The world is not enough: pleading against the data gap postulate for building interdisciplinary research actions.
Résumé
« We have not enough data about that ». This is often the answer one modeler may encounter. The common answer the modeler think about is "ok, but then? We can do something ugly, fuzzy, and not precise but clearly, the mistake will be lower than not putting the process because of the lack of data". Even more, the "not-enough-data" assertion is theoretically always valid: whatever the issue, data will be lacking. Where to position the limit after which one can say "ok, let us combine what we know to see it has a meaning", i.e. try to build a theory, a model of the reality for answering a question? More generally, our purpose in this communication is to argue that asking "how can we model without all the necessary information?" means actually ""how can we model?". One may consider that modelling is the last avatar of the less known and less visible and valorizing second part of research: the construction and creation of theories from data and information scientists have previously collected. This demarche (or modality of inference), often called "inductive" in the most accepted hypothetico-deductive research approach, i.e. deduction, induction and abduction (Blecic & Cecchini, 2008, p. 539-540), implies to combine altogether "elements", pieces of science along a plan that may enlighten an issue.
Domaines
Méthodes et statistiques
Origine : Fichiers produits par l'(les) auteur(s)
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