From: lambert@spectrum.cs.unsw.oz.au (Tim Lambert)
Subject: Re: NSW homicides
Organization: none
	<1992Sep26.193758.11315@CSD-NewsHost.Stanford.EDU>
	<LAMBERT.92Oct1003841@henna.spectrum.cs.unsw.oz.au>
	<1992Oct3.003654.18954@CSD-NewsHost.Stanford.EDU>
Date: 6 Oct 92 17:03:52

>>>>> On Sat, 3 Oct 1992 00:36:54 GMT, andy@SAIL.Stanford.EDU (Andy Freeman) said:
> (Tim Lambert) writes:
>>I thought that Andy's problem was that he could not add together 1 and
>>1.  It turns out that he doesn't know the meaning of the word "parameter".

> Nope, Andy's right again.

Andy is wrong this time, was wrong the time before and will no doubt
get it wrong again if he follows up to this article.  Might I
recommend a career in economics or weather forecasting?

Even if we use Andy's non-dictionary definition of the word "parameter"
(which seems to be "a choice made or not made and somehow associated with
the model") he is wrong.  I will use "Andy-parameter" below to refer
to Andy's definition, and "parameter" when I want the dictionary
definition.

>> (Andy Freeman) said:
>>> There's the "nothing else happened" parameter.
>>
>>Andy, please look up the word "parameter" in a dictionary and try to
>>post something sensible next time.

> Andy's posts have been extremely sensible.

In a surreal kind of way.  What on earth do you mean by 'the "nothing
else happened" parameter"?  Seems you want to count the fact that
there are only two parameters to the model and not more as an
Andy-parameter.

>   The model has parameters and the fitting has parameters.  Some of
> those parameters are common to both, and some aren't.

Which Andy-parameters belong to the model, which to the fitting and
which to both.

>   When discussing the whole, we shouldn't ignore the
> fitting-specific parameters, even though Lambert's "my model is so
> much simpler" argument does.

By "fitting-specific parameters", I assume you mean the
Andy-parameters of the starting and ending years for the data.  These
Andy-Parameters apply equally to both models, so we have:

Model                        Parameters   Andy-parameters
1. Abrupt change in 1920              2               2+n
2. Decline starting in 1910           4               4+n

either way, the abrupt change model is simpler.

>>> There's the choice of years to ignore (or not) parameter.
>>
>>Andy implies here that the years chosen for the analysis (1911-30)
>>were chosen to give the model a better fit.  This is not true.

> No, Andy didn't imply anything about the reasons for data selection,
> but that it was done, that other choices were possible, that data
> selection was a parameter, a choice made.

Data selection is an Andy-parameter that applies to both models.

>>> The point is that Lambert both misrepresents his model's simplicity
>>> and the meaning of that simplicity.
>>
>>Do go on Andy.  How have I misrepresented the meaning of that
>>simplicity?

> Lambert has claimed that his "step" model is to be preferred over more
> complex models because it fits the data adequately.  Yet, once we
> count even some of the factors involved in the process of applying
> that model to the data, factors which he doesn't count, the relative
> simplicity is less than he claimed.

Model 2 has twice as many parameters as model 1.  If we look at the
ratio of the number of Andy-parameters: (4+n)/(2+n) we can get a value
anywhere between 2 and 1, since n can be anything Andy wants it to be.
This only proves that ratios of Andy-Parameters are useless.

> The step model is for a reality where a significant number of people
> in Oz decided on a given day that they wouldn't kill with the guns
> that they already owned.  None of them became convinced at other times
> because of the law, or changed their minds.  Curious.  It is for a
> reality where nothing else of consequence happened during the relevant
> time period.

Strawman.  Just because you can construct an implausible explanation where
gun-control caused the decline, it does not follow that all such
explanations are implausible.

> Yes, the "nothing else happened" assumption presents a problem with
> trying to use statistical methods; it simply isn't true for long
> enough to gather "enough" data.  The known-to-be complex reaction of
> complex organisms also come into play here.

"The world is complicated, so simple models can't fit the data".
Ahem.  The simple model DOES fit the data.  Huff and puff all you
want, you can't get away from that fact.

Tim
Newsgroups: talk.politics.guns