Exploring the Dynamics of Learned, Pre-existing, and Partial Knowledge in Dependence Networks within Multi-Agent Systems

Dependence networks play a pivotal role in shaping interactions among agents in multi-agent systems, influencing collaboration, resource management, and overall system performance. In this study, we explore the effects of limited knowledge on the dynamics of dependence networks within such systems. Through a series of experiments, we investigate how agents with restricted awareness of their environment and potential partners navigate within dependence networks and the implications of their limitations on system outcomes. Our findings reveal that agents with limited knowledge face significant challenges, including reduced collaboration opportunities, suboptimal resource utilization, and limited goal achievement. Moreover, we demonstrate the tangible costs associated with acquiring trustworthiness knowledge and the critical role it plays in optimizing agent interactions. Overall, our study sheds light on the intricate interplay between knowledge, dependence, and trust in multi-agent systems, offering insights into strategies for enhancing system efficiency in real-world applications.