[OPE-L:8700] Re: Exchange, value

From: Michael Eldred (artefact@t-online.de)
Date: Tue Apr 01 2003 - 15:18:35 EST

Cologne 01-Apr-2003

Re: [OPE-L:8697]

clyder@gn.apc.org schrieb Tue, 1 Apr 2003 10:46:56 +0100:

> Quoting Andrew Brown <Andrew@lubs.leeds.ac.uk>:
> > Hi Michael,
> >
> > re 8687:
> >
> > Use
> > value is a necessary *condition* for system-wide exchange, but so
> > are many, many things (e.g. humanity, matter, conducive weather,
> > etc.): your point ii provides no argument that use value should be
> > privileged over any other condition, as far as I can see. The point is
> > that these conditions have all been entirely abstracted from in
> > exchange, so cannot explain exchange value. Only labour is left
> > (the quantity of SNLT is not entirely abstracted from in exchange
> > though proportionality obviously doesn't hold) and the labour that is
> > left is highly peculiar (defining the CMP at the most abstract level),
> > since all natural materiality has been drained from it.
> Whilst not wanting to detract from your general argument, with
> which I agree, I think that it may be worth bringing out another
> property of the underlying substance of value: that it must be
> a scalar quantity. As such use values, being distinct are obviously
> ruled out, but by itself this does not establish that labour is
> that scalar.
> In principle that scalar could be something else - for example
> the energy input required to make a commodity. The fact that
> energy input turns out to correlate relatively poorly with exchange
> value when compared to labour input is an empirical fact -
> not something we can establish at the level of a purely logical
> argument.
> In principle one could also treat the Sraffian basic commodity
> as the scalar input that determines values - and there is a real
> and very subtle insight in Sraffas proposal here.

I think this kind of statistical thinking which searches for correlations
between factors in data in order to uncover surmised causal relations
exemplifies just how little the phenomena themselves are and have been taken
into view.

For this mathematical scientific approach to work, from the outset it has to
be presupposed that there are linear mathematical relations among the
variables. This is the precasting of the situation requiring causal
explanation. The statistical analysis of data selected in line with this
precasting, usually under highly simplifying assumptions which make the
phenomena amenable to quantification, then decides whether the assumption of
a correlation was justified or not. The statistical precasting cannot see
anything else and presupposes all that seems obvious.

This is entirely different from a phenomenological approach in which it's
the thinker's own lived experience of simple, obvious phenomena that is
opened up to questioning.

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