公司社会学系吕鹏教授（唯一作者）在国际期刊《Applied Mathematics and Computation》（IF=4.091, JCR一区, 中科院Top期刊），发表SCI/SSCI收录论文《Heterogeneity, Judgment, and Social Trust of Agents in Rumor Spreading》。

  文章摘要：As one of the typical collective actions or cooperative behaviors of human beings, the rumor is widespread and harmful in the society for most cases. Modeling and predicting the dynamics and evolutions or rumors’ spreading has been widely investigated in existing models, including the (expanded) SIR models. In this paper, a micro-model of neighborhood interactions between agents (ABM) is proposed to explore the mechanism of rumors’ spreading. In the proposed model, the agents (sources or receptors) interact with the neighbors on a square lattice, and the source spreads rumors to receptors, and if the receptors decide to spread rumors they become sources as well. For each agent, the individual judgment heterogeneity and social trust heterogeneity are introduced, and the distance to the original source provides the basic field function. Distance, Judgment, and Trust consist of the thresholds that should be overcome before the rumor can be spread by certain agent to others. The heard time records the frequency that the rumor is heard, and the agent spreads the rumor if the heard time satisfies the threshold condition. As the mean effects of individual judgment and social trust on rumors’ spreading are stabilized, this paper focuses on their heterogeneity effects. The spreading curves monitors the instant spreading percentage and they have two stages, which are the “rapidly increase stage” with the linear relationship and “slowly increase stage” with the nonlinear relationship. Simulation outcome indicate that heterogeneity promotes the spreading while the homogeneity dampens it. Besides, the conditional effects of social trust heterogeneity under individual judgment heterogeneity coincide with the general effects. This work paves the way for the full-process prediction of rumors’ spreading.

  关键词: Rumor Spreading; Simulation; Heterogeneity; Agent-based modeling; Collective actions.