The tradeoff between simplicity and accuracy of knowledge is discussed using a variety of examples from the natural and social sciences, and it is shown that this tradeoff can vary depending on how the knowledge is used.
“There is always a tradeoff between simplicity and accuracy of knowledge.” My high school chemistry teacher said this in passing during class. I could relate to this statement quite a bit. Whenever I saw pictures of the demilitarized zone dividing the Korean peninsula, I felt the pain and suffering of the people indirectly, but when I actually went to the DMZ and saw the Imjin River flowing through it, I could vividly feel the pain and suffering of the people during the Civil War. This was very different from the emotions I felt from the photographs, and it made me feel strongly that the knowledge represented on a two-dimensional plane, that is, the knowledge that is simply implied on a two-dimensional plane, such as textbooks, photographs, and books, does not reflect the real three-dimensional world that surrounds us. Of course, this example doesn’t really illustrate the tradeoff between simplicity and accuracy. But it’s a good way to illustrate the tradeoff between simplicity and accuracy, and it hints at the overall direction of this essay.
Simple knowledge is easy to understand, simple knowledge that is deduced from the lack of implicit assumptions in the explanation. Accurate knowledge is a measure of the accuracy of a factual statement. A tradeoff is a relationship between two goals that requires one to be sacrificed in order to achieve the other; in other words, it’s a tradeoff. The tradeoff between simplicity and accurate knowledge can be seen in social and natural science phenomena. For example, a simple globe gives us a big picture of what the Earth looks like and where each country is located. However, it doesn’t give us precise and complex knowledge about the geographical characteristics of countries, their area, etc. Examples of this tradeoff between simplicity and accuracy are all around us, but the extent to which this tradeoff exists is not well understood. I argue that the tradeoff between simplicity and precision is related to how knowledge is used. In both the social and natural sciences, the tradeoff between simplicity and accuracy becomes more pronounced when knowledge is used to predict real-world phenomena, but less so when knowledge is used for explanation itself. In this essay, we will examine the above statement more closely.
First, let’s look at the tradeoff between simplicity and accuracy of knowledge from the perspective of the natural sciences. In the natural sciences, the validity of the above statement depends on how knowledge is used. There are two ways to use knowledge: to use it to predict the world around us, i.e., to predict the three-dimensional world from the two-dimensional world, or to stay in the two-dimensional world and explain it. In physics class, we once used a simple Boltzmann model to predict the temperature of the Earth’s surface. The result was that the Earth’s surface temperature was 284 K squared, even though the actual temperature was 263 K. This experience shows that using simplified knowledge to predict real-world events can lead to inaccurate results. However, when knowledge is used to explain itself independently, simplicity and accuracy appear to be positively related. For example, the simple model Bronsted-Laurie’s acid theory implies precise and complex knowledge. It implies that the acids mentioned in the theory are proton donors and that acids are electron pair acceptors. This theory is a more complex version of Lewis’ theory of acids. In other words, when knowledge is used to predict real-world phenomena, the trade-off between simplicity and accuracy is apparent. In contrast, if knowledge is used for explanation itself, the tradeoff is invisible.
One might argue that simple models of knowledge are just as accurate at predicting natural phenomena. For example, a standard hydrogen electrode, as described in the data collections I used in high school, provides accurate knowledge about the feasibility of redox reactions. For example, I once chose Zn/Zn(2+) and Cu/Cu(2+) chemistries based on a simple calculation while building an electrochemical cell for a high school chemistry experiment. The calculations showed that the total cell potential was 1.10 volts, and I knew that the chemical reaction could be spontaneous, meaning that chemical energy would be converted to electrical energy without any external disturbance, which was the expected result of the experiment. As a result of this experiment, we can say that we were able to predict the feasibility of the redox reaction relatively accurately. However, the current shown on the voltmeter was 0.75 volts, which was slightly lower than the predicted 1.10 volts. In other words, the quantitative prediction was not accurate. So we can conclude that simple knowledge can be accurate in terms of describing and predicting qualitative outcomes, but not in terms of predicting quantitative outcomes. By qualitative predictions, we mean patterns in certain scientific phenomena that are not necessarily quantitative predictions.
The trade-off between simple knowledge and precise knowledge in the social sciences is similar to the situation found in the natural sciences. An example of this is the monetary policy theory used by the Thai government to overcome the 1997 Asian financial crisis. According to that theory, the Thai government raised interest rates to protect its currency. Instead of overcoming the crisis, this decision led to a situation where the crisis was magnified. This suggests that when second-order knowledge is applied to real-life crises, it has the potential to worsen human conditions and adversely affect society as a whole. However, theoretical monetary policy successfully explains human behavior when the knowledge itself is discussed within an independent academic framework. For example, to increase aggregate demand in an economy, the government can cut interest rates and increase spending. In the end, the tradeoff between simplicity and accuracy becomes apparent when social science knowledge is used to predict real-world phenomena. However, when knowledge is used to explain itself, simplicity and accuracy can coexist.
On the other hand, an opponent of my argument would argue that simple knowledge can also be accurate in predicting situations. For example, the very simple law of demand as we know it is accurate in terms of predicting the psychology of consumption and changes in production. The law of demand gives rise to the simple statement that “as the price of a particular good or service increases, the quantity demanded will decrease.” To illustrate the law of demand, let’s say that the only substitute for an Apple smartphone is a Samsung smartphone. If the price of Samsung smartphones increases, the demand for Samsung smartphones will decrease and the demand for Apple smartphones will increase. Of course, the premise of the law of demand assumes that we only consider the variables of price and quantity demanded, which may be a bit far from reality. However, this simple law of demand allows us to predict changes and outcomes in the real market and provides qualitative information about how the market will behave. As in the natural sciences, simple knowledge in social science phenomena is difficult to predict accurately when it comes to quantitative predictions.
In conclusion, we can see that it is not true that “simplicity and accuracy of knowledge are always a trade-off”. It depends on how you use that knowledge. So far, we’ve been looking at the extent of the tradeoff between simplicity and precision, using the definition of precision as a measure of the correctness of knowledge. However, throughout the essay, we realized that the tradeoff between simplicity and accuracy of knowledge is viewed in different ways by two disciplines: the natural sciences mean “knowledge accuracy” and the social sciences mean “knowledge precision”. In the following paragraphs, we will explore how the definition of “accuracy” differs in these two fields and delve deeper into the essay topic by exploring the differences between the social and natural sciences.
Broadly speaking, social science knowledge tends to be subjective, while natural science knowledge tends to be objective. Take economics, a branch of social science, for example. Most theories are based on the premise that humans are idealized. This is to explain the most common social behaviors because every individual thinks and acts differently. This premise leads to the existence of two or more theories to explain specific human behavior, as in the case of the Keynesian and monetarist perspectives. Monetarists believe in the efficiency of market forces and argue that the government is not necessary to run an economy efficiently. Keynesians, on the other hand, argue that economic efficiency is maximized when the government interferes through government policies. The difference between these two theories is simply the perspective from which human behavior is analyzed. Therefore, accuracy in social science is determined by the decision maker’s feelings, reasoning, and intuition about which theory to use. This can have unintended consequences, as in the case of the Thai government’s decision to exacerbate the crisis. In contrast, theories in the natural sciences are relatively objective. For example, Newton’s second law and Darwin’s theory of evolution have been disproven and refined to become more accurate and definitive knowledge. In the end, we can see that natural science theories seek objective knowledge. Therefore, accuracy is determined by the decision maker’s feelings, reasoning, intuition, and personal perspective on which social science theory to use. Therefore, the specific definition of “correctness” required by the social sciences is “that which is most likely to contribute to improving the quality of life for all humanity,” while for the natural sciences it is “that which is closest to the truth of nature.
In this essay, we specifically examine the trade-off between simplicity and accuracy of knowledge and examine its validity in terms of social and natural science laws, respectively. We conclude that, in both the social and natural sciences, the trade-off between simplicity and accuracy of knowledge is more pronounced when knowledge is used to predict real-world phenomena, but that the trade-off is not visible when knowledge is used for explanation itself. Furthermore, we found that the definition of “accuracy” required by the social sciences and the natural sciences is different. The natural sciences require “what is closest to the truth of nature,” while the social sciences require “what is most likely to contribute to improving the quality of life for all humanity. From this, we can see that theories seek to generalize and explain complex realities.
