I often hear this critique of my em econ talks: “This isn’t hard science, so it is mere speculation, where anyone’s guess is just as good.”
I remember this point of view – it is the flattering story I was taught as a hard science student, that there are only two kinds of knowledge: simple informal intuition, and hard rigorous science:
Informal intuition can help you walk across a street, or manage a grocery list, but it is nearly hopeless on more abstract topics, far from immediate experience and feedback. Intuition there gives religion, mysticism, or worse. Hard science, in contrast, uses a solid scientific method, without which civilization would be impossible. On most subjects, there is little point in arguing if you can’t use hard science – the rest is just pointless speculation. Without science, we should just each user our own intuition.
The most common hard science method is deduction from well-established law, as in physics or chemistry. There are very well-established physical laws, passing millions of empirical tests without failure. Then there are well-known approximations, with solid derivations of their scope. Students of physical science spend years doing problem sets, wherein they practice drawing deductive conclusions from such laws or approximations.
Another standard hard science method is statistical inference. There are well-established likelihood models, well-established rules of thumb about which likelihood models work with which sorts of data, and mathematically proven ways to both draw inferences from data using likelihood models, and to check which models best match any given data. Students of statistics spend years doing problems sets wherein they practice drawing inferences from data.
Since hard science students can see that they are much better at doing problem sets than the lessor mortals around them, and since they know there is no other reliable route to truth, they see that only they know anything worth knowing.
Now, experienced practitioners of most particular science and engineering disciplines actually use a great many methods not reducible to either of these methods. And many of these folks are well aware of this fact. But they are still taught to see the methods they are taught as the only reliable route to truth, and to see social sciences and humanities, which use other methods, as hopeless delusional, wolves of intuition in sheep’s clothing of apparent expertise.
I implicitly believed this flattering story as a hard science student. But over time I learned that it is quite wrong. Humans and their civilizations have collected a great many methods that improve on simple unaided intuition, and today in many disciplines and fields of expertise the experienced and studied have far stronger capacities than the inexperienced and unstudied. And these useful such methods are not remotely we’ll summarized as formal statistical inference or deduction from well-established laws.
In economics, the discipline I know best, we often use deduction and statistical inference, and many of our models look at first glance like approximations derived from well-established fundamental results. But our well-established results have many empirical anomalies, and are often close to tautologies. We often have only weak reasons to expect many common model assumptions. Nevertheless, we know lots, much embodied in knowing when which models are how useful.
Our civilization gains much from our grand division of labor, where we specialize in learning different skills. But a cost is that it can take a lot of work to evaluate those who specialize in other fields. It just won’t do to presume that only those who use your methods know anything. Much better is to learn to become expert in another field in the same way others do; but this is usually way too expensive.
Of course, I don’t mean to claim that all specialists are actually valuable to the rest of us. There probably are many fraudulent fields, best abolished and forgotten, or at least greatly reformed. But there just isn’t a fast easy way to figure out which are those fields. You can’t usually identify a criminal just by their shifty eyes; you usually have look at concrete evidence of crime. Similarly, you can’t convict a field of fraud based on your feeling that their methods seem shifty. You’ll have to look at the details.
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The danger seems to lie in not knowing the boundaries of expert knowledge. (Kahneman makes this point.) "A little knowledge is a dangerous thing" can, perhaps, apply to entire disciplines.