Connectionists: Best practices in model publication

[Brad Wyble]

Thank you Mark, I hadn't seen this paper.  She includes this other point
that should have been in my list:

"From a practical point of view, as noted the time required to build
and analyze a computational model is quite substantial and validation may
require teams. To delay model presentation until validation has occurred
retards the development of the scientific field. "  ----Carley (1999)


And here is a citation for this paper.
Carley, Kathleen M., 1999. Validating Computational Models. CASOS Working
Paper, CMU

-Brad




On Mon, Jan 27, 2014 at 9:48 PM, Mark Orr <mo2259 at columbia.edu> wrote:

> Brad,
> Kathleen Carley, at CMU, has a paper on this idea (from the 1990s),
> suggesting the same practice. See
> http://www2.econ.iastate.edu/tesfatsi/EmpValid.Carley.pdf
>
> Mark
>
> On Jan 27, 2014, at 9:39 PM, Brad Wyble wrote:
>
> Dear connectionists,
>
> I wanted to get some feedback regarding some recent ideas concerning the
> publication of models because I think that our current practices are
> slowing down the progress of theory.  At present, at least in many
> psychology journals, it is often expected that a computational modelling
> paper includes experimental evidence in favor of  a small handful of its
> own predictions.  While I am certainly in favor of  model testing, I have
> come to the suspicion that the practice of including empirical validation
> within the same paper as the initial model is problematic for several
> reasons:
>
> It encourages the creation only of predictions that are easy to test with
> the techniques available to the modeller.
>
> It strongly encourages a practice of running an experiment, designing a
> model to fit those results, and then claiming this as a bona fide
> prediction.
>
> It encourages a practice of running a battery of experiments and reporting
> only those that match the model's output.
>
> It encourages the creation of predictions which cannot fail, and are
> therefore less informative
>
> It encourages a mindset that a model is a failure if all of its
> predictions are not validated, when in fact we actually learn more from a
> failed prediction than a successful one.
>
> It makes it easier for experimentalists to ignore models, since such
> modelling papers are "self contained".
>
> I was thinking that, instead of the current practice, it should be
> permissible and even encouraged that a modelling paper should not include
> empirical validation, but instead include a broader array of predictions.
>  Thus instead of 3 successfully tested predictions from the PI's own lab, a
> model might include 10 untested predictions for a variety of different
> experimental techniques. This practice will, I suspect, lead to the
> development of bolder theories, stronger tests, and most importantly,
> tighter ties between empiricists and theoreticians.
>
> I am certainly not advocating that modellers shouldn't test their own
> models, but rather that it should be permissible to publish a model without
> testing it first. The testing paper could come later.
>
> I also realize that this shift in publication expectations  wouldn't
> prevent the problems described above, but it would at least not reward
> them.
>
> I also think that modellers should make a concerted effort to target
> empirical journals to increase the visibility of models.  This effort
> should coincide with a shift in writing style to make such models more
> accessible to non modellers.
>
> What do people think of this? If there is broad agreement, what would be
> the best way to communicate this desire to journal editors?
>
> Any advice welcome!
>
> -Brad
>
>
>
> --
> Brad Wyble
> Assistant Professor
> Psychology Department
> Penn State University
>
> http://wyblelab.com
>
>
>


-- 
Brad Wyble
Assistant Professor
Psychology Department
Penn State University

http://wyblelab.com
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