Information coevolution spreading model and simulation based on self-organizing multi-agents

Abstract Coevolutionary spreading, the interdependent propagation of multiple-type information (or epidemics or social behaviors), has attracted both scientific and industrial attention due to its complex dynamics. While agent-based models (ABMs) are well-suited for modeling single-type contagion dynamics, they struggle to represent the microscopic interdependencies of co-evolving information types within different network topologies. This paper proposes a multi-information co-evolution propagation model based on self-organizing multi-agents, breaking through the limitations of traditional threshold spreading models and agent-based models. The model, which is validated through consistency with traditional SIR models under the circumstance of well-mixed agents, can be used to uncover the spreading mechanisms on different network topologies (such as ER, BA, WS) through a series of transmitting and recovering rules that act on each agent with social contagion behaviors and attributes. Furthermore, sophisticated spreading patterns, such as active counterattack and cooperative operation, are also explored based on this model to simulate the multi-information propagation process. These complex propagation simulations reveal some interesting phenomena: (1) When counterattacking the spread of a specific source information, blindly increasing the proportion of counterattackers or the information exclusion coefficient may not necessarily be the best choice, even without considering costs. (2) In networks with long-short loop structures, compared to the situation of single information dissemination, the coevolutionary spread of two types of information is more prone to avalanche phenomena, with the S (susceptible) state of information dropping sharply from a steady state of 60% to a steady state of 20% by the 10th generation. These findings provide actionable insights for controlling misinformation in social networks and optimizing public health interventions, emphasizing that "more intervention" does not always equate to "better control" in coevolutionary systems.