Gary, in the 80’s, project managers
started using “earned value” estimates for completion of software projects
because the “percent complete” method always reached 90% in just a tiny
fraction of the development schedule!
All measurements are biased by the
observer. The above “earned value” method attempted to remove the bias by
measuring how project completeness estimates compared to other previous project
completeness estimates, then correlating the old “actual” with the estimated
Previously, the managers had used the “percent
complete” method, and quickly had red faces explaining to their customers how
the project got 90% complete in two months of an 18 month schedule, but only
crept up a percent or less a month after that.
Measurement is also subject to distortions
of many psychological kinds, not just the positive thinking ones. The AI guru’s
just had unusual kinds of distortions.
Rich AT EnglishLogicKernel DOT com
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Gary Berg-Cross
Sent: Monday, December 28, 2009
Subject: Re: [ontolog-forum] if
you cannot measure...
there are measurment and scale issues. Scoring methods, for such things as risk
analysis and investment strategies, typically do not make any allowance for
human (analysts) bias as they assign “scores”. There is rarely an objective
basis for this. Even
numerically equal amounts aren’t always phenomenologically the same to
humans. The phenomena of loss aversion shows that if we lose $100 it is not
compensated by a $100 windfall – we lose more satisfaction than we gain. Loss aversion is a built in cognitive bias.
On Mon, Dec 28, 2009 at 8:18 AM, FERENC KOVACS <f.kovacs@xxxxxxxxxxxxxx> wrote:
In the practical world of business, such
as in car manufacturing, there are proven methods of giving account of
multitudes as well as unary objects. Moreover, they have a way to assemble them
with methods to measure the number of mistakes/faults/rejects made in the
process which are crucial as feedback to improve the methods applied.
In the SW world created for the representation of knowledge people are more
interested in magic than reality in the sense that the do not reveal the mental
routes leading to the outcome. Knowledge representations are sorted
morphologically, that is alphabetically, because that is the only sort key
(order) mathematicians know besides numbers. This fallacy is echoed in
Kuhn's paper as well as by John Dewey.
The current theoretical approach to semantic analysis as used in AI or applied
MT is certainly nothing to write home about, especially, if you look at the
actual products in contrast to ambitions.
Biology is an experimental science and look what they get in other languages to
share by using "theoretical sound scientific solutions, just because
computing deals with numbers"
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