Mathematical models are constructed at the interface between practice, experience and theories. The function of models puts us on guard against the privilege granted to what is accepted as abstract and formal, and at the same time puts us on guard against a static and phenomenological conception of knowledge. The epistemology of models does not suppress in any way the objectives of science: only, a dogmatic conception concerning truth is removed, and dynamic and dialectical aspects of monitoring are stressed to establish the most viable model. The purpose of this paper is to examine hybrid methodologies (inductive-deductive) that may either propose hypothetical causal relations and seek support for them in field data or detect causal relations in field data and propose hypotheses for the relations detected.
The authors follow a dialectical analysis for a type of inductive-deductive model.
In this work, the authors present an inductive-deductive methodology whose practical result satisfies the Hegelian dialectic. The consequent implication of their mutual reciprocal integration produces abstractions from the concrete that enable thought. The real problem in this case is a given ontological system or reality.
The essential elements of the models – variables, equations, simulation and feedback – are studied using a dialectic Hegelian theory.
Usó Doménech, J.L., Nescolarde-Selva, J.A., Segura-Abad, L. and Gash, H. (2020), "A dialectical vision of mathematical models of complex systems", Kybernetes, Vol. 49 No. 3, pp. 938-959. https://doi.org/10.1108/K-01-2019-0032
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