In this blog post, we will examine whether it is possible to strike a balance between simplicity and accuracy in natural sciences and social sciences, and explore the conflicting relationship and differences between the two.
“There is always a trade-off between simplicity and accuracy in knowledge.” This is something my high school chemistry teacher said in passing during class. I resonated with this statement to a large extent. Every time I looked at a photo of the Demilitarized Zone (DMZ) dividing the Korean Peninsula, I felt the pain and suffering of the Korean people indirectly. However, when I actually visited the DMZ and saw the Imjin River flowing through it, I could vividly feel the pain and suffering of the citizens during the Korean War. This was vastly different from the emotions I felt when simply looking at a photo. Through this experience, I strongly felt that knowledge presented on a two-dimensional plane—such as textbooks, photos, and books—which is succinctly condensed into a two-dimensional plane, does not adequately reflect the actual three-dimensional world surrounding us. Of course, this example does not adequately illustrate the trade-off between simplicity and accuracy in knowledge. However, it is suitable for explaining the trade-off between simplicity and accuracy and implies the overall direction of this essay.
Concise knowledge refers to knowledge that is easy to understand, with few implicit assumptions within the explanation, resulting in simple knowledge derived through inference. Accurate knowledge serves as a measure of the precision of knowledge corresponding to facts. A trade-off refers to the relationship between two goals, where achieving one goal requires sacrificing another. In other words, it is a conflicting relationship. The trade-off between concise and accurate knowledge can be easily observed in both social science phenomena and natural science phenomena. For example, a simple globe shows us the overall shape of the Earth and the locations of countries, but it does not provide accurate and complex knowledge about the geographical characteristics or areas of countries. Such examples of the trade-off between simplicity and accuracy can be easily found in our surroundings, but the extent to which such conflicting relationships exist is not well understood. I believe that the trade-off between simplicity and accuracy in knowledge is related to how knowledge is used. In both social sciences and natural sciences, the trade-off between simplicity and accuracy becomes clear when knowledge is used to predict actual phenomena, but when knowledge is used solely for explanatory purposes, the trade-off disappears. In this essay, I will examine this statement in more detail.
First, let us examine the trade-off between simplicity and accuracy in knowledge from the perspective of natural science. In natural science, the validity of the above statement depends on how knowledge is used. There are two ways to use knowledge: predicting phenomena in the real world through knowledge, i.e., predicting the three-dimensional world through a two-dimensional model, and staying within the two-dimensional world to explain knowledge. In physics class, we used a simple Boltzmann model to predict the temperature of the Earth’s surface. The result showed that while the actual temperature is 263K, the surface temperature of the Earth was calculated as 284K squared. This experience demonstrates that using simplified knowledge to predict real-world situations can lead to inaccurate results. However, when knowledge is used to independently explain itself, it appears to exhibit a positive relationship between simplicity and accuracy. For example, the simple Bronsted-Lowry acid theory contains accurate and complex knowledge. In this theory, an acid is both a proton donor and an electron pair acceptor. This theory is a more complex version of Lewis’s acid theory. In other words, when knowledge is used to predict actual phenomena, the trade-off between simplicity and accuracy becomes clear. In contrast, when knowledge is used solely for explanatory purposes, the trade-off relationship is not observable. Some may argue that even simple knowledge models can accurately predict natural phenomena. For example, the standard hydrogen electrode described in high school textbooks provides accurate knowledge about the feasibility of oxidation-reduction reactions.
For example, during a high school chemistry experiment, I selected Zn/Zn(2+) and Cu/Cu(2+) chemicals through simple calculations while constructing a galvanic cell. The calculation resulted in a total cell potential of 1.10 volts, indicating that the chemical reaction could occur spontaneously. This meant that chemical energy could be converted into electrical energy without external interference, and the experimental results matched the expected outcomes. As a result of this experiment, the feasibility of the redox reaction could be relatively accurately predicted. However, the current displayed on the voltmeter was 0.75 volts, slightly lower than the predicted 1.10 volts. This indicates that the quantitative prediction was not accurate. Therefore, we can conclude that simple knowledge may be accurate in explaining and predicting qualitative results, but it may not be accurate in predicting quantitative results. Here, qualitative prediction refers to the pattern of a specific scientific phenomenon, not quantitative prediction.
In the social sciences, the conflict between simple knowledge and accurate knowledge is similar to the situation found in the natural sciences. An example is the monetary policy theory used by the Thai government to overcome the 1997 Asian financial crisis. According to this theory, the Thai government raised interest rates to protect its currency. This decision not only failed to overcome the crisis but also exacerbated it. This suggests that when two-dimensional knowledge is applied to real-life crises, it has the potential to worsen the human condition and have adverse effects on 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 the economy, the government can lower interest rates and increase spending. Ultimately, when social science knowledge is used to predict actual phenomena, the conflict between simplicity and accuracy becomes clear. However, when knowledge is used solely for the purpose of explanation, simplicity and accuracy can coexist. On the other hand, those who oppose the author’s argument will argue that simple knowledge can also predict accurate situations.
For example, the simple law of demand that we commonly know is accurate from the perspective of predicting changes in consumer psychology and production. The law of demand states that “when the price of a specific good or service increases, the quantity demanded decreases.” Using the law of demand as an example, let’s assume that the only substitute for Apple smartphones is Samsung smartphones. 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 law of demand assumes that only two variables—price and demand—are considered, which may be somewhat detached from reality. However, this simple law of demand allows us to predict actual market changes and outcomes and provides qualitative information about “how the market will flow.” Just like in natural science, when it comes to quantitative predictions, simple knowledge about social science phenomena makes it hard to predict accurate information.
In conclusion, we can see that it’s not always true that “simplicity and accuracy of knowledge are always in a trade-off relationship.” It depends on how you use that knowledge. So far, we have examined the degree of conflict between simplicity and accuracy of knowledge under the definition of accuracy as a measure of the correctness of knowledge. However, through this essay, we have learned that the two disciplines view the trade-off between simplicity and accuracy of knowledge differently. In other words, we have come to understand that the “accuracy of knowledge” in natural science and the “accuracy of knowledge” in social science are different. The following paragraph will examine how the definition of “accuracy” differs between these two fields and explore the differences between social sciences and natural sciences to gain a deeper understanding of the essay topic.
Broadly speaking, social scientific knowledge tends to be subjective, while natural scientific knowledge tends to be objective. Let’s take economics, a field of social science, as an example. Most theories are based on the assumption that “humans are ideal.” This is because all individuals have different thoughts and behaviors, and the goal is to explain the most universal social behavior. Based on this assumption, there are two or more theories that explain the behavior of specific humans, such as the Keynesian and monetarist perspectives. Monetarists believe in the efficiency of market forces and therefore argue that the government is not necessary to run the economy efficiently. Keynesians, on the other hand, argue that economic efficiency is maximized when the government intervenes through government policies. The difference between these two theories can be simply explained as a difference in perspective on how to analyze human behavior. Therefore, accuracy in social science is determined by the emotions, reasoning, and intuition of the decision-maker regarding which theory to use. As in the example above, where the Thai government’s decision exacerbated the crisis, unintended consequences can arise. In contrast, theories in natural science are relatively objective. Unlike human behavior, natural phenomena are theorized based on objective standards. For example, Newton’s second law and Darwin’s theory of evolution have been refined through numerous refutations and improvements, establishing themselves as more accurate and reliable knowledge. Ultimately, it can be seen that natural science theories pursue objective knowledge. Therefore, accuracy is determined by the decision-maker’s emotions, reasoning, and intuition regarding which social science theory to use, all integrated into their personal perspective. Therefore, the specific definition of “accuracy” required in social science is “that which has the greatest potential to improve the quality of all humanity,” while in natural science, it is “that which is closest to the truth of nature.” Through this essay, we have examined the trade-off between the simplicity and accuracy of knowledge in detail and reviewed its validity from the perspectives of social science laws and natural science laws.
In conclusion, when knowledge is used to predict actual phenomena, the trade-off between simplicity and accuracy becomes more apparent in both social science and natural science. However, when knowledge is used for the purpose of explanation itself, the trade-off relationship disappears. Furthermore, we have seen that the definitions of “accuracy” required by social science and natural science are also different. Natural science seeks “what is closest to the truth of nature,” while social science seeks “what is most likely to contribute to improving the quality of all humanity.” This reveals that theory aims to generalize and explain the complex reality.
