MiroFish is a sophisticated, universal swarm intelligence engine designed to simulate future scenarios by transforming real-world data into dynamic, agent-populated parallel worlds. Users can upload various "reality seeds" such as news articles, policy drafts, analytical reports, or even fictional narratives. The platform then processes this input through a five-step workflow:
- Ontology Generation: Converts raw reports into structured entities, motives, and factual anchors.
- Graph Construction: Assembles a living relationship graph that visualizes actors, tensions, and the memory structure of the scenario.
- Parallel Simulation: Deploys platform-native agents (over 1 million per run) to interact across simulated Twitter and Reddit-style channels over multiple rounds, mimicking social evolution.
- Report Generation: Condenses the simulation's trajectory into a comprehensive prediction report, highlighting key turning points, risks, and confidence signals.
- Deep Interaction: Allows users to interrogate the generated world through a "ReportAgent" or by directly interviewing individual simulated characters.
MiroFish's primary output is a detailed prediction report, offering insights into likely trajectories before real-world events unfold. It enables users to inject variables, observe social evolution, and inspect potential outcomes.
Key features include:
- Input Flexibility: Accepts PDF, Markdown, and plain text files as narrative seeds.
- Scalable Agent Simulation: Supports over 1 million parallel agents per simulation run.
- Multi-Platform Simulation: Simulates interactions across Twitter and Reddit-style surfaces.
- Comprehensive Output: Generates prediction reports with executive summaries, predicted developments, major risks, evidence lines, and key actors.
- Interactive Exploration: Allows deep interaction with the simulated world and its agents.
MiroFish is applicable across a wide range of use cases, including:
- Market Simulation AI: Stress-testing product launches, pricing strategies, and market entry.
- Public Opinion Forecasting: Modeling how narratives escalate and are reshaped by institutions, media, and influencers.
- Policy Impact Analysis: Inspecting how different stakeholder groups interpret draft policies.
- Brand Crisis Management: Simulating how reputational events expand and identifying crisis response gaps.
- Financial Scenario Planning: Running market-facing scenarios to understand sentiment divergence.
- Literary Continuation: Treating fictional worlds as live graphs of motives and memory to test how new events alter the story.
The platform emphasizes that while it automates complex simulations, human judgment remains crucial for choosing scenario boundaries, validating graph integrity, and making high-stakes operational decisions. It positions forecasts as inspectable hypotheses rather than certainties, grounded in generative agents and social simulation research.
