Social Experiment Simulator

Inspiration

The rise of deepfakes, social media misinformation, and sophisticated social engineering attacks targeting remote workers inspired us to create a safe training environment where people can experience manipulation tactics firsthand without real consequences.

What it does

Our platform simulates realistic social engineering and misinformation scenarios using AI agents that interact naturally while attempting to extract passwords or spread false information. Users observe live conversations, analyze manipulation tactics, and receive AI-generated reports with actionable security recommendations.

How We Built It

We developed a multi-agent social dynamics simulation platform using a FastAPI backend with SQL database and vanilla JavaScript frontend. The system leverages Letta's stateful agent framework to create persistent AI agents with memory that can engage in realistic social manipulation scenarios. Our architecture includes template-based experiment configuration, real-time conversation monitoring via REST APIs, and an AI moderator that analyzes interaction patterns. The frontend implements a 5-step workflow: experiment selection, template configuration, simulation execution, live observation, and comprehensive reporting with actionable insights. The platform lets us simulate popular social experiments and themes using LLM agents, with a templatized conversation system between the agents simulated over multiple rounds, analyzed and compiled into insights and trends at the end by another LLM agent. We have replicated a few common social phenomena, and added the ability to generate custom experiments to simulate more novel scenarios as well.

Challenges We Faced

On the technical side, we struggled with concurrency, and API rate-limiting. On the functional side, we experienced the limitations of generative models for a use-case such as this. LLMs are trained on human knowledge but do not necessarily share the same pitfalls and biases in reasoning. However, that also presents an opportunity to understand and explore the completely different mistakes LLMs tend to make in these scenarios. And the overlap between human and LLM agent's susceptibility to manipulation and deceptive tactics are enough to derive some meaningful results.

Future Scope

The platform can expand to include more customization, such as goals for individual and overall goals for agents which can be tracked, interaction/relationship graphs, emotion modelling for individual agents etc. We also envision a setup which draws inspiration from evolutionary algorithms where agents codify their strategy but replace it with better performing ones collaboratively over rounds, with a chance for thinking up and executing a brand new strategy (mutation). This could reveal some novel patterns which have not been conceived of before. There are also usability enhancements, like multi language support, voice support, ability to replace an agent with a human interacting with the system etc.

Built With

• letta