DEVS Modelling and Simulation of Human Social Interaction and Influence
Résumé
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
behavior 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
individual's reaction to a change within an organization 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 population’s 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 being 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 details, 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.
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