This is supposed to be a blog about 'soft science'. No, I don't want to bash 'soft scientists', at least not all of them. But bashing has something to do with this blog's purpose. The patterns of disdain between different fields of science is pretty complex; one exemplaric way to bring order in the world of interdisciplinary academic bashing - sorting the sciences by "purity" - is illustrated by xkcd. A rather simple way to speak about the "worthiness" of different scientific fields is to divide them into "hard" and "soft" sciences. Although not always using those terms, the idea of "hard and "soft" sciences is widely spread - and ultimatively I want to argue that this is a bad thing. The 'soft sciences' are not as useless as they may seem, which I will try to demonstrate in this blog.
If you find the concept of "hard and soft science" to be even remotely interesting, you might already have checked the wikipedia entry. If you haven't, that's not a problem, as I will quote it anyway:
"Hard science and soft science are colloquial terms often used when comparing fields of academic research or scholarship, with hard meaning perceived as being more scientific, rigorous, or accurate. Fields of the natural, physical, and computing sciences are often described as hard, while the social sciences and similar fields are often described as soft. The hard sciences are characterized as relying on experimental, empirical, quantifiable data, relying on the scientific method, and focusing on accuracy and objectivity." (Source: Wikipedia, 25.05.2011)
Apparently, what makes science "hard" is the scientific method: theories are tested and modified using empirical data and/or experiments. The availability of accurate empirical and experimental data depends heavily on the field of study. If a phenomenon can be repeatedly measured in a way that provides data that can be put in numbers, you have "accurate" empirical data. If you can repeatedly set a certain process in motion, having exactly the same starting position every time, you have an accurate experiment.
Those two things are rather possible in physics, for example - you can build a machine that measures the boiling point of water, a hundred or a thousand times in a row, hence receiving "accurate" data about when the water will reach its boiling point. On the basis of that data, you can predict when the water will boil on your one thousand and first experiment - thus proving your theory about the boiling point of water. If, however, your field of study is human society or culture, it is difficult to get quantifiable numbers, and even more difficult to set up experiments. The number of factors that you have to consider are much higher with people than with atoms, molecules, dirt or bacteria. In our little water-boiling-point-experiment, we might oversee the influence of air pressure on the boiling point - but once we have figured that out, we can compare the boiling point of water in different heights and improve our theory.
To illustrate the difficulty in doing research on humans, I have come up with a purely fictional experiment. We would try to measure people's metaphorical "boiling point" - by testing how often you have to give a person electric shocks until they complain. We have a lot of factors that could be relevant. That particular "boiling point" could depend on age, culture, class, sex, gender, body weight, historical period, mood, you name it - not to speak of biographical factors that are different for every person. We could, for example, shock 10000 middle-aged single women from denmark and get a result of an average 3,7 20-volt-shocks until they complain. A comparing group of 10000 middle-aged single women from france gets a result of an average 5,3 20-volt-shocks until they complain. While those numbers would certainly make for a fun little text in a tabloid, what theory could we construct based on those values? We could assert that french women are tougher - or we could argue that danish women complain earlier. Even if we find a way to keep those two factors apart - for example by making experiments with the same people that make them complain, but without causing any pain - what information do we get? We may conclude that danish middle-aged single women from denmark complain earlier than french middle-aged single women, but how do we explain that? Is it more acceptable in "danish" culture to complain? Do middle-aged people in denmark generally complain more than middle-aged people in france? As we see, even with more or less "quantifiable" studies, we would need to do a LOT of experimenting to establish "facts" about even a very specified "type" of people. Also, any theory based on this fictional experiment would only apply to the time frame in which it was conducted. Who knows, maybe french mainstream culture shifts towards earlier expression of pain in the next decade. People are complicated, and so are culture and society. And how do we define "french" and "danish" anyway? How do we define "woman", "middle-age", "single"? And how do we account for the unknown number of people from both countrys who are not brave (or stupid) enough to participate in an experiment that involves you getting shocked?
This was just one fictional experiment, but it's safe to assume that coming up with reliable ways to classify and measure people and comping up with stable environments for experiments with people is much, much more complicated than with less complex phenomena. Also, there are many things we cannot possibly measure at all - we cannot conduct experiments on people from different eras due to a lack of time travel technology, for example.
So, okay, experiments and empirical data are less accurate and more difficult to compare in social and cultural sciences than in biology, physics or chemistry. Point taken. "Hard sciences" operate with less complex data and can therefore generate and quantify empirical data easier and more precise. Of course, that does not mean that social and cultural sciences are more complex than natural sciences - but their data is more complex. "Using empirical and experimental data" is not a good definition for what science should be. We can argue that "hard sciences" have more objective and accurate data than "soft sciences". But that does not mean that there is no accuracy and no objectivity in "soft sciences". Answering a question scientifically means trying as hard as you can to be accurate and objective - in natural sciences, that means using empirical and experimental data. In social and cultural sciences, that mostly means requiring good reasons and logical proof for everything you say.
It is even wrong to call natural sciences "more accurate" than social and cultural sciences because their main difference is not the method, but the questions. Explaining natural phenomena with the methods of social science would be inaccurate, but it's the same way around. If your question is: "What effect did the invention of the atom bomb have on the american culture in the 1950s", using experiments or huge bulks of quantifiable data will not lead to an answer. Instead, you would have to read and analyse sources like books, magazines, movies, comics etc. As "cultural impact" is difficult to quantify, historians and sociologists would need to carefully construct theories based on those sources, and argue with each other about how important certain aspects are. While we will never get a perfect one-page-answer to a question like that, our theories on social and cultural questions are constantly tested, evalued an re-evalued by many, many "soft" scientists. That way, we get closer and closer to an answer that most or all scientists can agree with - which is as close to "objective truth" as it gets. In many ways, the process of creating viable "soft" theories resembles the way theoretical physics works - a lot of smart people with a certain set of data and already-agreed-on theories argue which of their unproven concepts seems to be the best answer to a certain problem.
The main reason why we should not underestimate the value of "soft sciences" is that they try to answer questions in a reasonable way that are asked by most people. Unlike physics and chemistry, almost everyone has theories concerning social and cultural questions. While most people are rather indifferent about string theory, controversial social, cultural and historical questions are discussed in everyday life - be it in the media, in the arts or on a meeting with friends on friday night. Most living people did experience neither World War II nor the Holocaust, but Godwin's law is still very much in effect.
While everybody has theories concerning the field of "soft sciences", many of those theories suffer from errors in reasoning and on common misconceptions. Reducing "soft science" to some kind of second-rate science leads to the idea that, since social and cultural sciences are an "inherently inaccurate" field, any theory in those fields is as good as the other. But that is not true. A theory that has good arguments and that gives good explanations is a better theory. A theory that has contradictions and that ignores evidence to the contrary is a bad theory.
Ultimately, the purpose of this blog is to discuss "soft science" theories, and sometimes to reveal some weaknesses of certain common theories. If answering questions in philosophy, history, sociology and culturology means to do "soft science", we can at least do our best to do good "soft science".
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