Multi-Agent AI Simulations

The MiroFish Simulation Engine

MiroFish is the core intelligence behind AI Predicted Wins. Instead of relying on a single model or analyst, it runs a full swarm of 1,000 independent AI agents — each with a unique persona, knowledge base, and analytical framework.

How Simulations Work

For each market opportunity identified by the scanner:

1. Agent deployment — 1,000 agents are instantiated, each with a randomly assigned persona drawn from behavioral finance archetypes (contrarian traders, momentum followers, fundamental analysts, domain experts, etc.)
2. Independent analysis — Each agent evaluates the market question independently, producing an initial probability estimate
3. Structured debate — Over 30 rounds, agents can revise their estimates based on aggregated group signals — without seeing individual agent responses, preventing herding
4. Consensus extraction — The final probability is computed as a weighted average, with agents who demonstrated better calibration in prior simulations receiving higher weight

Why Swarm Intelligence Works

Individual forecasters — whether human or AI — exhibit predictable biases. Anchoring, overconfidence, recency bias, and herd behavior all distort predictions. By running 1,000 agents with diverse analytical frameworks, MiroFish systematically cancels out these biases.

The result is a probability estimate grounded in the “wisdom of crowds” — a well-documented phenomenon where the aggregate judgment of many independent estimators outperforms any individual expert.

Knowledge-Informed Agents

MiroFish agents aren’t blank slates. They draw on curated knowledge bases covering:

• Prediction market mechanics — How Kalshi order books work, common inefficiency patterns, and historical mispricing data
• Behavioral finance — Cognitive biases that create systematic mispricings (anchoring, overconfidence, loss aversion)
• Domain expertise — Political science, meteorology, macroeconomics, and technology trends relevant to specific market categories