Multi-Agent Simulation of Product Diffusion in Online Social Networks from the Perspective of Overconfidence and Network Effects

Abstract

Online social networks (OSNs) have steadily become the primary mechanism of product promotion. However, previous studies have paid little concern to the irrational consumer behavior (e.g., overconfidence) and network effects that influence product diffusion in OSNs. We use overconfidence theory, network effects theory, and evolutionary game theory to build a multi-agent simulation model that captures the nonlinear relationship between individual actions to examine the effects of overconfidence and network effects on product diffusion in OSNs. We found that (1) overestimation is profitable for improving the diffusion level of product diffusion in OSNs and maintaining market stability; however, the closer the degree of overprecision is to 1 (i.e., individuals are more rational), the more stable the market will be. We also found that (2) moderate network effect intensity can better promote product diffusion on the social network. When the network effect intensity is small, the non-overconfident scenario has the highest percentage of adoption. The overprecision scenario has the highest percentage of adoption where the network effect intensity is high. Additionally, we found that (3) the scale-free network is more conducive to the diffusion of products in OSNs, while the small-world network is more susceptible to overconfidence and network effect. This research laid the groundwork for investigating dynamic consumer behavior utilizing a multi-agent method, network effects theory, and a psychological theory.