As the article you quoted says, "our scientific interest in a model lies ...
in the relation of the component parts of the model to component parts of
the world.*" Compare this with the earlier quoted statement that "the model
informs us about the reailty as it is defined by the model". The latter
admits no external referent, no grounding. The former requires it. So all
mathematical models are tautologies only insofar as they cannot be shown to
have some definable relation to and grounding in the physical world.
How to know if this connection and grounding is present? In the same way
that scientific hypotheses (also tautological if left to themselves, without
external referent) are tested: by seeing if the hypothesis correctly
predicts something in the physical world. Which takes us back to the
original question: if social/economic models cannot do this, then they are
(as you've shown here) effectively tautological, living in their own vacuum,
and thus what good are they?
In other words, without this level of predictive filtering to test whether a
model's components do relate to analogous parts of the world, mathematical
modeling is precisely on par with astrology -- in fact astrology is a good
example of a complex mathematical model that has at best a tenuous, if any,
connection to the physical world.
Mike Sellers
> -----Original Message-----
> From: News and discussion about computer simulation in the
> social sciences [mailto:[log in to unmask]] On Behalf Of Dan Olner
> Sent: Thursday, June 11, 2009 9:47 AM
> To: [log in to unmask]
> Subject: Re: [SIMSOC] what is the point?
>
> From the paper I included before - all mathematical models
> are tautologies:
>
> "The code that runs a model can be expressed in mathematical
> equations, and it is mathematical element that makes a model
> tautological. // Models will necessarily be tautological
> with respect to their component parts. Because it is possible
> to reduce a model to a set of mathematical equations this
> must be true. But our scientific interest in a model lies not
> merely in the equations as such, *but in the relation of the
> component parts of the model to component parts of the world.*"
>
> So, as with 1 + 2 = 3, the hard lifting is not the model
> itself, but in demonstrating how known elements (1, 2) and
> causal laws ( + ) and the tautological result, 3, relate to
> real-world phenomena. Example from the same paper:
>
> "If the presence of the ingredient elements ('1 + 2') is
> uncontroversial but the result ('3') is either not known or
> not commonly associated with these ingredients, then the
> model makes a prediction: that the result element will arise
> from the ingredients."
>
> Last quote: "When communicating their model and the
> implications which they derive from it, the obligation is
> upon the modeller to deŻne which features of the model are
> supposed to correspond to which features of the world, and
> what the purposes of constructing a model like this are. Only
> when a proposal about the theoretical content of the model is
> offered can the value of the model be evaluated."
>
> So - whether or not it's akin to astrology depends on the
> theoretical case made about how the model corresponds to the
> real world. The tautological nature of modelling doesn't mean
> this is so, however.
>
> Dan
>
> ________________________________
> From: News and discussion about computer simulation in the
> social sciences [[log in to unmask]] On Behalf Of Mike
> Sellers [[log in to unmask]]
> Sent: 11 June 2009 17:21
> To: [log in to unmask]
> Subject: Re: [SIMSOC] what is the point?
>
> This line of reasoning as articulated in your post-- hinging
> in particular on "the model informs us about the reailty as
> it is defined by the model" -- is strangely tautological, and
> thus works for any explanatory model. It puts social and
> economic modeling on the same footing as say astrology.
>
> Not what I'd call a strong case for the utility of such modeling.
>
> Mike Sellers
>
> ________________________________
> From: News and discussion about computer simulation in the
> social sciences [mailto:[log in to unmask]] On Behalf Of
> Loet Leydesdorff
> Sent: Wednesday, June 10, 2009 10:55 PM
> To: [log in to unmask]
> Subject: Re: [SIMSOC] what is the point?
>
> The main function of a model is, in my opinion, to reduce the
> complexity of the modeled system in the representation.
> Evolutionarily, this makes the modeling system anticipatory.
> An anticipatory system can be defined as a system which
> entertains a model of itself (Rosen, 1985).
>
> When the system is not an individual mind, but a social
> system, the discourse can use the model for improving its
> anticipatory capacities. The model enables us to codify the
> communication, and therefore to process more complexity and
> learn faster. However, potentially orthogonal perspectives on
> the "reality" -- that is, the modeled system -- remain
> possible. Because of these different angles in the
> projection, one cannot even expect the "reality" to be the
> same from different perspectives. The model informs us about
> the reality as it is defined by the model ("strong
> anticipation", Dubois, 1998). Of course, the models are not
> completely closed (as Luhmann assumed), because the
> discourses are able to translate among models and import
> insights from one another in the evolutionary competition
> among discourses for solving problems.
>
> For example, models in neo-classical economics stand
> analytically orthogonal to models in evolutionary economics
> because in the former the market is considered as the
> equilibrating mechanism at each moment, while in the latter
> the markets are continuously upset over time.
>
> Reduction of uncertainty perhaps is the overarching concept
> between the concept of explanation and this conceptualization
> of models as functional to enhancing learning. Whether a
> model was further developed, informs us about the further
> development of the modelling system(s) (that is, the relevant
> discourse(s)).
>
> Best wishes,
>
>
> Loet
>
> ________________________________
> Loet Leydesdorff
> Amsterdam School of Communications Research (ASCoR),
> Kloveniersburgwal 48, 1012 CX Amsterdam.
> Tel.: +31-20- 525 6598; fax: +31-20- 525 3681
> [log in to unmask] <mailto:[log in to unmask]> ;
> http://www.leydesdorff.net/
>
>
> ________________________________
> From: News and discussion about computer simulation in the
> social sciences [mailto:[log in to unmask]] On Behalf Of
> Mike Sellers
> Sent: Wednesday, June 10, 2009 8:39 PM
> To: [log in to unmask]
> Subject: Re: [SIMSOC] what is the point?
>
> If models of economic policy are fundamentally unable to at
> some point predict the effects of policy -- that is, to in
> some measure predict the future -- then, to be blunt, what
> good are they? If they are unable to be predictive then they
> have no empirical, practical, or theoretical value. What's
> left? I ask that in all seriousness.
>
> Referring to Epstein's article, if a model is not
> sufficiently grounded to show predictive power (a necessary
> condition of scientific results), then how can it be said to
> have any explanatory power? Without prediction as a
> stringent filter, any amount of explanation from a model
> becomes equivalent to a "just so" story, at worst giving old
> suppositions the unearned weight of observation, and at best
> hitting unknowably close to the mark by accident. To put
> that differently, if I have a model that provides a neat and
> tidy explanation of some social phenomena, and yet that model
> does not successfully replicate (and thus predict) real-world
> results to any degree, then we have no way of knowing if it
> is more accurate as an explanation than "the stars made it
> happen" or any other pseudo-scientific explanation.
> Explanations abound; we have never been short of them. Those
> that can be cross-checked in a predictive fashion against
> hard reality are those that have enduring value.
>
> I know Epstein is a leader in this field (and may read here
> for all I know). But I have to say that his sixteen reasons
> are, to my eye, somewhat embarrassing as examples of lazy and
> self-justifying thinking. We must be careful to examine his
> assertions on their face, and not impute value on them based
> on the fallacious appeal to authority. Without going into a
> detailed review here, most if not all of his reasons to build
> models eventually lead back to the need for explanation built
> on predictive power. Possibly two -- discovering new
> questions and revealing the simple/complex nature of
> phenomena -- are not dependent on prediction and may lead to
> potential non-predictive value for modeling. The rest --
> perhaps most notably "offer crisis options in near-real time"
> and "promote a scientific habit of mind" are completely
> dependent on a model being able to predict real phenomena
> based on results obtained from a model.
>
> Please note that I am not saying that modeling has no value.
> Nor am I trying to avoid the inherently probabalistic nature
> of any predictions made (as with weather prediction, say,
> showing the path of a coming hurricane). And in some areas,
> such as agent-based models dealing with small numbers of
> individuals, or preferences based on emotion or culture,
> predictability is difficult to come by because there are so
> many hidden variables at work (there may even be a social
> equivalent to the Heisenberg Uncertainty Principle in
> physics, whereby the smaller number of agents you look at in
> greater and greater fidelity, the more probabalistic any
> resulting predictions must become).
>
> But the difficulty of creating even probabalistically
> predictive models, and the relative infancy of our knowledge
> of models and how they correspond to real-world phenomena,
> should not lead us into denying the need for prediction, nor
> into self-justification in the face of these difficulties.
> Rather than a scholarly "the dog ate my homework," let's
> acknowledge where we are, and maintain our standards of what
> modeling needs to do to be effective and valuable in any
> practical or theoretical way. Lowering the bar (we can
> "train practitioners" and "discipline policy dialogue" even
> if we have no way of showing that any one model is better
> than another) does not help the cause of agent-based modeling
> in the long run.
>
> Mike Sellers
>
>
>
>
> ________________________________
> From: James Millington [mailto:[log in to unmask]]
> Sent: Tuesday, June 09, 2009 7:55 PM
> To: [log in to unmask]; Mike Sellers
> Cc: James Millington
> Subject: Re: what is the point?
>
> For me prediction of the future is only one facet of
> modelling (whether agent-based or any other kind) and not
> necessarily the primary use, especially with regards policy
> modelling. This view stems party from the philosophical
> difficulties outlined by Oreskes et al.
> (1994)<http://dx.doi.org/10.1126/science.263.5147.641>,
> amongst others. I agree with Mike that the field is still in
> the early stages of development, but I'm less confident about
> ever being able to precisely predict future systems states in
> the open systems of the 'real world'. As Pablo suggested, if
> we are to predict the future the inherent uncertainties will
> be best highlighted and accounted for by ensuring predictions
> are tied to a probability. Model ensembles and Bayesian
> approaches will assist with this. Of course, there are
> reasons to model other than prediction of the future and I
> subscribe to many of the reasons Epstein gave recently:
> http://jasss.soc.surrey.ac.uk/11/4/12.html It would be
> interesting to see how other modellers would rank those
> reasons in terms of importance/frequency of sue. Maybe we
> could set up some kind of online poll to survey this. I think
> one less frequently discussed way in which modelling is
> useful is that it allows us to make explicit our
> implicitly-held models. Developing quantitative models forces
> us to be structured about our worldview - writing it down
> (often in computer code) allows other to scrutinise that
> model, something that is not possible if the model remains
> implicit (in our heads). Thus, models are useful as
> communication tools. If agent-based approaches provide a
> means to represent imperfect, heterogeneous actors from the
> bottom-up (in a way that traditional, analytical methods
> cannot) there are also possible benefits for their use to
> help citizens or stakeholders identify/understand the
> potential consequences of their individual actions in broader
> societal action. Furthermore, from a Geographer's
> perspective, this bottom-up aspect of agent-based models
> provide a means to investigate issues of 'place' and the
> significance of local spatial variability over (or in
> combination with) global drivers. James Millington -- Center
> for Systems Integration and Sustainability Michigan State
> University http://www.landscapemodelling.net
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