Simulation World Building

Simulation world building constructs rich digital environments populated by AI agents for the purpose of prediction and analysis. Inspired by approaches like MiroFish, this skill covers the end-to-end process: designing agent personalities, building memory systems (like those powered by Zep Cloud), constructing dual-platform simulation environments, processing seed material into scenarios, injecting events, and exploring outcomes interactively. The goal is to create simulated worlds realistic enough that their emergent behaviors can inform real-world predictions.

## Key Points

1. Key entities (people, organizations, technologies)
2. Main narrative threads
3. Points of contention or uncertainty
4. Potential future developments
5. Stakeholder positions
1. Agent personality design using psychologically validated models (Big Five) produces more realistic behavioral diversity than random attribute generation
2. Multi-layer memory (working, episodic, semantic, procedural) gives agents consistent behavior over time; Zep Cloud provides production-grade persistent memory
3. Dual-platform simulation (microblog + forum) captures both rapid viral dynamics and deeper deliberative reasoning
4. Seed material processing converts real-world news and data into structured simulation scenarios with event schedules
5. Scenario injection lets you model "what if" events and observe emergent reactions across the agent population
6. Population demographics should mirror real-world distributions: mostly followers and lurkers, few influencers and contrarians
7. Agent mood and energy dynamics prevent unrealistic constant engagement; fatigue and emotional contagion drive realistic behavior