concepts. Other models have been discarded while others have been retained and although many explanations exist for this occurrence; the most common one is that prevailing scientific knowledge at the time of formulation of a model could not allow for accurate explanations. The problem with most models is that they are limited to the extent of scientific knowledge prevailing at that time.
To this end, their value diminishes substantially once advances are made and this hinders the search for knowledge. A classic case is an atom and all theories used to describe it. The first model was propagated by 5th-century scientists – Democritus and Leucippus who used deductions to explain that everything in the universe is composed of atoms as their smallest units. In the seventeenth century, Thompson proposed his sphere model which suggested that there were other smaller particles in the atom called electrons and protons with the latter found in the entire atomic volume.
However, this model was abandoned after experiments illustrated that most protons were not evenly distributed in the atom thus paving the way for the Rutherford model in 1911. However, the Bohr atom quickly came into being in 1913 after linkages were made concerning electromagnetic radiation such as light and movement of particles within the atom. In 1915, inefficiencies in Bohr’s model were compensated by the Parson Magento model which proposed an orbiting electron; it answered questions to most of the problems in other models. However, Debroglie came up with his model in 1924 to overcome inefficiencies in the latter.
The pattern went on and on with the Schrodinger model, Dirac’s model, Standard elementary particles model, and the Luca model in 1996. The latter model is accepted by chemists and physicists as the most reliable since it can predict electron numbers of articles on the periodic table. At current levels of scientific knowledge, the studies carried by Lucas are seemingly correct, however, one may not know what the future has in store especially because scientific progress can be unpredictable. This confinement of models to existing knowledge rather than furtherance of new knowledge is quite limiting and therefore hinders the knowledge acquirement process. (CSS, 2, 3, 5, 12)
Sometimes one phenomenon may attract several models. For instance, to demystify protein synthesis, physical models have been used to illustrate the location. Mathematical models have been applied to calculate the rate of synthesis while computer simulation models have also been carried out. Although many affirm that the existence of so many explanations is necessary to explain the different components of that phenomenon, one cannot ignore the fact that some controversies can be generated through these models.
If a certain system is being explained then there should be some way of forging a relationship between those models i.e. making a deductive relation. The lack of any systematic relations between the models, therefore, leaves stakeholders with a patchwork of ideas that only retains applicability in certain domains. This severely hampers the pursuance and acquisition of knowledge.
The realist school of thought claims that for a method to accurately predict its respective phenomenon then it must contain at least an approximate degree of truth to it. However, using the same argument, if several methods (Theories or models) have relatively high predictive rates but are found to be highly inconsistent then it can be argued that not all those models are true or even partly true.
If one takes the realism perspective and argues that truth should be the major goal for any scientific endeavor then chances are that models cannot be reliable avenues for the pursuance of knowledge. Some explanations on the atom such as the Bohr model (which was later nullified) are still applied in certain circumstances today yet many agree that it is not accurate. In this regard, if knowledge is composed of truth, then models do very little to bring about that knowledge.
Fictional models sometimes create more problems than they can solve and this may result in conflicting conclusions on the matter under analysis. To understand this argument, it is necessary to familiarize oneself with the reasoning behind the use of models. Models are used to understand the world around us. However, one does not gain insight into one’s surroundings by just looking at the model; one must be able to manipulate it and apply it.
Manipulations are often carried out by thought experiments but the manner of performing those experiments may be radically different since the assumptions behind them are also divergent. In the end, actual gaols of research are rarely met and the pursuance of knowledge is dramatically impeded. For example, the Bohr model of the atom was based upon the existence of a fictional object; although significant insights on the working of the atom were made through this model, one cannot ignore the fact that fictional models were used to get to the actual results and this may be different from what goes in reality even when the prediction of behavior is true.
Furthermore, the use of models brings in the immense task of converting knowledge about the model to knowledge about the actual concept or phenomenon under study. Models are only applicable upon the assumption that there are certain commonalities between that model and the real world. Once there have been idealizations, then the model cannot be a true depiction of reality because all assumptions no longer hold in the real world.
To some point, models become limiting because they may do a good job of explaining how the representation works but not the actual object. For instance, when studying circulatory motion and hence attractions of planetary bodies to one another, Newton created a model in which it was assumed that all the bodies are perfect spheres. However, those assumptions do not exist and the transference of this model to the actual system is deeply impaired. (French & Da Costa, 116)
The level of abstraction that a certain model possesses can also tremendously influence that model’s applicability, especially when compared to their more direct representations. For instance, the model of the solar system has been scaled down through its respective model and is not very abstract. However, there are certain circumstances in which a representation can be quite abstract thus necessitating computer simulation. Computer simulation has provided new possibilities for the process of research because it provides a platform against which new insights can be made for concepts that cannot use standards methods.
The major challenge with computer simulations is that it is highly dependent on methodology yet the methodology followed may be suspect. For example, some calculations done by a computer may be such that they inculcate only part of the parameter space and the whole system’s features may therefore not be revealed. Furthermore, the use of computer simulation may also encourage several scientists to rush to the use of models that have not yet been fully developed.
They may also incorporate a range of unnecessary and complex parameters merely because manipulations can be done digitally. This prevents persons in the scientific arena from fully understanding the mechanisms that cause those observations and thereby impeding an important aspect in knowledge acquisition which is to demystify how different systems work. (Winsberg, 442)
Conclusion
The use of models hinders the acquisition of knowledge in the natural sciences and by extension in other fields that use research because striking a balance between a very simple model and a complicated one can be tricky to concerned scientists. Furthermore, those models are restricted by the level of scientific progress and can be nullified at any time; this brings into question their true ability to increase knowledge. Models can sometimes be contradictory when several of them are used to explain the same phenomenon as stakeholders may not be sure about which model to choose.
Matters are further worsened by the application of the realist school of thought which defines knowledge based on truth yet certain models are still used today when they are unrealistic and untrue. Also, abstraction in models creates the need for computer simulation which may mystify the processes required to get to those results. Lastly, the representational nature of models brings serious problems in the transference of the insights obtained from the models to reality and this dramatically impedes their contribution to knowledge.
Works Cited
French, Steven & Da Costa, Newton. “Models, structures and theories, 30 years on.” Journal of philosophy of science, 67(2000): 116.
Harris, Todd. “Data models & acquisition of data.” Journal of philosophy of science. 70(2003): 77.
Winsberg, Eric. “Models, simulations and theories.” Philosophy of science 68(2001), 442.
CSS. Historical models of the atom. CSS 2007. Web.