llm-social-simulation

Design LLM agent-based social simulations by specifying simulation tiers, agent roles, interaction rules, and environment parameters. This produces synthetic social data while applying multi-level validation to ensure simulated outputs accurately reflect intended social dynamics without misrepresenting them as real human evidence.

Multi-agent systems validation uses a multi-level framework assessing micro, meso, and macro outcomes. It evaluates agent-level agreement, network dynamics, and aggregate statistics, running robustness checks across different LLM models and temperatures to ensure reliable social simulation results.

Yes, multi-agent deliberation with memory is supported through structured agent architecture patterns. Model personas, beliefs, and interaction dynamics so agents can deliberate and update their positions over time, enabling exploration of complex social theories across Tier 3 and Tier 4 simulation levels.

Ethical limitations include WEIRD bias, non-independence of agent outputs, and stereotype replication. Apply strict reporting guardrails and ethical boundaries to avoid presenting simulated social data as real human evidence, documenting these constraints to maintain research integrity.

Validate social simulation results by running robustness checks across different LLM models and temperature settings. Compare outputs using metrics like agreement, distribution overlap, and network dynamics at micro, meso, and macro levels to confirm the simulation's reliability.