Multi-Agent Systems for Collaborative Financial Decision-Making Over Distributed Network Architectures

In financial collaborative decision-making use case, we focus on Multi-Agent Systems (MAS) based financial decision making through distributed network architectures. The study analyses agent collaboration efficiency, system scalability, communication overhead, and decision-making accuracy through simulating different scenarios. These results show that the system adapts to varying dynamic conditions, since it continues producing high performance as the number of agents and connectivity of the network both increase. To illustrate, heatmaps and comparisons of overhead and decision texts show less communication overhead and better decisions than traditional centralized approaches can generate. The results confirm the suitability of MAS for complex financial target patterns, thus providing a scalable, efficient and robust solution for the next generation of financial decision support systems. These findings help extend MAS-inspired approaches to distributed, high-stakes decision making environments.