Discrete Event System Specification-based framework for modeling and simulation of propagation phenomena in social networks: application to the information spreading in a multi-layer social network

Abstract

<jats:p> The diffusion of information is defined as the communication process by which an idea or information spreads within a social system and impacts the behavior of social actors (individuals). The social interaction plays an important role in studying the propagation of information and how it influences people. When an informational event occurs, it can either die out quickly or have significant impact on a population. The interactions could be supported by physical proximity contact, remote collaboration, any type of social meetings, and some forms of verbal or written communication, depending on the situations. Institutions and firms search to understand and predict the impact of information propagation on individuals. Agent-based modeling is a powerful approach for studying such a collective process. However, existing models oversimplify the cultural attributes, the different types of links, and information content, despite the evidence of their central role in the diffusion process. </jats:p><jats:p> In this context, great benefits could be derived from the exploitation of an individual’s personality and cultural values in the diffusion models. In this paper, we describe a new architecture for an agent-based model using the DEVS (Discrete Event System Specification) framework and show how this architecture is flexible and can support the simulation of the dissemination process. In more detail, we define a set of models of individuals characterized by a set of state variables to represent the behavior of an individual and the individual’s network within a multi-layer social network. Then, we start by introducing the platform architecture, specifically designed to simulate message propagation in a multi-layer network. Finally, a military scenario of message diffusion during a stabilization phase is used to test our DEVS models on the platform and the relevancy of the simulation results. </jats:p>

Combining PDEVS and Modelica for describing agent-based models

“…Other DEVS-based approaches include its combination with cognitive modeling, the multi-agents model 33 or the application of the Cell-DEVS formalism, where each cell represents an individual agent. 34 Examples of applications can also be found in the works of Uhrmacher and Schattenberg, 35 Camus et al, 36 and Bouanan et al 37…”

“…Other DEVS-based approaches include its combination with cognitive modeling, the multi-agents model 33 or the application of the Cell-DEVS formalism, where each cell represents an individual agent. 34 Examples of applications can also be found in the works of Uhrmacher and Schattenberg, 35 Camus et al, 36 and Bouanan et al 37 On the other hand, multiple software tools have been developed to describe ABMs (cf. Kravari and Bassiliades 38 and Abar et al 39 for an extensive survey on ABM platforms and tools).…”

Action network: a probabilistic graphical model for social simulation

“…Action selection mechanism may be as simple as a set of basic “if-then” decision rules, but the set of rules implies a causality that may be difficult to elucidate in imperative forms. And increasingly, work in ABSS deploys a more complex mechanism such as BDI (Belief–Desire–Intention), 40,41 a cognitive architecture, 42 a game-theoretic framework, 43,44 discrete event system specification (DEVS) and its variants, 45,46 and diffusion equation. 47…”

Heuristic methods for synthesizing realistic social networks based on personality compatibility

“…First developed in the early 1980s, UCINet is a social network analysis application that calculates a variety of network metrics (Freeman 1988). UCINet includes functions for discovering cohesive subgroups in a network (Borgatti et al, 2014). An associated archive of social networks, represented as adjacency matrices, is maintained in the UCINet format (Freeman, 2009) (Freeman, 2016).…”