Like any other organized body of doctrine, science has its
founding myths. Modern science has two central ones: (i) That there exists a
non-trivial scientific method (SM)[1]
and (ii) that theory in science plays second fiddle to observation/experiment.
Combine (i) and (ii) and we reach a foundational principle: good science
practice discards theories when they clash with experimental observation. And, maybe just as important, bad science
involves holding onto theories that conflict with experiment/observation. Indeed,
from the perspective of SM perhaps the
cardinal irrationality is theoretical obstinacy in the face of recalcitrant
data.[2]
There are several reasons for the authority of this picture.
One is that it fits snugly with the Empiricist (E) conception of knowledge. As
I’ve noted before (much too often for many of you I suspect) E is both
metaphysically (see here)
and epistemologically (see here)
suspicious of the kind of generalizations that required by theory.
Metaphysically, for E, observation is first, generalizations
are second, the latter being, summations of the former. As theories, at least
good ones, rest on generalizations and given that they are only as good as the
data that they “generalize” it is not surprising that when they come in
conflict, theories are the things that must yield. Theories and laws are, for
E, useful shorthand compendia of the facts/data/observations and shorthands are
useful to the degree that they faithfully reflect that which they hand shortly.
Epistemologically, generalizations are etiologically
subsequent to observations. In fact they are inductively based on them and, in
the limit, should do nothing more than summarize them. Good scientific practice
should teach how to do this, should teach how to eliminate the “irrationalities”
that waylay legit inductions forcing them away form the data that they are (or
should be) built on. So again, in practice, when data and generalization/theory
conflict, the problem likely lies with some illegitimate bias tripping up the
induction from data to generalization/theory.
There is a less highfalutin reason that makes the two fold
myth above attractive to scientists. It lends them authority. On this view, scientists
are people who know how to see the world without illusion or distortion. They
are privy to a method that allows them to find the truth (or at least not be
distracted from it. So armed, scientists have a kind of epistemological
expertise that makes their opinions superior to those of the untrained. The
speculations of scientists are grounded in and regulated by the facts, unlike
the theories (and prejudices) of the unwashed. Observe that here the scientific
opinion is grounded in reality, and is not just one opinion among many. Being
scientific endows legitimacy. Being non-scientific removes it.
This thnking is a holdover from the old demarcation debates
between science and non-science (i.e. religion, ethics, prejudice, etc.) that
the Positivists loved to engage in. Within philosophy proper the idea that
there exists a sharp demarcation between science and non-science is not well
regarded anymore. In fact, it has proven very difficult to find any
non-circular ways of establishing what belongs on either side of the divide. But
scientists don’t always hold the wisdom of philosophers in high regard,
especially when, IMO, it serves to puncture their self-regard and forces them
to reconsider the degree to which their scientific credentials entitles them to
automatic deference in the public sphere. Most everyone enjoys the deference
that legitimate authority confers, and science’s access to such revolves around
the combo of (i) and (ii) above. The bottom line is that the myth buttresses a
very flattering view: scientists think more clearly and so see better because
their views are based in the facts and so deserve deferential respect.
So here are two reasons that the founding myth has proven so
strong. But there are other reasons too. We tend to tell our stories (at least
our mythical ones) about how science advances largely in terms that fit this
picture. A recent short Aeon paper
discusses one such founding myth involving that great scientific hero Galileo
and the Copernican world view. Btw, I am one of those that count both as heros.
They really were pretty great thinkers. But as this
paper notes, what made them such is not that they were unafraid to look at
the facts while their opponents were mired in prejudice. Nope. There was a real
debate, a scientific one, based on a whole series of interacting assumptions,
both empirical and theoretical. And given the scientific assumptions of the
time, the push back against Galileo’s Copernicism was not irrational, though it
proved to be wrong.[3]
The hero of the short piece is one Johann Locher. He was a
Ptolemeian and believed that the earth was the center of the universe. But, he
made his case in purely scientific terms, the biggest problem for the
Copernican vision being the star-size problem which it took some advances in
optics to square away. But, as the piece makes clear, this is not the view we
standardly have. The myth is that opposition to Galileo/Copernicus involved
disregard for the facts driven by religious prejudice. This convenient account
is simply false, though it part of the reason it became standard is Galileo’s
terrific popular polemic in his Dialogues
Concerning the Two Chief World Systems.
Christopher Graney, the author of the short piece, thinks
that one baleful result of the scientific caricature Galileo unleashed is
today’s science skepticism. He believes that today’s skeptics “…
wrap themselves in the mantle of Galileo, standing (supposedly) against a
(supposedly) corrupted science produced by the ‘Scientific Establishment’” (3).
This may be so. But I doubt that this is the only, or most important problem
with the myth. The real problem (or another problem) is that the myth sustains
a ghostly version of the demarcation criterion among working scientists. Here’s
what I mean.
As I’ve noted before, there is a general view among
scientists that data trumps, or should trump, theory. The weak version of this
is unassailable: when data and theory clash then this constitutes a prima facie
problem for theory. But this weak version is compatible with another weak view:
when data and theory clash this constitutes a prima facie problem for the data.
When there is a clash, all we know, if the clash is real, is that there is
either a problem with the theory or a problem with the data and that is not
knowing very much. No program of action follows form this. Is it better to
drop/change the theory to handle the data or to reanalyze the data to save the
theory? Dunno. The clash tells us nothing. However, due to the founding myth,
the default view is that there is something wrong with the theory. This view,
as I’ve noted, is particularly prevalent in linguistics IMO and leads the field
to dismiss theory and to exalt description over explanation. So, for example, missing
a data point is considered a far worse problem than having a stilted
explanation. Ignoring data is being unscientific. Eschewing explanation is just
being cautious. The idea really is that facts/data have an integrity that
theories do not. This asymmetric attitude is a reflection of Science’s founding
myths.
So where does this leave us? The aim of science is to
understand why things are as they are. This involves both data and theory in
complex combinations. Adjudicating between accounts requires judgment that
rarely delivers unequivocal conclusions. The best we can do is hold onto two
simple dicta and try to balance them: (i) never believe a theory unless
grounded in the facts and (ii) never believe a fact unless grounded in a
theory. It is the mark of a truly scientific temperament, in my view, that it
knows how to locally deploy these two dicta to positive effect in particular
circumstances. Unfortunately, doing this is very difficult (and politically it
is not nearly as powerful as holding the first of these exclusively). As Graney
notes, “science has always functioned as a contest of ideas,” not just a
contest of competing observations and data points. Facts (carefully curated)
can tell us how things are. But scientific explanation aims to explain how
things must be, and for this, facts are not enough.
[1]
By this I mean a substantive set of precepts rather than cheers of the sort “do
your best in the circumstances at hand,” or as Percy Bridgeman said “use your
noodle and no holds barred.”
[2]
The reply to this is well known: a theory is responsible for relevant data and what exactly counts as
relevant often requires theory to determine. But I put such niceties aside
here.
[3]
A most amusing discussion of this period can be found in Feyerabend’s writings.
He notes that there were many reasons to think that looking through a telescope
was hardly an uncontroversial way of establishing observtions. He is also quite
funny and does a good job, IMO, of debunking the idea that a non-trivial SM
exists. By non-trivial I intend something other than “do your best in the circumstances.”
No comments:
Post a Comment