There are many definitions of model-based sciences, which have a different philosophical meaning. This is due to the fact that the signification of the term ‘model’ is ambiguous: some of these theories may be based only on one kind of models and are unable to integrate in their field the other ones. I will give here two examples of model-based theories, but you can find many other ones.
The semantic theory of models, more recently called a ‘model-based’ view of science attacked the empirical explanatory models, which dominated the philosophy of science before the 1960’s, and promoted formal explanatory models during the following decade. Even if various versions of this approach differ (Patrick Suppes, Frederick Suppe, Bas van Fraassen, etc.), it continues to be developed nowadays. In this approach models, as abstract representations of some portion of the world which are different from empirical laws, are the central element of scientific knowledge. For 21st century researcher, computer modelling will permit the statement, manipulation, and evaluation of more and more complex theoretical models, as Thomas Burch (2017) said. But how in this case identify the relations between the theoretical model and the empirical observations, and test the fit of a simulation model? There is a real danger to construct theoretical models without any relationship with observed data and no way to verify this relationship.
The mechanistic view, which had been mainly developed for biological sciences during the 1990’s, is also considered as a model-based science. Again various versions of this approach differ (William Bechtel, Carl Carver, Stuart Glennan, etc.), but its development nowadays is increasing not only for biological sciences but for social sciences. A more recent version of this view is given by Robert Franck (2002) as the functional-mechanistic approach. As the semantic view the mechanistic theory of models rejects the empirical explanatory approach, and may appear as similar. But, while for the semantic approach a theory is a formal system empty of any empirical content, the mechanistic one infers, from the sustained observation of some property of nature, its functional structure –in classical terms the axiom, form, principle or law- which rules the process generating this property, and without which this property could not come about as it does. By focusing on the mechanism, generating a social property, the functional structure is treated independently of the causal structure, and may be generalized. We used this approach, with other researchers, in a recent paper on model-based demography (2017).
Under this question, I would like to discuss here the different model-based theories, their main aims, and the use of the term model.
References
Burch T. (2017). Model-based demography. Springer.
Courgeau D., Bijak J., Franck T., Silverman E. (2017). Model-based demography: towards a research agenda. In Agent-based modelling in Population Studies, Grow A., van Bavel J. eds., Springer.
Franck R. ed. (2001). The explanatory power of models. Kluwer Academic Publishers.
Mäki U. (2001). Models: philosophical aspects. In International Encyclopedia of the Social & Behavioral Sciences, Smelser N.J., Baltes P.B. eds., Elsevier Ltd.
@Stefan
Thank you for your very interesting answer. Yes I think, like you, that the introduction of models complicated the classical-logical notion of ‘theory’. I will even go further than you and say that these notions are antagonistic. Let me explain my position and I hope that you will tell me if you agree or not with it.
The first way, which you call the classical-logical perspective, is issued from Hume’s notion of ‘sense data’ (1748). For him: ‘It seems a proposition, which will not admit of much dispute, that all our ideas are nothing but copies of our impressions, or, in other words, that it is impossible for us to think of anything, which we have not antecedently felt, either by our external or internal senses. I have endeavoured to explain and prove this proposition, and have expressed my hopes, that, by a proper application of it, men may reach a greater clearness and precision in philosophical reasonings, than what they have hitherto been able to attain. Complex ideas may, perhaps, be well known by definition, which is nothing but an enumeration of those parts or simple ideas, that compose them.’ This proposition had been developed by the circle of Vienna which tried to reduce scientific thinking to a lower degree of confirmation, the protocol sentences, as you call them. They thought that our immediate experience and perception are the ultimate ground for knowledge (Carnap, 1932). This approach will have no use of models as it at the lower degree of confirmation. I will not develop it here, but I will only say that after a long debate, Hempel (1950) recognized that it was “useless to continue to search for an adequate criterion of testability in terms of deductive relationships to observation sentences”. So that ‘sense data’ are not to be taken as the foundation of scientific explanation.
The second way was formulated by Bacon (1620): “For the sense by itself is a thing infirm and erring; neither can instruments for enlarging or sharpening the senses do much; but all the truer kind of interpretation of nature is effected by instances and experiments fit and apposite; wherein the sense decides touching the experiment only, and the experiment touching the point in nature and the thing itself.”. For him it is not ‘sense data’ which lead us to knowledge but an interpretation of nature he called induction. Induction is not taken here in the sense of generalization from particular facts, which Bacon rejects, but on the sense of a modern scientific way of thinking, rising from a meticulous observation of studied facts to the “formation of ideas and axioms by true induction”. This method of induction consists of discovering the principles of natural or social properties by way of experimentation and observation. The Baconian induction rests on the requirement that without these principles the properties observed would be different Such a method uses models as the semantic approach or the mechanistic view, I shortly presented in my introduction.
References
Bacon, F. (1620). Novum Organum, London: J. Bill.
Carnap R. (1932). Die Physikalische Sprache als Universalsprache der Wissenchaft, Erkenntis, 2.
Hempel 1950, “Problems and Changes in the Empiricist Criterion of Meaning”, Revue Internationale de Philosophie, 4.
Hume D. (1748). Philosophical Essays Concerning Human Understanding.
I think that model-based theories are ideas which substitute realities. This could have interest in theoretical exploratory research but less interest for real and objetive research.
@Mariano
You wrote: “I think that model-based theories are ideas which substitute realities. This could have interest in theoretical exploratory research but less interest for real and objective research”.
For me, your answer may be true if you are undertaking empirical studies or if you agree with the classical-logic paradigm, which I described in my last answer. However it is widely agreed that the empirical studies would be better aimed were they based on firmer theoretical underpinnings, and I have shown in my previous answer that the classical-logic paradigm, based on ‘sense data’ may not be taken as the foundation of scientific explanation.
If you want to understand the structure of a phenomenon you will need a more theoretical basis by different modelling practices: statistical models and mathematical models, conceptual models, diagrams and maps, machines, artificial networks and computer modelling.
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