Modelagem e simulação de propagação de informações para populações baseadas em autômatos celulares probabilistas contínuos assíncronos

Abstract

Information spreads amongst people in di erent forms. Either by learners and informers agents, or individuals which look for information in media, for instance. There is a similarity with disease propagation, but instead of virus or bacterial infection in the contact between two individuals, they change information. In this work, we present two models for rumor propagation: a discrete, based on a disease spreading model, and a continuous, based on the learning process between two agents. In this case, we assume the possibility of marketing campaigns to a fraction of the population, and this is another way for an individual to obtain information. By using the rst model, we study a case of rumor propagation inside a company; and by using the second model, we study the impact of marketing campaigns parameters choice in the population information level. Both models are based in probabilistic cellular automata, the rst being discrete and synchronous, and the second, continuous and asynchronous. Results of discrete model show that educational campaigns in companies mays save costs to stop a rumor spreading, and the continuous model suggests that marketing campaign must be directed to a parcel of population with adequate content.