My IB TOK Essay Got Full Marks (Real Full Score Example)

Preface

Recently, while browsing through old folders, I found the TOK essay I subitted during my IB Diploma. Since I like to keep a clean history of my past work, I decided to share it here.

The essay's topic was related to models. Little did I know back then that a decade later I'd be writing my Ph.D. in the area of model theory in Finance.

I know there are IB students writing their TOK essays who scour the internet for examples. So I decided to share mine, I hope it helps.

The Essay

A model is a simplified representation of some aspect of the world. In what ways may models help or hinder the search for knowledge?

Stanley Seidner once prominently asserted that ‘Striving to find a meaning in one's life is the primary, most powerful motivating and driving force in humans’¹. It is an irrefutable human characteristic that mankind seeks to understand and aims to justify certain aspects of our world. In many areas of knowledge where humans desire to find a meaning models occupy a key role.

When referring to the search for knowledge, a model can be seen as a medium through which knowledge may be conveyed. The danger with models is that they impose their own limits on what they attempt to represent, and anything that is not covered by the model’s terms is often ignored, until a new paradigm shifting model comes along and re-defines the limits of the previous model. So, a model may inevitably simplify in order to clarify, and this clarification can be very useful in pragmatic terms of making sense of the world. However, we have to learn to view models in a contingent way and not be shocked by attempts to re-define how we model reality. This essay aims to explore this process in relation to several areas of knowledge.

Theoretical physicists Lawrence Krauss once emphasized that ‘uncertainty is a central component of what makes science successful’² . Hence finding knowledge in science greatly relies on not being certain. This interesting assertion can be supported when looking at a model such as the atom. J. Thomson was the first scientist to construct a model of the atom, his ‘plum pudding model’ described the atom as a mixture of positive and negative charges suspended in a fixed space. This model was improved by E. Rutherford’s ‘nuclear model’ which was then further improved by N. Bohr, which was yet again improved by E. Schrödinger to the ‘electron cloud model’ which is widely accepted nowadays. On the one hand this process of doubting and reconstructing models has given rise to models emerging that account for more findings, which we therefore interpret to be more accurate or correct. Since models like the atom continuously improve, we may be on the right path to finding a greater knowledge, as long as we recognize that our models are not final, and are steps on the way to further knowledge.

In the case of the atom the numerous models helped us in the search for knowledge, by providing a foundation on which new ideas are based possibly bringing us closer to finding what is true.

Evidently not all models may guarantee the attainment of a greater knowledge. It is interesting to compare the influence of a successful and a less successful model on the search for knowledge. For example, during the Biology course a few years ago, we looked at two contesting models of evolution. The first model was from J.B. Lamarck, a French biologist who was best known for his ‘use and disuse’ model which was based on his theory of ‘Inheritance of Acquired Characteristics’. He subscribed to the idea of organisms changing during their life and passing on these characteristics to their offspring. The second model we discussed was Darwin’s model of ‘Natural Selection’ which was based on his ‘Theory of Evolution’ with its idea that offspring is born with the parents’ helpful traits: these animals will thus survive to reproduce whilst the less well adapted animals die.

In this conflict between the two opposing models technological advance in genetics proved Darwin correct. The people who adhered to Lamarckism were literally misled by an erroneous model. To some extent we could argue that people who limited themselves to Lamarckism were hindered in their search for knowledge since they were led to believe something wrong. On the other side, a wrong model may indirectly lead to greater knowledge by inspiring ones that are correct. In the case above it was Darwin who effectively doubted Lamarckism. As seen here, opposing ideas that emerge in science may be argued to fuel modern day science in the search for what is actually right. What is even more fascinating is the rise of ‘neo-Lamarckism’ or epigenetics, which is a recent revision of Darwinism that attempts to come to terms with the probability that we can in fact change genetically through environmental reasons and pass these characteristics on to our offspring, which means that the Darwinian model has to undergo some revision again.

As I began researching the title of this essay I came across a news article about simulating a Mars mission on Earth. A model capsule was constructed in the European Space Agency in Moscow in order to test the psychological impact of isolation and confinement. This is the use of a model in the sense of a helpful imitation of the real thing constructed to simulate conditions in order to help us gain useful knowledge. The article argued that this model is ‘a lot cheaper than flying people in space and it’s much safer too’³. On the other hand ‘real dangers can’t be simulated [as well as the conditions in space]’⁴. I understood that the applicability of models often have many limitations. For instance if somebody were to become ill, for example, they could be taken to hospital; in space this would be impossible. More significantly, the model can only be based on what we know of space, and it cannot generate the ‘unknowables’ that will only be present in real space.

As seen from the models discussed above, all models hold different implications. Among the most interesting is that some models possess the ambiguous quality of being helpful and hindering in the search for knowledge under different circumstances. Recently in Physics, when discussing thermodynamics, we used formulas to explain the macroscopic behavior of a gas by assuming that the gas obeyed the model of an ideal gas. The model of the ideal gas is essentially a series of assumptions that keep the calculations simple. For the I.B. Physics course these assumptions may be more than sufficient for us to grasp the concept of thermodynamics. However for an engineer constructing an engine these assumptions will give answers that simply do not apply due to their inaccuracy.

Therefore the applicability of models depends on the assumptions and simplifications that are made. To what extent they therefore help or hinder in finding what is being searched for greatly depends on the knower in this case, the student or the engineer.

Among the human sciences, the models that are used in geography are the ones that I am most familiar with. Whilst looking at populations in transition throughout our Geography course, we encountered the DTM (Demographic Transition Model) which models the population transition in terms of the birth and death rate as the country develops from a pre-industrial economy to an industrialized economy. When comparing the demography of separate countries with the DTM one can say that there are usually several similarities. This model will undoubtedly help to understand a pattern which conveys a general trend and might help demographers to make future projections of how the population might change.

Nonetheless this model will inevitably fail to explain reality. China’s demography epitomizes this flaw. During the ‘Great Leap Forward’ in China, the country experienced an exceptionally high birth rate, a demographic anomaly that can, however, not be depicted on the DTM since it simply wouldn’t apply to the general demographical pattern of other countries which the DTM tries to show.

Moreover the DTM illustrates the change from pre-industrialized to industrialized perfectly well for a simple model. However the countries that are experiencing this change from pre-industrialized to industrialized today definitely undergo a different pattern to the countries that experienced this change many years ago. The reasons for this different pattern that could occur today could be factors like international aid, millennium development goals and greater availability to global markets which are all factors that are not accounted for in the current DTM.

The property of models usually only being applicable to certain periods of time or within certain boundaries greatly impairs their ability to help in the search for knowledge as well as to depict reality.

Galileo Galilei, an innovator in vast areas of science and philosophy, dedicated his life to the truth. He once famously said that ‘In questions of science, the authority of a thousand is not worth the humble reasoning of a single individual’⁵. Throughout the 17th century the universally accepted paradigm manifested in the Catholic scriptures asserted that earth is stationary. Nonetheless by means of scientific evidence Galileo tried to show that the opposite was in fact true. It was purely due to the large number of people who believed in the scripts and that claimed religion superior to scientific method which hindered Galileo from enlightening the world with, in this case the actual truth.

This shows that the way in which a model can help or hinder in the search for knowledge is not only restricted to the limitations of a certain model but commonly even to human reluctance to accept the introduction of new ideas.

In conclusion, models are limited to human perception which justifies why certain abstract theories cannot be modeled. Moreover the limits of finding knowledge are often bound to human reluctance with respect to change. Equally models may be argued as the fuel for science since the process of continuous doubt of scientists gives rise to finding new evidence for the truth and what is actually right. In general the effectiveness of a model is most largely dependent on the actual knower which is why its application varies throughout all areas of knowledge.

Hey, I'm Max.

I'm an Athlete turned Finance Ph.D., Engineer, and Corporate Consultant.