Hi All
I tried to reply before but I wasn't a member of the list so
apparently it didn't get through. Thanks to Chris Douce for adding
me, so this reply should work.
On 28/11/2009, at 7:43 AM, Raymond Lister wrote:
On Fri, 27 Nov 2009, Richard O'Keefe wrote:
Anthony Robins
http://www.cs.otago.ac.nz/department/staff.php?name=Anthony%20Robins
recently noticed
http://www.cs.otago.ac.nz/staffpriv/anthony/publications/pdfs/RobinsLEM.pdf
Thanks to Richard for mentioning my paper on this. But there are some
minor revisions to be made before it is published, so please don't
quote the version on my web page quite yet. I'll put the final
version up next week some time.
And G'day Ray, long time no see!
Third, I regard Anthony's model as a good counter argument to the
conventional "geek gene" argument, but if I ignore the "geek gene"
argument and merely examine Anthony's model in isolation, then I
suspect that the number of free parameters in his model means that
you can get any distribution you want, by a suitable choice of
parameter values.
I don't think it has an overabundance of free parameters. Given some
pretty minimal assumptions that generate a normal distribution (M0),
it requires exactly one additional parameter (an offset, M1) to turn
the normal distribution into an anti-normal / bimodal one. This basic
model can only vary the "flatness" of the distribution on a continuum
from sharply peaked at the centre to normal to flat to bimodal to
sharply peaked at the extremes (and, of less interest from my point of
view, it can move the centre of the distribution). After that one
further parameter shapes the curve at the top or bottom end, but I
still don't think that you can quite get "any distribution you want"
out if it!
I presented one particular version in the paper, but the actual effect
is very robust. As Richard described it: "Roughly speaking: a series
of topics where failure at any point makes later failure more likely,
success makes later success more likely" - almost any reasonable
interpretation and implementation of this concept can turn a normal
distribution into an anti-normal / bimodal one.
As to what, if anything, the model means in educational terms, I make
the case in the paper. But Ray's certainly right that there's plenty
more to do - I welcome his suggestions as to where to look next, and
all other suggestions too...
Cheers
Anthony
Anthony Robins =======================================================
Computer Science [email protected]
University of Otago ph: +64 3 4798314
Dunedin fax: +64 3 4798529
NEW ZEALAND
http://www.cs.otago.ac.nz/staff/anthony.html
