Abstract-The Modeling and Simulation (M&S) of complex systems leans on the collaboration between different actors coming from specific domains. These actors have to communicate through an efficient software in order to improve the M&S process. We therefore propose in this article a collaborative M&S software framework called DEVSimPY. We point out the use of DEVSimPy through a concrete case study: hydraulic network management.

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The modeling and simulation (M&S) of complex systems often requires models described at different levels of detail characterized by differences in abstraction hierarchies and/or time granularity. The discrete-event system specification (DEVS) is a framework based on mathematical systems theory that offers a computational basis for application of M&S to systems engineering and that has become widely adopted for its support of discrete-event, continuous, and hybrid applications. A fundamental representation of DEVS hierarchical modular model structures is the system entity structure (SES), which represents a design space via the elements of a system and their relationships in a hierarchical and axiomatic manner. As has been described in a number of publications, the SES supports development, pruning, and generation of DEVS simulation models. The goal of this paper is to propose an extension of SES in order to integrate both the concepts of abstraction hierarchies and time granularity into DEVS. This paper explains in detail: (i) the concepts of abstraction hierarchies and time granularity; (ii) the extension of SES in order to take into account these concepts; (iii) DEVS M&S of complex systems according to different levels of detail (abstraction hierarchies and time granularity); (iv) the use of a Python DEVS simulator (DEVSimPy) to implement the management of abstraction hierarchies and time granularity. A real case study is given to illustrate the proposed approach, and follow-on research needed to implement the concepts is discussed.

This paper presents a modeling and simulation approach in order to perform a generative analysis of folktales aimed at validating Claude Lévi-Strauss’ theory and method. To this aim, a discrete-event simulation is proposed. The simulation is based on the development of a set of discrete-event models dedicated to generating a set of folktales from an initial one, according to Claude Lévi-Strauss’ structural analysis based on symmetry and double twist transformations. This paper describes in detail how these discrete-event models have been implemented in the framework of the DEVSimPy software environment by using myths of Native American mythology and folktales of Corsican oral literature. The validation involved the following steps: (i) definition of a reference folktale (according to Claude Lévi-Strauss’ methodology) (ii) generation of a set of folktales by performing their own transformations (iii) generation of a graph allowing to analyze the links that have been created after performing a set of folktales transformations. Finally, the computational validation of Lévi-Strauss’s method is intended to ground a new research that may reformulate structural analysis and elaborate a neo-structural model of canonical formalization based on transformational morphodynamics. The aim is to conceptualize and measure recursively the structural dynamics and the recurrent patterns of current identity transformations in liberal democracies, especially in US and EU contexts where ethnic/racial divisions and migration challenges are becoming more acute than ever.

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For several years, we worked to improve a discrete events modeling formalism: called DEVS. Having defined a method to take into account the inaccuracies iDEVS, in this paper, we present the second part of our research work.Generally, our approach is to associate the DEVS formalism with an object class, which allows using it to new fields of study, and in our case fuzzy systems.This paper describes a new modeling methodology. It allows to modeling and to use fuzzy inference systems (FIS) with DEVS formalism in order to perform the control or the learning on systems described incompletely or with linguistic data. The advantages of this method are numerous: to extend the DEVS formalism to other application fields; to propose new DEVS models for fuzzy inference; to provide users with simple and intuitive modeling methods. Throughout this paper we describe the tools and methods which were developed to make possible the combination of these two approaches.

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