Hi Mike,
you said:
Sorry for my delayed
response.
Quite
alright. It is difficult for me to respond quickly from time to time as I
am in a busy place myself. Nonetheless I do welcome you decision to
discuss cognitive patterns and I hope it gets a long run of
interest.
I would
not have used "cognitive" as the pattern word that drew me to
attend the first PLOP conference in 1994, though I did go hoping to find
such a topic of study. I had been, since 1974, using the name
"cultural patterns" or "reasoning patterns" for my
topic of pattern interest. In my local effort, culture's loss of
financial accountability drew me into my special interest in double entry
bookkeeping.
I didn't
know at the time that a proper double entry bookkeeping framework had
become extinct in software circles; I went expecting find experts. So,
naturally, I now wonder if my desire for discussion of cognitive patterns
might ask what I would have asked then: why did our ancestral cultures
place a strong emphasis on economic accountability that our present
culture believes it can ignore? My assumption here is that "societal
beliefs" are an equally important part of "cognitive
patterns" used by individuals.
> Do you believe that a
statistical model is an effective way to study similarity within and
among generative processes?
I think it is a good way to model pattern recognition and the cognitive
process.
My leading
into cognitive pattern discussion with a question on statistical
modelling is my way of introducing my belief that such a discussion calls
for a Kuhnian paradigm shift that my local experience has found is
essential to the discussion intellectual patterns in general.
Like some said before, and I
agree, in pattern recognition by humans there is uncertainty.
It is human to err,
but mostly to re-interpret, morph, change, or
forget.
My local
effort focuses on how one recognizing similarity within patterns that
form a generative process so that such "recognized patterns"
can be synthesized into a useful language. That, I believe, is a
different paradigm of reasoning than to study whether one has
differentiated the content of a pattern correctly, which may lend itself
to statistical study.
For
example, why has the present software culture abandoned the double entry
bookkeeping framework that served culture so well for 650 years? Has
electronic data processing rendered the old pattern unnecessary, or has
electronic data processing found the bookkeeping framework too difficult
to reproduce in current software languages? Or, given a cultural shift in
societal controls to a new breed of rule-makers, has our current culture
opted for the opium's delight in unaccountable behavior for whatever such
behavior might bring to its users that accountability would not bring?
In other words, there is only a
probability that human would know all the rules, agree with all the
rules,
and apply them correctly while recognizing (or even applying) a
pattern. The lack of certainty
in the cognitive process is certainly
statistical.
To
"know the rules," "to agree with the rules," and
"to apply the rules" are certainly three important parts of
cognitive study. But I want to bring in a fourth category of cognitive
pattern study: "to make the rules." Software, by
its very nature, is a rule-making process. Do know, Mike, that software
today is on one hand bringing great benefits to the local culture I live
in, while, on the other hand, it inflicts bone-crushing rules on select
persons in that culture. It is the group that is being harmed that I hope
my contribution to this discussion can bring to light. I want these
negative patterns brought to light simply because they are dangerous and
unnecessary bi-products of current software practices.
On the other hand, how to
do statistics and quantify them, is very hard, and I am not sure the
paper
has it right or even close, or that it can be done
right.
Here we
agree. The statistical analysis of cognitive patterns is difficult to
quantify as well as difficult to judge whether the statistical analyst's
data model is asking the correct questions.
>
how have you applied statistics in SCRUM,
Well, to be in the position to analyze Scrum patterns, I would have to be
an anthropologist attempting to
analyze whether Scrum had been applied to a culture. A sort of
reverse engineering social study of sorts,
where we could use Moreno socio-metrics, etc. Such studies have
been done, for example Coplien et. al.,
in finding their organizational pattern
language.
I take the
important short answer here to be "No!' (-:
>
what improved study mode might you recommend to one
who studies the art of generative change?
To experiment generative change. Take the patterns any patterns
in any subject and start using them.
Learn by doing and building, and trying things out. Share
experience and knowledge with others,
become part of an expert community,
etc.
In this
cognitive pattern discussion you have introduced, I will argue from time
to time that the vagueness of this final portion of your reply indicates
a bankruptcy in today's normal science. I live, as you know, in a
scientific community. That community, for lack of an alternative, is
applying increasingly powerful computers to statistical analysis, with
little in the way of social and environmental improvement. I see in their
belief pattern a need for a Kuhnian Revolution that moves the current
mathematically dominated normal science, to a new scientific state that
looks to patterns in software for better clues to patterns in human
cognition. Human cognition is after all is said and done, the foundation
of Social Science.
Dan
PS: for you information, here is a quote from Kuhn relative to Normal
Science versus the need for a scientific revolution:
Normal
science, the activity in which most scientists inevitably spend almost
all of their time, is predicated on the assumption that the scientific
community knows what the world is like. Much of the success of the
enterprise derives from the community's willingness to defend that
assumption, if necessary at considerable cost. Normal science, for
example, often suppresses fundamental novelties because they are
necessarily subversive of its basic commitments. Nevertheless, so long as
those commitments retain an element of the arbitrary, the very nature of
normal research ensures that novelty shall not be suppressed for very
long. Sometimes a normal problem, one that ought to be solvable by known
rules and procedures, resists the reiteration onslaught of the ablest
members of the group within whose competence it falls. On other occasions
a piece of equipment designed and constructed for the purpose of normal
research fails to perform in the anticipated manner, revealing an anomaly
that cannot, despite repeated efforts, be aligned with professional
expectation. In these and other ways besides, normal science repeatedly
goes astray. And when it does--when, that is, the profession can no
longer evade anomalies that subvert the existing traditions of scientific
practice--then begin the extraordinary investigations that lead the
profession at last to a new set of commitments, a new basis for the
practice of science. The extraordinary episodes in which that shift of
professional commitments occurs are the ones known in this essay as
scientific revolutions. They are the tradition-shattering complements to
the tradition-bound activity of normal science."
-The Structure of Scientific Revolutions, by Thomas
Kuhn.
***
The normal problem that I cannot get to fit into current
social science is the loss of rule-making in my local culture. I want
science to find a way for astute local citizens to resolve issues of
economic accountability that I see approaching a danger zone. I hope your
cognitive discussion thread can serve to illuminate this difficulty among
we local folks.
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