“Everything should be made as simple as possible, but not simpler”

The above quote by Albert Einstein, sums up a possible perspective of the knower; a perspective where accuracy and simplicity are mutually exclusive. The term ‘simplicity of knowledge’ refers to a process of converting complex expressions into easily understandable statements. In the process, it should not result in the loss of the statement’s core elements, as making a statement too simple is impractical in helping to understand its true meaning. On the other hand, ‘accuracy of knowledge’ signifies the extent to which a statement is true with regard to the entity it is describing. The more clearly and closely a statement describes something, the more accurate it is said to be. The tradeoff between accuracy and simplicity becomes quite evident when statements are broken down to reduce their complexity, to make them more comprehensible. But in the process, the statement loses certain core elements, which are critical in maintaining the accuracy of the statement. The tradeoff suggested here does not mean that accuracy and simplicity cannot exist at the same time, but rather, for simplicity to increase, the accuracy is compromised (and vice versa). On the other hand, through our sense perception and reason, we are also led to question the foundation of the term ‘knowledge’, whether knowledge itself is a simplification of the entity it is describing. No matter how intricate a piece of knowledge is, it is still an explanation of something that is compressed in terms that are understandable. So the complexity of the knowledge is itself incapable to exceed that of the entity it is trying to describe. Here the term complexity is synonymous with accuracy, as the accuracy of knowledge is often determined by how intricate complex expressions are at describing an entity. Human and Natural Sciences are domains, where there seems to be a fundamental tradeoff that exists between accuracy and simplicity, which is intricately linked with the laws of nature. The importance of this tradeoff in the formulation of theories has been the subject of endless debates since the time of ancient Greece. This leads us to our Central Knowledge Question "To what extent are accuracy and simplicity mutually irreconcilable in pursuing and understanding knowledge?" In this essay, I will investigate the role of simplicity considerations in scientific methodology and the aspect of accuracy of knowledge in the physical world.

Understanding the human mind has been one of the most mystifying and complex endeavors sought out by Psychologists. Psychology is one such subject where there is substantial difficulty in differentiating between accuracy and simplicity. My claim is that the accuracy of a model as determined by its complexity is always reduced by the process of simplification. Psychologists make use of models to predict and explain human behavior. However, the complexity of the human mind along with its influence to biological and sociocultural factors have been major hurdles in understanding human behavior. One’s behavior might be influenced by his/her upbringing, or as a direct result of peer pressure. For example, when I had changed schools after my 10th grade, the sudden exposure to a more competitive environment along with an increased coursework, and other social factors made me anxious and incongruous. So in order for a psychologist to explain the change in my behavior, he would have to take into account all those factors into his model to come to his final decision. Therefore, Models become extremely complicated to account for a multitude of such factors that play a role in influencing one's behavior. In this case, simplifying the model would compromise its applicability and reliability, as important information would have to be left out in order to reduce its complexity. Models would then fail to encompass for all the factors that might affect the final outcome, and would therefore make it hard to apply knowledge obtained from such models to real world scenarios.
 
Cognitive scientists for long have been attempting to study the role of simplicity in our inferential psychology. My counter-claim is that the simplicity of knowledge plays a key role in decision making processes in the human sciences. Cognitive science is an area of interdisciplinary science that draws on many fields of human sciences such as psychology and linguistics; in developing theories about perception, thinking and learning. Perception, learning and high-level cognition all involve pattern recognition in data. Our perception is influenced by our visual senses, which help directly identify patterns in data collected from the external world. Determining structure in language requires finding patterns in linguistic inputs and cognition involves finding patterns in dense, impenetrable information. The problem of induction is very fundamental in cognitive science, where there are seemingly infinite patterns that could be inferred from a finite collection of data. Tania Lombrozo, a cognitive psychologist at UC Berkeley, conducted several experiments on her patients where she found that participants used the relative simplicity of explanations as a guide to assess their probability. The participants were found to favor the more complex theory only when they were given a disproportionate amount of contrary information favoring the complex theory; otherwise they almost always preferred the simpler theory. I too shared the same experience, when I had visited an ENT specialist to find a solution for my ‘tinnitus’ problem. I had been given a choice of three different methods as a form of treatment from pills to an extensive sound therapy. I resorted to pills as it was a much simpler method of treatment; a method which I had some previous knowledge about. I had bypassed the other two methods simply on the basis of how much knowledge I had of their working. This led me to ask the following questions ‘To what extent is simplicity desired in a decision making process?’ and ‘Is simplicity the only factor that makes an individual make a particular decision?’.

Occam’s razor is often deemed as one of the most fundamental dogmas of modern science. The principle states that “When you have two competing theories that make exactly the same predictions, the simpler one is always better”. My claim is that simplicity of theories must be considered as important criteria for comparing rival theories. In the natural sciences, simplicity of a theory is a motivating factor for its acceptance in the scientific community. As a result, scientists frown upon the use of unnecessary assumptions on theories. Heisenberg’s Uncertainty Principle in Quantum Mechanics is one of the most cited examples of Occam’s razor in action. According to the Principle, there is like a fundamental limit on the accuracy to the measurement of two independent variables – position and momentum in Quantum Physics. Physical events taking place at the sub-microscopic level are not fully determinate and are only predictable statistically in terms of probabilities. It not only suggests that accuracy is limited, but that it is limited due to natural physical properties and not due to the simplifying knowledge. ‘To what extent is accuracy limited in the natural world’? For example, in our lives we unknowingly attempt to simplify things scientifically when trying to decipher a mystery. When we hear a strange noise from outside, we often try to reduce the internal noises by turning the television volume down and/or have the people present in the room stop talking. We do this because the internal noises only hinder our ability to hear the outside noises more clearly. Therefore, the quieter the room, the more clearly and accurately one would be able to identify the auditory source and decipher its information.

But at the same time, there are numerous reasons to suspect that scientific theories are not always driven by simplicity. Science, especially Physics, has repeatedly shown that data collected from future experiments often supports the more complex theories compared to pre-existing data. My counter-claim is that scientific evaluation of theories should not be based purely on philosophical principles of simplicity, but rather be driven by current data as well as the possibility of future experiments to accurately formulate theories. In the late 19th century, physics was believed to have neared its end; where every known phenomenon related to motion, heat and electricity, were beautifully summarized in the form of equations. The theories describing those three phenomenon were thought to be complete. But since then, these theories have been incrementally replaced by more and more complex theories such as General Relativity. As more experiments are performed to test the validity of these theories, the more it seems that the simplest theories often fail to explain all the underlying phenomenon and/or assumptions. Today, this has culminated in the formulation of ‘M-theory’, a theory that seeks to unify the laws of Quantum Mechanics and Gravitation, in doing so predicting the existence of 10 dimensions of space, made up by tiny one-dimensional ‘strings’ as the fundamental building blocks of reality. The history of physics has also repeatedly shown that theories get more complicated as our understanding of the physical world improves. Therefore, theories should not be eliminated solely based on their simplicity, as the simplest of explanations often yield to complexities as our knowledge of them increases.

In conclusion, there is certainly a tradeoff between accuracy and simplicity as shown through the claims and counterclaims of the two areas of knowledge discussed in this essay. However, there are numerous cases, as shown, where simple statements need not be untrue, as well as accurate statements need not be complex in nature. Knowers should instead carefully evaluate individual statements using their own ways of knowing, without letting any presumptions interfering on whether complex or simple statements could be trusted or not.