Generalized Multi-agent Social Simulation Framework
This work addresses scalability and reusability issues in multi-agent social simulation for researchers and developers, though it appears incremental as it builds on existing LLM-based approaches with modular enhancements.
The authors tackled the challenges of scaling and reusability in multi-agent social simulation by developing a modular, object-oriented framework with a memory summarization mechanism, successfully simulating human interactions on social media to replicate real-world behaviors.
Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design. To address these issues, we designed and developed a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability. We inherited the framework to realize common derived classes. Additionally, a memory summarization mechanism is proposed to filter and distill relevant information from raw memory data, prioritizing contextually salient events and interactions. By selecting and combining some necessary derived classes, we customized a specific simulated environment. Utilizing this simulated environment, we successfully simulated human interactions on social media, replicating real-world online social behaviors. The source code for the project will be released and evolve.