Quick answer
Multi-agent simulation is useful when interaction changes the outcome.
Instead of asking what one model thinks will happen, you model what happens when different actors keep responding to each other under explicit assumptions.
Definition
Multi-agent simulation represents participants as separate actors with goals, memory, and behavior.
In MiroFish, those actors are connected through a graph and allowed to react across multiple rounds, exposing disagreement, amplification, adaptation, and missing pressure.
Why people use it
- Public opinion and narrative spread.
- Policy interpretation and compliance pressure.
- Product launch reaction and competitor framing.
- Organizational conflict, trust, and community behavior.
Simulation vs one-shot
Multi-agent simulation is different from one-shot AI output.
Question
One-shot AI: What is the likely answer?
Multi-agent simulation: What happens as actors keep reacting?
Structure
One-shot AI: One response from one model pass.
Multi-agent simulation: Multiple actors, incentives, memory, and changing state.
Best output
One-shot AI: A summary or recommendation.
Multi-agent simulation: Reaction paths, pressure points, and assumptions to review.
MiroFish pattern
MiroFish turns the pattern into a reviewable forecast workflow.
Layer 1
Build the actor graph
MiroFish extracts actors, claims, incentives, relationships, and pressure points from the uploaded source packet.
Layer 2
Run reaction rounds
Agents respond over several rounds so the first reaction can change the second and the second can change the third.
Layer 3
Review the forecast
The report shows likely paths, dominant narratives, confidence limits, and missing evidence that should be checked.
Prompt template for multi-agent simulation
Simulate three rounds of reaction to the uploaded event, show who gains influence first, and explain which narrative becomes dominant.
FAQ
Questions about multi-agent simulation
What is multi-agent simulation?+
Multi-agent simulation models a system by representing different participants as separate actors with their own goals, constraints, memory, and behavior.
How is multi-agent simulation different from one-shot AI output?+
One-shot AI output compresses a scenario into one answer. Multi-agent simulation preserves interaction, disagreement, amplification, adaptation, and changing state over time.
When is multi-agent simulation useful?+
It is useful when outcomes depend on interaction, such as public opinion, policy interpretation, launch reaction, organizational conflict, or community behavior.
Is multi-agent simulation the same as agent-based modeling?+
They are related. Agent-based modeling is the broader simulation approach, while MiroFish applies multi-agent simulation to AI-driven scenario forecasting with source packets, actor graphs, reaction rounds, and reviewable reports.
How does MiroFish use multi-agent simulation?+
MiroFish turns uploaded source material into a graph, then lets agent profiles react across multiple rounds before producing a forecast report for review.
Read next
Connect the model to practical simulation pages.
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