III Pre-Specific Modeling: Computational Machines in a Coexistence with Concrete Universals and Data Streams

Abstract

We discuss that how the majority of traditional modeling approaches are following the idealism point of view in scientific modeling, which follow the set theoretical notions of models based on abstract universals. We show that while successful in many classical modeling domains, there are fundamental limits to the application of set theoretical models in dealing with complex systems with many potential aspects or properties depending on the perspectives. As an alternative to abstract universals, we propose a conceptual modeling framework based on concrete universals that can be interpreted as a category theoretical approach to modeling. We call this modeling framework pre-specific modeling. We further, discuss how a certain group of mathematical and computational methods, along with ever-growing data streams are able to operationalize the concept of prespecific modeling.Therefore, the first motivation of this present essay is to find a unifying (abstract) perspective for assessment of different (inter-disciplinary) modeling approaches, while keeping the diversities. We think toward this aim we need to investigate the mathematical and philosophical grounds of scientific modeling. Markov'(

StationRank: Aggregate dynamics of the Swiss railway

“…Existing parametric tools, such as agent-based models [ 1 ], have been applied to many engineering problems related to complex systems, including analysis of public transportation networks (PTN). Nevertheless, their success relies on the acceptance of a predefined set of properties [ 2 ], whose gradual increase in pursuit of realism is bound by the curse of dimensionality [ 3 ]. On the other hand, purely stochastic models implicitly capture complexity in a probabilistic manner.…”