Humanist Discussion Group, Vol. 38, No. 145.
Department of Digital Humanities, University of Cologne
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[1] From: scholar-at-large@bell.net <scholar-at-large@bell.net>
Subject: Re: [Humanist] 38.143: a paradox? (54)
[2] From: Mcgann, Jerome (jjm2f) <jjm2f@virginia.edu>
Subject: Re: [Humanist] 38.143: a paradox? (19)
[3] From: Tim Smithers <tim.smithers@cantab.net>
Subject: Re: [Humanist] 38.143: a paradox? (98)
--[1]------------------------------------------------------------------------
Date: 2024-09-18 15:15:39+00:00
From: scholar-at-large@bell.net <scholar-at-large@bell.net>
Subject: Re: [Humanist] 38.143: a paradox?
Willard
I wonder if the zone you are seeking to access might be approached via a
triangulation.
Model, modeled & modeling
By analogy with translation where the translation and the translated are
instances of the (matrix) of translating.
Are you seeking to access a zone of potentials?
François
> On Sep 18, 2024, at 1:12 AM, Humanist <humanist@dhhumanist.org> wrote:
>
>
> Humanist Discussion Group, Vol. 38, No. 143.
> Department of Digital Humanities, University of Cologne
> Hosted by DH-Cologne
> www.dhhumanist.org
> Submit to: humanist@dhhumanist.org
>
>
>
>
> Date: 2024-09-18 05:08:06+00:00
> From: Willard McCarty <willard.mccarty@mccarty.org.uk>
> Subject: side by side
>
> Here's a question I am pondering and would like some help with.
>
> Much is written about modelling, a bit of it by me. But I am bothered by
> the built-in assumption that the role of the machine in this instance is
> to imitate the modelled object or process as closely as possible or
> practical. If, however, we juxtapose the computational machine as we
> know it to a human process or practice, neither to model the latter by
> the former nor to do a point-by-point comparison but to hold the two in
> mind in order to see what happens, what happens then? Where might one
> find a way to think about this situation?
>
> Comments welcome.
>
> Yours,
> WM
>
>
> --
> Willard McCarty,
> Professor emeritus, King's College London;
> Editor, Interdisciplinary Science Reviews; Humanist
> www.mccarty.org.uk
--[2]------------------------------------------------------------------------
Date: 2024-09-18 09:56:02+00:00
From: Mcgann, Jerome (jjm2f) <jjm2f@virginia.edu>
Subject: Re: [Humanist] 38.143: a paradox?
Dear Willard,
Is this indeed the widespread case?
But I am bothered by the built-in assumption that the role of the machine in
this instance is to imitate the modelled object or process as closely as
possible or practical.
I hope not. Your objection is of course entirely pertinent, and it’s especially
so when one is building computational models to process ωhat we rather
helplessly call analogue materials. Furthermore, “object or process” cites a
really crucial distinction. Despite what AI projects appear to assume (perhaps
not all AI projects?), no computational machine can be more than a prosthesis
for a biological machine (and it occurs to me just now to say, or
wonder, perhaps vice versa).
X
Jerry
--[3]------------------------------------------------------------------------
Date: 2024-09-18 08:30:09+00:00
From: Tim Smithers <tim.smithers@cantab.net>
Subject: Re: [Humanist] 38.143: a paradox?
Dear Willard,
I'd like to take exception to your assertion that when we
build and use a model, we make ...
"... the built-in assumption that the role of the
machine in this instance is to imitate the modelled
object or process as closely as possible or practical."
Making and using a good model is not, in my experience, nor in
what I teach PhDers, about imitating as closely as possible or
practical the subject to be modelled.
Making a model is about making modelling decisions, and these
involve deciding what of the subject you need to, or want to,
model, and further decisions about how to implement these
chosen [observable] aspects, qualities, or features, of your
subject, so that your model is good enough for your purpose;
for what you want to use your model.
The goodness of your model is a matter of how fit for purpose
it is, not how closely it imitates the subject being modelled.
Without having clear what the purpose of your model is, there
is no way to do good model making, or using. And, anyway,
there is usually no satisfactory way of knowing how closely
something imitates something else, even if it's supposed to.
Deciding this well takes lots of knowledge and understanding
of both the subject and thing that's supposed to imitate it,
and if we had all this knowledge and understanding, we
probably would not need a model of it, not for research at
least.
Models, for research, are instruments of investigation,
"epistemologically equivalent to the microscope and the
telescope," as Marcel Boumans (2012) nicely puts it. Models
are built from well chosen simplifications and idealisations
of the subject, as we known and understood it. Models are not
made from "similarities." Even if you think you have a good
definition of what "similarity" is, and, better, one with
which other people agree, it still takes lots of verified
knowledge and understanding of your subject to apply any tests
of your "similarity" notion, knowledge and understanding we
don't usually have. But to make and use good models we don't
need to do any of this similarity checking. What we do need
to do is to show that, and how, our model satisfies the
Modelling Relation well enough for our purpose. This is what
it takes properly to have a model of what we say it is a model
of for our purpose.
Here’s one way of putting all this which I like.
"A model is a representation of something by someone for
some purpose at a specific point in time. It is a
representation which concentrates on some aspects —
features and their relations — and disregards others. The
selection of these aspects is not random but functional:
it serves a specific function for an individual or a
group. And a model is usually only useful and only makes
sense in the context of these functions and for the time
that they are needed." -- Fotis Jannidis (2018)
Making and using a model is, in my experience, always an
iterative business: earlier modelling decisions, and
implementation decisions, are revised on the basis of what we
learn from verifying, validating, and using our model, and
thereby gradually discover what we need to simplify and
idealise of our subject, and how, to have ourselves a good
enough model for our investigations. It's a conversation with
our subject enabled by trying to model it in some useful way,
rather than by trying to observe it in some way, using a
microscope or telescope, for example. Which is also involves
a conversation.
This conversation, enabled by our model making and model
using, is like, I would say, your idea
"... to hold the two [your subject and your model] in
mind in order to see what happens ..."
There's no paradox here that I see.
-- Tim
References
1. Marcel J Boumans, 2012: Mathematics as Quasi-matter
to Build Models as Instruments, in: Dieks D, Gonzalez W,
Hartmann S, Stöltzner M, Weber M (eds), Probabilities,
Laws, and Structures. The Philosophy of Science in a
European Perspective, Vol 3, Chapter 22, pp 307–316,
Springer, Dordrecht.
2. Fotis Jannidis, 2018: Modeling in the Digital Humanities:
a Research Program?, a chapter in Historical Social
Research Supplement 31, pp 96-100, published by GESIS
DOI: 10.12759
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