Multiagent social network simulations are an avenue that can bridge the communication gap between the public and private platforms in order to develop solutions to a complex array of issues relating to online safety. While there are significant challenges relating to the scale of multiagent simulations, efficient learning from observational and interventional data to accurately model micro and macro-level emergent effects, there are equally promising opportunities not least with the advent of large language models that provide an expressive approximation of user behavior. In this position paper, we review prior art relating to social network simulation, highlighting challenges and opportunities for future work exploring multiagent security using agent-based models of social networks.

Key Contributions

• Comprehensive Review of Multiagent Simulators
• Challenges in Scaling and Complexity
• Opportunities with Large Language Models
• Applications in Online Safety
• Future Directions for Research

Conclusion

Multiagent simulators are expressive models of online interaction and have demonstrably yielded value in varied applications. While there are limitations from scale and complexity, there is significant value that is likely to be unlocked by advances in computational modeling and machine learning for this area.