Youssef Bouanan

DEVS modelling and simulation of human social interaction and influence

The social influence is at the centre of consideration in social science. In industrial engineering, although the enterprise has reached the age of the electronic communication, the human direct communication is not sufficiently considered even if it remains critical communication vector to transmit information. The idea is to predict some human attributes behaviour that will help enterprise to make efficient decision. The research in the domain gives significant results but the impact of information on individuals within a social network is, mostly, statically modelled where the dynamic aspect is not frequently tackled. The individuals reaction to a change within an organisation or ecosystem (implementation of a new system, new security instructions?etc.) is not always rationale. The opinion of individuals is influenced by information gathered about the attributes of the technology from other members of their social network. In addition, the works about modelling and simulation of the populations reactions to an event do not use explicit specification languages to support their models. A behavioural specification model is one critical missing link. Adding a clear behavioural model can help for specification verification and reuse. From literature, the DEVS formalism (Discrete EVent system Specifications) appears to be general enough to represent such dynamical systems (Zeigler et al., 2000). It provides operational semantics applicable to this domain. The contributions of this work are dynamic models of individuals using low-level language to simulate the propagation of information among a group of individuals and its influence on their behaviour. In more detail, we define a set of models of individuals characterized by a set of state variables and the mesh between the individuals within a social network. Then, we introduce the information diffusion based on epidemic spreading algorithms and we transpose them into the case of the message propagation in a social network. Finally, a basic scenario is used to give a beginning of validation to our models using a platform based on DEVS formalism.

Using DEVS and CELL-DEVS for Modelling of Information Impact on Individuals in Social Network

We observe that major works about modelling and simulation within social science, especially for social, organizational and cultural influences on opinion information spreading over a population, do not use specification languages to describe their models. These models are specified in the shape of math formulas and then directly coded using classical programming languages. The specification language can be a missing link. For instance, the DEVS formalism (Discrete EVent system Specifications) being general enough to represent dynamical systems, can provide an operational semantics applicable to this domain. These models independent from implementation are easily reusable. In this article, we recall first the use of discrete modelling approaches in the social influence. Then we present models for human information treatment and propagation using DEVS and Cell-DEVS (Cellular DEVS). Finally, we present a simulation transposed from epidemic models to the impact of information on individuals using CD++, a simulation tool for DEVS and Cell-DEVS.

Generating Multidimensional Social Network to Simulate the Propagation of Information

Social simulation implies two preconditions: determining a population and simulate the information diffusion within it. A population represents a group of interconnected individuals sharing information. In this paper, the population we generate is detailed by socio-cultural features, specifically the way that people tend to link together. To this end, the use of a social network is a little bit restrictive: people are linked by only one relationship. Multidimensional Social Networks (MSN) model 3D social networks where each dimension represent a kind of relationship [1]. The MSN architecture allows us to better represent the diversity of humans relations but also define distinctive rules for the simulation of the message diffusion. The inner idea is that information disseminates differently according to the links through which the information propagates. So, we present in this paper the modeling of our MSN based on social science and a simulation using propagation rules for each dimension.

A hybrid approach to study communication in emergency plans

Recent disasters have shown the need to improve emergency plans and the importance of the communications while managing the emergency. These communications can be modeled as an information transmission problem in multiplex social networks in which agents interact through multiple interaction channels (layers). Here, we propose a hybrid model combining Agent-Based Modeling (ABM), Discrete Event System Specification (DEVS), Network theory and Monte Carlo Simulation. We explore how the information spread from agents in an emergency plan taking into account several communication channels. We developed formal and simulation models of information dissemination in such emergency plans. We reuse a model architecture based on ABM, DEVS & Network Theory taking into account the behavior of the nodes in the network and the different transmission mechanisms in the layers. Finally, we execute a scenario to observe the communications using a DEVS network modeling platform powered by VLE.

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

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. 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.

Diffusion Process in a Multi-Dimension Networks: Generating, Modelling, and Simulation

Social networks simulation implies two preconditions: (1) determining a population behavior and (2) simulating the information diffusion within it. A population is defined by a group of interconnected individuals possessing individual and structural behaviors in regard to information sharing. In this paper, the population generated is defined by socio-cultural features, specifically the way that people tend to link together. To this end, the definition of a unique social network is too restrictive: realistically, people are not interlinked by only one relationship. To overcome this limitation, multidimensional social networks (MSN) have been proposed to model social interactions where each dimension represents a category of relationship. The MSN architecture allows not only to better represent the diversity of human’s relations but also to define distinctive rules for the simulation of the message diffusion. We study a model of information spreading on multiplex networks, in which agents interact through multiple interaction channels or with different relationships (layers). The inner idea is that information disseminates differently according to the category of links through which the information propagates. So, this paper presents the modelling of an MSN based on social science and a simulation using propagation rules for each dimension.