scaled-swarm-predict

Scaled Swarm Predict

Large-scale social simulation engine. Generates 50-200 demographically diverse AI personas, runs them through a simulated Twitter or Reddit board debating a topic, then analyzes emergent consensus, fault lines, and predictions.

What this is: A structured simulation where you generate a population of diverse personas matching real-world demographics, run 4-6 rounds of simulated social media discussion in a local SQLite database, then analyze position shifts and emergent consensus to make predictions.

What this is NOT: This is not MiroFish/OASIS (which runs independent agent processes). All personas are generated and role-played by a single model. The value comes from forcing diverse demographic perspectives through structured rounds of interaction — not from emergent multi-agent behavior. Think of it as a sophisticated polling simulation, not swarm intelligence.

Cost per run: ~$5-15 with Claude Sonnet (100 agents × 5 rounds = 500 generations). ~$25-60 with Opus. Each round is one large prompt, not 100 separate API calls.

When to Use

• Predict public reaction: "How will Twitter react to this product launch?"
• Election/poll modeling: "What does a demographically representative sample think about X policy?"
• Content testing: "Will this announcement go viral or get ratio'd?"
• Market sentiment: "How will crypto twitter react to this protocol change?"
• Risk assessment: "What will the Reddit comments look like when we announce this pricing change?"
• Brainstorming at scale: "What arguments exist for/against X across different demographics?"

Phase 1: Define the Simulation

1.1 — Get the Topic

Ask the user for:

1. Topic/scenario — What are we simulating discussion about?
2. Platform — Twitter (short-form, viral dynamics) or Reddit (long-form, threaded)
3. Population size — 50 (fast, cheap), 100 (balanced), 200 (thorough)
4. Seed data — Any documents, articles, data to ground the simulation