Modeling Human Modifier Diffusion in Social Networks

Modeling the impact and actions of adverse events on populations, networks, groups and individuals.

This paper focuses on modeling the impact and actions of adverse events on populations, networks, groups and individuals. In the proposed approach social network evolution is analyzed for different scenarios. Therefore, this research aims at developing models to reproduce the diffusion of Human Behavior Modifiers (HBM) in social networks. This is related not only to PSYOPS in traditional operative scenarios, but also to the reproduction of the impact of terrorist actions, contaminated or defective product lots, the influence of news broadcasts as well as the diffusion of consumer fear. This research is based on the development of a dynamic simulation that combines continuous and discrete models to investigate hypotheses about the impact of several parameters and the response of different elements on the diffusion of these factors. The authors focus on fear diffusion, presenting simulator examples developed for military (i.e. PSYOPS) and civilian scenarios (i.e. food contamination) and for hybrid situations (i.e. deterrence during civil disorder).The goal is to provide interoperable stochastic models to complete statistical experimental analyses on social network behaviors to create realistic scenarios for CAX (Computer Assisted Exercises ) and to support training.