Information spreading in a population modeled by continuous asynchronous probabilistic cellular automata

Abstract

In this paper, we propose a model for information propagation in a population based on cellular automata. Different to what is commonly used in the models of the area, instead of a binary level for the information, individuals have a level of knowledge. Moreover, the population can have a marketing campaign to help to spread the information with a certain limit due to the consideration of marketing rejection in the model. Numerical simulations show that these campaigns must be wisely set in order to not saturate the population and decrease the marketing balance due to the rejection. The simulation data is statistically analyzed by using principal component analysis in order to identify the most relevant variables for the model output. The conclusion is that dealing with this rejection is difficult, as well as choosing the percentage of the population which will receive the marketing, and along with the weight for the word-of-mouth were the most important variables of the model according to the simulations and the principal component analysis.