It perplexes me that the word 'scientism' is predominantly used as a slur to put people down and criticize their world view and methodology. I realized something recently, however, that helped me understand the error that is often being made, and how that error compounds the problem that is often being called out when people make the accusation of scientism.
First off, lets settle what scientism is. Wikipedia gives a good definition, that fits well with the contexts in which I see the term used:
Scientism is belief in the universal applicability of the scientific method and approach, and the view that empirical science constitutes the most authoritative worldview or most valuable part of human learning to the exclusion of other viewpoints.
Well, that's a strange accusation. I've made it clear in numerous places that this is exactly my position, and I've repeatedly defended that position with robust logical arguments.
On the universal applicability of scientific method: yes, absolutely. If a thing has meaningful consequences, then why should science not be a good way learn about its properties? Whether it is something normally associated with science (astronomy, atomic physics, medicine, evolutionary biology, or whatever), or something more concerned with politics, law (here and here), morality, issues of religion, or the supernatural, all things can be investigated scientifically.
To be clear on my position on the supernatural: there is no such thing, it's a necessarily empty set (I'll come back to this point in a future post), but there are certain putative entities often identified as supernatural: ghost, goblins, fairies, gods, and such like. If such things did exist, however, (it's hard to say absolutely categorically that they don't, at least without being more precise about what they are, though there is, of course, no evidence supporting belief in any of them) then they would necessarily be physical beings, amenable to scientific investigation.
On the maximally authoritative nature of science-based investigation: again, this is necessarily so. Being scientific really just means being systematic. The only alternatives are at best, ignorance and at worst, fantasy passed off as fact. Why would any person wish to know about something, and choose to be non-systematic in the manner of their formation of beliefs about it? One cannot, while being coherent (see my post, Is rationality desirable?). To think that one can achieve rationally supportable degrees of belief, without using a rationally supportable procedure is a clear mistake. And this is identically what scientific method does: produce rationally supportable degrees of belief.
Now some might be tempted to argue that science isn't always necessary. Some things, for example, are just obvious. But let me emphasize: scientific method is a graded affair - not black or white. Whatever we can learn by implementing a low level of scientific rigour, we can learn a little more, in a little more detail, and with a little more confidence, by applying a slightly more systematic procedure.
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Now, here's the thing I noticed when I recently saw a little scientism bomb being dropped, elsewhere on the internet, by a person whose awareness of the scope and meaning of scientific method I have good reasons to trust. It seemed to me that what this person was complaining of was actually the opposite of scientism, i.e. dismissal as irrelevant or intractable, certain valid philosophical questions, because they are perceived not to fall within the scope of scientific method.
Well, lets be clear about what philosophy is: philosophy is love of wisdom. Wisdom can be thought of as dividable into three categories (where by 'knowing', in the following, I mean having a rationally supportable high level of confidence in some proposition):
- Knowing what things are true
- Knowing what procedures are effective for discerning what things are true
- Knowing how to behave effectively under certain circumstances
Note, however, that items 2 and 3 are really special cases of item 1. The question of whether it is valid, for example, to use probability theory when trying to attain rationally supportable degrees of belief is a question of fact about the nature of the real world (spoiler alert: it IS valid to use probability theory). The question of how to behave is a duo of empirical problems: (i) what is my utility function? (what do I actually value? - yes, this is an empirical question, my values are physical properties of my mind), and (ii) what actions will lead to consequences that will maximize my expected utility? (In fact, 2 is also a special case of 3: how should I behave if I value knowledge of X?) So all of philosophy is about figuring out what is probably true.
But, as I just argued, all meaningful questions of fact are best answered using science, and so love of wisdom entails a desire to follow scientific method. Thus, philosophy (defined as an endeavour, and not in terms of the traditional type of education received by the typical practitioner) is identical to science.
Thus, all philosophical questions fall under the scope of scientific method, and the scientist who dismisses such issues as unscientific is failing to appreciate the range of validity, the very meaning, of their own profession. This is a mistake that it's important to call out. Part of the reason we have a society run by politicians and law makers who believe that they can divine correct policy, without implementing scientific procedure, is that prominent scientists are repeatedly telling them that they can. ('Oh, that's not a scientific question, that's a matter of human affairs,' or, 'there's the evidence, now it's for you, the politicians, to decide what it means,' or perhaps worst of all, the Nuremberg defence: 'don't ask me if it's right or wrong, I'm just a scientist.')
But notice that the accusation of scientism completely misses the mark, here. Scientism, recall, is believing that all questions fall under science's magisterium, while the actual error being committed is the claim that certain problems are not in this category.
The cry of scientism, therefore, fails to draw proper attention to the fallacy that has been committed, and, in fact, is quite likely to reinforce it. Faced with this charge, one is, of course, free to refer to sources such as the Wikipedia definition, quoted above. Many a scientist who is somewhat on the ball, philosophically, however, is likely to look at such definitions and say, 'yup, that's me, and proud of it!' And of whatever mistake they might have made that prompted the rebuke, they are likely to conclude, 'if that's scientism, then I'm perfectly happy to continue committing the crime.'
I would argue in contrast that there is a prior basis of rational reasoning, which is conceptual analysis (which includes logic, mathematics, understanding language, and so on). Only on this basis can the metaphysical and propositional model underlying your domains of knowledge can be defined; e.g. only once you understanding "things" to exist in "reality" and propositions as being "true" if they correspond to reality can you define (again, using language and logic - conceptual analysis) the question of whether some proposition is true (your (1)).
ReplyDeleteConceptual analysis is what (good) philosophy is all about, and does NOT fall under the scientific method but rather justifies it - it is because of our understanding of what "truth" or "belief" are, for example, and by the application of logic and mathematics, that we can justify the use of Bayes' Rule as the way to seek out truth.
If our methods of thinking were justified scientifically, we would have circularity - our methods justifying our methods. What we have instead, I suggest, is foundationalism - our methods for reasoning under uncertainty are founded on our methods for thinking about certainty (which are themselves axiomatic).
The upshot of all of this is that the charge of Scientism CAN be correctly derogative when people maintain that we should apply the Scientific Method to conceptual questions. You don't do mathematics by empirical induction, and you don't do an analysis of what "morally good" means by induction (although what people think about when the use the word is important, at the philosophical level the point is to explicate a clear meaning rather than to describe the confused and varied uses in ordinary use).
In practice, however, the charge of Scientism is usually leveled at applications of the Scientific Method where it DOES belong, as in e.g. the science of morality (which IS a science, although like all sciences it is based on arbitrary/philosophical definitions of its subject matter), rather than where it doesn't belong (as in e.g. coming up with said definitions).
Yair
P.S. On an unrelated question - I'm teaching some Scientific Method to highschoolers. I taught them Bayes Rule, but I can't find a nice and SIMPLE (highschoolers!) Baysian analog of simple linear regression - coming up with the parameters of for a line formula and the uncertainty in them from a Bayesian perspective. Can you perhaps direct me in the right direction?
Cheers,
Yair
Hi Yair
DeleteInteresting comments.
As I see it, conceptual analysis does nothing more than specialize in eliminating ambiguity in the relationship between symbol and signified - something that is inherently part of science, anyway. Your examples of "things" and "reality" are trivial empirical questions, and the concept of truth is already given, once we have the hardware in place to constitute a decision-making entity.
Of course, we would like to be able to boast a non-circular foundation for everything, but I'm afraid this is just not a luxury we can aspire to. You say that reasoning under uncertainty is founded on axiomatic principles of reasoning under certainty - well yes, but but where do those axioms come from? They are either arbitrary (hardly a satisfactory solution to the circularity problem), or else they are derived from our capacity to reason probabilistically.
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Regarding a linear regression example, I would start with a case that is assumed to pass through the origin, so that there is only one parameter to estimate - the calculation can then be done numerically using a spread sheet, or if you would like to be able to extend it more easily to higher dimensionality, using some simple code.
Do they have any familiarity with the normal distribution? They will need to be able to appreciate that each x-y pair used to fit the line has itself an associated probability distribution.
When I get some time, I'll develop an example and post it on the blog - hopefully, before too far into the new year.
"As I see it, conceptual analysis does nothing more than specialize in eliminating ambiguity in the relationship between symbol and signified - something that is inherently part of science, anyway. Your examples of "things" and "reality" are trivial empirical questions, and the concept of truth is already given, once we have the hardware in place to constitute a decision-making entity."
DeleteReducing ambiguity is a huge part of conceptual analysis, and it is indeed a part of the scientific method - but it is the non-empirical part, the part that comes before (as well as after) the empirical investigations. And it has a name - "philosophy". :)
I don't think our natural conceptions of things like "thing" or "truth" are necessarily clear enough, I do think philosophizing and coming up with non-ambigous (and useful!) definitions and recognizing our preconceptions can be productive.
"Of course, we would like to be able to boast a non-circular foundation for everything, but I'm afraid this is just not a luxury we can aspire to. You say that reasoning under uncertainty is founded on axiomatic principles of reasoning under certainty - well yes, but but where do those axioms come from? They are either arbitrary (hardly a satisfactory solution to the circularity problem), or else they are derived from our capacity to reason probabilistically. "
I don't think there is any way to justify our most basic rational intuitions; they're what we use to judge everything else. For myself, I am convinced by deductive arguments that Bayesianism is the right way (e.g. Cox's Theorem) rather than being convinced by probabilistic arguments that logic is true (I'm not sure if you can even state that consistently).
"Do they have any familiarity with the normal distribution? "
No, but they can gain familiarity...
"When I get some time, I'll develop an example and post it on the blog - hopefully, before too far into the new year."
I'm looking forward to it!