Quick answer
MiroFish simulates by making the hidden scenario structure visible.
The workflow is source packet, entity extraction, graph construction, multi-round agent reaction, then forecast report. Each layer gives the operator something to inspect.
Definition
An AI scenario simulation workflow turns source material into a reviewable model of reaction.
In MiroFish, that workflow is not a single prompt response. It is a chain of source parsing, graph construction, multi-agent interaction, and forecast reporting.
Input
One source packet with actors, tension, and a decision window.
Model
A scenario graph that exposes relationships before the simulation runs.
Dynamics
Multi-agent reaction rounds that reveal spread, objections, and drift.
Output
A forecast report that shows likely paths, assumptions, and weak evidence.
Pipeline
From source material to simulated reaction
Step 1
Seed the scenario
Start with one report, policy note, product launch brief, market memo, or narrative file. The source should contain actors, pressure, and a concrete question.
Step 2
Extract entities and motives
MiroFish converts the source into structured actors, audiences, claims, constraints, incentives, and tension points.
Step 3
Build the graph
The graph makes the scenario visible. Operators can inspect relationships, missing actors, and influence concentration before running the simulation.
Step 4
Run multi-round interaction
Agents react over multiple rounds so weak claims, amplification paths, objections, and narrative drift can surface.
Step 5
Read the forecast report
The report translates the simulated reaction into likely paths, confidence gaps, dominant risks, and follow-up questions.
The graph is the audit surface.
If the graph misses an actor, relationship, constraint, or source of pressure, the final report can still sound polished while being directionally weak.
Agent rounds expose movement.
Multi-round interaction is where narratives spread, fragment, stabilize, or attract objections that a static answer would skip.
A useful forecast report answers bounded questions.
- What changes first after the trigger event?
- Who amplifies the narrative or objection?
- Which risk becomes dominant across rounds?
- Where is confidence weak because the source is thin?
Operator checks before trusting the run
- The source packet has one clear event or decision window.
- The extracted graph includes the actors that can change the outcome.
- The simulation question names an audience and time horizon.
- The report explains assumptions instead of only giving conclusions.
FAQ
Questions about the simulation engine
Is MiroFish predicting the future with certainty?+
No. MiroFish builds inspectable scenario forecasts. Use the output to review likely paths, assumptions, risks, and missing evidence before making a decision.
What makes the simulation different from a summary?+
A summary compresses source material. MiroFish turns the source into a graph, lets agents react over multiple rounds, and reports how the scenario may change.
Why does graph review matter?+
The graph exposes whether important actors, incentives, constraints, and tensions were captured before the simulation result becomes persuasive.
When should I run another simulation?+
Run another simulation when the graph is missing an actor, the prompt was too broad, or you want to compare a changed message, audience, or time horizon.
Read next
Connect the engine to a real workflow.
What Is MiroFish?
The product model behind graph, simulation, and forecast reports.
Open guideHow to Use MiroFish
A practical first-run workflow for preparing and reviewing a simulation.
Open guideLLM Social Simulation Explained
How language models can represent interacting audiences and narratives.
Open guide