01
What is multi-agent system simulation?
It is a way to inspect how several actors or agents interact and produce a system-level path.
- Multiple actors
- Relationships and constraints
- Second-order reactions
Multi-agent systems
Multi-Agent System Simulation
MiroFish supports multi-agent system simulation for decisions where several actors influence one another. Teams can ground actors in a source packet, inspect the graph, run reaction rounds, read a prediction report, and ask follow-up questions. The goal is reviewable strategic rehearsal, not autonomous agent deployment or certainty.
Not a statistically representative survey, customer panel, or deterministic prediction.
Live decision rehearsal
Multi-role reaction path
R1 · Actor Areaction
Interprets the decision through its own goals, evidence, and constraints.
R2 · Actor Breaction
Responds to Actor A, creating second-order effects and new risk paths.
R3 · Actor Creaction
Changes the narrative, approval path, or timing under visible assumptions.
R4 · Reviewerreview
Uses the report and follow-up questions to decide what evidence is missing.
System signalActors interact, assumptions surface, follow-up questions guide validation
Direct answers
What should teams understand before they simulate?
Start with the evidence needed for the decision. Use simulation to expose uncertainty, not to hide it behind generated volume.
02
How does MiroFish represent the system?
MiroFish represents the system through a source-grounded actor graph, reaction rounds, reports, and follow-up questions.
- Graph review
- Report review
- Deep Interaction follow-up
03
Who should use this page?
Use it when a strategy, product, policy, or market decision depends on interaction among stakeholders.
- Product and strategy teams
- Market researchers
- Public affairs and leadership teams
Five-step workflow
How does the simulation move from evidence to action?
Every step leaves something inspectable: the source, the actor assumptions, the reaction path, or the next human check.
- 1
Define the system boundary
Name the event, decision, audience, actors, and time horizon.
- 2
Ground the actors
Use evidence to define goals, knowledge, constraints, and conflict points.
- 3
Inspect relationships
Use the graph to check who influences whom and where assumptions are weak.
- 4
Run simulation rounds
Let actor reactions compound and create reviewable system paths.
- 5
Ask follow-up questions
Interrogate the report and convert claims into validation work.
What should the report give your team?
Useful output makes the next decision or validation step more specific.
- Multi-agent graph with visible relationships
- Round-by-round reaction paths
- Prediction report for system-level review
- Follow-up answers and validation gaps
What can this simulation not establish?
These boundaries apply even when the output looks detailed or consistent.
- MiroFish is not a programmable ABM framework, NetLogo replacement, AnyLogic replacement, Python simulation engine, or calibrated mathematical model.
- Agent simulation produces hypotheses, reaction paths, assumptions, and validation questions, not a deterministic prediction.
- Generated actors are not real people, recruited respondents, statistically representative samples, or observed market behavior.
- Use domain experts, customer or stakeholder evidence, experiments, analytics, legal review, or accountable human judgment before consequential decisions.
Frequently asked questions
What else should teams know?
Is MiroFish agent-based modeling software?
MiroFish can support qualitative agent-based decision rehearsal, but it is not a programmable ABM framework for calibrated mathematical simulation, code-based model building, or formal sensitivity analysis.
What should teams upload for agent simulation?
Upload a focused source packet: the decision, context, actor list, known constraints, evidence, assumptions, prior research, market notes, and the question the team needs to review.
What does the actor graph add?
The graph makes actors, relationships, conflicts, knowledge, and influence paths visible before the report becomes persuasive, so weak assumptions are easier to challenge.
Can AI agents predict real behavior?
No. AI agents can rehearse plausible reactions and expose assumptions, but real behavior requires validation through people, data, experiments, or accountable domain review.
Evidence → actors → reactions → review
Rehearse the decision before the market makes it expensive.
Bring the current evidence, a bounded question, and the assumptions your team is willing to challenge.
Related methods and scenarios
Multi-Agent System Simulation Guide
Follow a practical workflow from source packet and actor setup to graph review, report reading, and follow-up questions.
Agent-Based Modeling vs Multi-Agent Simulation
Compare traditional ABM, multi-agent systems, LLM simulation, and where MiroFish fits.
LLM Social Simulation
Review MiroFish's social simulation workflow with language-model actors.