In this blog post, we will look at the trade-offs and differences between simplicity and accuracy of knowledge in the natural and social sciences.
“There is always a trade-off between simplicity and accuracy of knowledge,” said my high school chemistry teacher as he walked by during class. I could relate to this statement to a great extent. Whenever I saw a photo of the demilitarized zone that divides the Korean Peninsula, I felt the pain and suffering of the people. But when I actually went to the demilitarized zone and saw the Imjin River flowing through it, I could vividly feel the pain and suffering of the citizens during the Korean War. It was very different from the emotions I felt from just looking at a photo. This gave me a strong feeling that the knowledge that appears on a two-dimensional plane, i.e. the knowledge that is simply and concisely presented on textbooks, photographs, and books, does not properly reflect the real three-dimensional world that surrounds us. Of course, this example does not well represent the conflict between simplicity and accuracy of knowledge. However, it is suitable for explaining the conflict between simplicity and accuracy and suggests the overall direction of this essay.
Simple knowledge is easy to understand and is inferred from the lack of implicit assumptions in the explanation. Accurate knowledge is a measure of the accuracy of knowledge that corresponds to the facts. A trade-off is a relationship between two things in which one is sacrificed to achieve the other. In other words, it is a conflict. The trade-off relationship between simplicity and accurate knowledge can be easily found in social and natural science phenomena. For example, a simple globe shows us the big picture of how the Earth looks like and where each country is located. However, it does not provide accurate and complex knowledge about the geographical characteristics or area of each country. Examples of the trade-off between simplicity and accuracy can be easily found around us, but the extent to which this conflict exists is not well known. I believe that the trade-off between simplicity and accuracy of knowledge is related to how knowledge is used. In both social and natural sciences, the trade-off between simplicity and accuracy is clear when knowledge is used to predict actual phenomena, but the trade-off disappears when knowledge is used for the purpose of explanation itself. In this essay, I would like to take a closer look at the above statement.
First, let’s look at the trade-off relationship between simplicity and accuracy of knowledge from the perspective of natural science. The validity of the above statement in natural science depends on how the knowledge is used. There are two ways to use knowledge: to predict surrounding phenomena through knowledge, that is, to predict a three-dimensional world through a two-dimensional world, and to stay in a two-dimensional world and explain the knowledge. I once tried to predict the surface temperature of the Earth using a simple Boltzmann model in physics class. As a result, I found that the actual temperature was 263 K, but the surface temperature of the Earth was 284 K squared. This experience tells us that using simplified knowledge to predict real-life situations can lead to inaccurate results. However, when knowledge itself is used to explain something independently, it is seen as having a positive relationship between simplicity and accuracy. For example, the simple model Bronsted-Lowry acid theory implies accurate and complex knowledge. The acid mentioned in this theory means that it is a proton donor and an electron pair acceptor. This theory is a more complex version of Lewis’s mountain. In other words, if knowledge is used to predict actual phenomena, the trade-off between simplicity and accuracy of knowledge is clearly visible. On the other hand, if knowledge is used for the purpose of explanation itself, the trade-off relationship is not visible.
Some may argue that the simple knowledge model is also accurate in predicting natural phenomena. For example, the standard hydrogen electrode written in the data collection I used mainly in high school provides accurate knowledge about the feasibility of redox reactions. For example, I once chose Zn/Zn(2+) and Cu/Cu(2+) chemicals through simple calculations while making a galvanic cell during a high school chemistry experiment. The calculation showed that the total cell potential was 1.10 volts, indicating that the chemical reaction could occur spontaneously. In other words, it was found that chemical energy is converted into electrical energy without external disturbance, which is the expected result of the experiment. As a result of this experiment, it can be said that the feasibility of the redox reaction could be predicted 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. Therefore, it can be concluded that simple knowledge may be accurate in terms of explaining and predicting qualitative results, but it may not be accurate in predicting quantitative results. The qualitative prediction here refers to the pattern of a certain scientific phenomenon, although it is not a 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 has not only failed to overcome the crisis, but has instead led to a situation that has further exacerbated the crisis. This suggests that when two-dimensional knowledge is applied to real-life emergencies, it has the potential to worsen the situation for humanity 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 system. For example, to increase the total demand in the economy, the government can lower interest rates and increase spending. In the end, when social science knowledge is used to predict actual phenomena, the trade-off between simplicity and accuracy of knowledge becomes clear. However, when knowledge is used for its own explanatory purposes, simplicity and accuracy can coexist.
On the other hand, those who oppose my argument will argue that even simple knowledge can predict the exact situation. For example, the very simple law of demand that we are all familiar with is accurate in terms of predicting changes in consumption psychology and production. The law of demand leads to the simple law that “when the price of a particular good or service increases, the amount of demand decreases.” Let’s take the law of demand as an example. Suppose that the only alternative to Apple smartphones is Samsung smartphones. If the price of Samsung smartphones increases, the demand for Apple smartphones will increase as the demand for Samsung smartphones decreases. Of course, the premise of the law of demand is that “when only the variables of price and demand are considered,” which may be somewhat far from reality. However, this simple law of demand allows us to predict changes and outcomes in the real market and provides qualitative information on “how the market will flow.” As with the natural sciences, simple knowledge of social scientific phenomena is difficult to predict accurate information in quantitative forecasting.
In conclusion, we can see that “the simplicity and accuracy of knowledge are always in a trade-off relationship.” It depends on how that knowledge is used. We have so far examined the degree of trade-off between simplicity and accuracy of knowledge under the definition that accuracy is a measure of the accuracy of knowledge. However, through the essays, we found that the two disciplines view the trade-off between simplicity and accuracy of knowledge in different ways. In other words, I learned that the “accuracy of knowledge” meant in the natural sciences is different from the “accuracy of knowledge” meant in the social sciences. The following paragraphs will examine how the definitions of “accuracy” differ in these two fields and explore the differences between the social sciences and the natural sciences to better understand the topic of the essay.
Broadly speaking, social science knowledge is subjective and natural science knowledge is objective. Let’s take economics, a branch of social science, as an example. Most theories are based on the premise that “humans are ideal.” This is to explain the most common social behavior because each individual has different thoughts and behaviors. Based on this premise, there are two or more theories that explain certain human behaviors, such as the case of Keynesianism and monetarism. Monetarists believe in the efficiency of market forces, so they argue that the government is not particularly necessary to run the economy efficiently. On the other hand, Keynesianists argue that economic efficiency is maximized when the government interferes through government policies. The difference between these two theories is, simply put, which perspective is used to analyze human behavior. Therefore, accuracy in the social sciences is determined by the emotions, reasoning, and intuition of the decision-maker about which theory to use. As in the case of the Thai government’s decision, which exacerbated the crisis, there may be unintended consequences. In contrast, theories in the natural sciences are relatively objective. Unlike human behavior, natural phenomena are formed by objective standards. For example, Newton’s second law of motion and Darwin’s theory of evolution have been established as more accurate and certain knowledge after a number of refutations and improvements. In the end, it can be seen that natural science theories seek objective knowledge. Therefore, accuracy is determined by the individual’s perspective, integrating the decision maker’s emotions, reasoning, and intuition on which social science theory to use. Therefore, the specific definition of “accuracy” required by social science is “the most likely to contribute to improving the quality of all mankind,” and in the case of natural science, “the closest to the truth of nature.”
In this essay, we specifically examined the trade-off relationship between simplicity and accuracy of knowledge, and examined the validity of this from the perspective of social and natural science laws, respectively. In conclusion, the trade-off between simplicity and accuracy of knowledge becomes more pronounced when knowledge is used to predict actual phenomena in both social and natural sciences, but the trade-off disappears when knowledge is used for its own explanatory purposes. Furthermore, it was found that the definition of “accuracy” required by social sciences and natural sciences is also different. Natural sciences require “the closest thing to the truth of nature,” while social sciences require “the thing that is most likely to contribute to improving the quality of all mankind.” This shows that theories seek to explain complex realities by generalizing them.
