I've become interested in something called defeasible reasoning of
late.  I rather like the idea because it lets us argue from the
perspective that one might be wrong.  I can't get any moral authority
from golden salamanders, so I'm interested in defeasible obligations,
which give rise to defeasible inferences about what we are, all things
considered, obliged to do.  Thus I think we should act as though
mankind is screwing up big-time and get more control of global
warming, even if the evidence eventually turns out to have been
misinterpreted.  The ideas are not new.   David Ross, in 1930,
discussed the phenomena of prima facie obligations. The existence of a
prima facie obligation gives one good, but defeasible grounds, for
believing that one ought to fulfill that obligation. When formal
deontic logic was developed by Chisholm and others in the 1960s, the
use of classical logic gave rise to certain paradoxes, such as
Chisholm's paradox of contrary-to-duty imperatives. These paradoxes
can be resolved by recognizing that the inference from imperative to
actual duty is a defeasible one.
Prediction always involves an element of defeasibilty. If one predicts
what will, or what would, under some hypotheis, happen, one must
presume that there are no unknown factors that might interfere with
those factors and conditions that are known. Any prediction can be
upset by such unanticipated interventions. Prediction thus proceeds
from the assumption that the situation as modeled constitutes a closed
world: that nothing outside that situation could intrude in time to
upset one's predictions. In addition, we seem to presume that any
factor that is not known to be causally relevant is in fact causally
irrelevant, since we are constantly encountering new factors and novel
combinations of factors, and it is impossible to verify their causal
irrelevance in advance. This closed-world assumption is one of the
principal motivations for McCarthy's logic of circumscription
(McCarthy 1982; McCarthy 1986).

Now this may be a bit clumsy, but I would hope all could see (say)
that Chris and I could agree on what we ought to try to do about
"global warming" based on a shared agreement that are arguments are
defeasible (and possibly bullshit ridden).  This is an altogether
different stance in argument than points scoring, ad hominem and the
rest.  One might have to be careful in language when religious figures
with surgical instruments are around, but circumscription allows the
possibility that one's opponents may be right and takes into account
what we don't currently know and that our opinions change in the flow
of time forward..  I know I do not have a second sister, due to the
absence of one - but would be convinced to change my mind should one
appear.  I know there is no 10.55 a.m. train into town, as none is
listed.  I have never knowingly sensed dark matter and have worked
without knowing about it.  My work might well take a different tack if
the LHC finds the stuff.  The point, I guess, is that we don't know
much about the nature of the argument we so readily enter into, and
how restricted it often is.  I believe we could have much deeper,
multi-voiced arguments about what we should do and would find one very
loud, censoring tone preventing us taking circumspect action - our
primitive and brutal notion of what an economy is.

I doubt anyone here would want to read these papers, but some might be
interested to note the area they come from.


McCarthy, John M. and Patrick J. Hayes, 1969, “Some Philosophical
Problems from the Standpoint of Artificial Intelligence”, in Machine
Intelligence 4, B. Meltzer and D. Mitchie (eds.), Edinburgh: Edinburgh
University Press.
–––, 1977, “Epistemological Problems of Artificial Intelligence”, in
Proceedings of the 5th International Joint Conference on Artificial
Intelligence, Pittsburgh: Computer Science Department, Carnegie-Mellon
University.
–––, 1982, “Circumscription — A Form of Non-Monotonic Reasoning”,
Artificial Intelligence, 13: 27-39, 171-177.
–––, 1986, “Application of Circumscription to Formalizing Common-Sense
Knowledge”, Artificial Intelligence, 28: 89-111.

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