Can multilayer networks improve multi-agent system efficiency?
On Some Fundamental Problems for Multi-Agent Systems Over Multilayer Networks
This paper studies multi-agent systems operating on multilayer networks, where agents can interact through different relationship types (layers). It explores fundamental problems like how system behavior (phase space) changes with multiple layers, the difficulty of determining if two multi-agent systems are equivalent, and whether simpler systems with fewer layers can represent the same complex behavior.
For LLM-based multi-agent systems, the research demonstrates that even simple threshold-based local agent behaviors can lead to complex global system dynamics when using multiple interaction layers. It also highlights the computational challenge of analyzing and comparing different multi-agent architectures, suggesting the need for specialized algorithms and tools when dealing with such complex systems. Finally, the paper raises questions about the optimal design of multi-agent systems, exploring tradeoffs between complexity (number of layers) and expressiveness (range of representable behaviors). This is directly relevant to LLM-based agents, where design choices regarding the number and type of interaction channels could significantly impact overall system capabilities.