01
What does agent-based simulation software do?
It models how actors with different incentives and constraints may interact inside a scenario.
- Actor roles
- Rules and constraints
- System-level reaction paths
Agent simulation software
Agent-Based Simulation Software
MiroFish helps teams run agent-based simulation software workflows when a decision depends on interacting actors. It turns source material into an actor graph, runs qualitative reaction rounds, creates a prediction report, and supports follow-up questions. Use it to rehearse decisions, not to claim measured or deterministic behavior.
Not a statistically representative survey, customer panel, or deterministic prediction.
Live decision rehearsal
Multi-role reaction path
R1 · Buyerreaction
Responds to value proof, price pressure, risk, and peer influence.
R2 · Competitorreaction
May ignore, copy, undercut, reposition, partner, or escalate.
R3 · Institutionreaction
Applies policy, trust, compliance, procurement, or reputation constraints.
R4 · Teamreview
Reviews graph assumptions and decides what must be validated outside the model.
WorkflowEvidence, actors, incentives, reactions, report, 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
What makes MiroFish different from a generic chatbot?
MiroFish structures the work around source material, an actor graph, simulation rounds, reports, and follow-up questions.
- Inspectable graph
- Multi-round reactions
- Reviewable report
03
What decisions fit this workflow?
Use it for launches, pricing, market entry, policy, public response, crisis communication, and stakeholder decisions.
- Product and GTM
- Public affairs and policy
- Competitive strategy
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
Frame the system
Define the actors, decision, context, and source packet.
- 2
Review assumptions
Inspect actor goals, constraints, relationships, and likely blind spots.
- 3
Run reactions
Let the simulated agents respond across rounds so second-order paths appear.
- 4
Read the report
Identify conclusions, assumptions, risk paths, and unsupported claims.
- 5
Validate externally
Use real evidence, interviews, data, experiments, or expert review for consequential claims.
What should the report give your team?
Useful output makes the next decision or validation step more specific.
- Actor-specific reaction paths
- Graph view of assumptions and relationships
- Report sections that explain plausible system outcomes
- Validation checklist for claims the simulation cannot prove
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
Agent-Based Modeling Software
Use MiroFish for qualitative agent-based decision rehearsal with source packets, actors, graphs, reports, and review questions.
Agent-Based Simulation Examples
Review examples for launches, pricing, policy, competitive response, crisis communication, and stakeholder decisions.
Multi-Agent Simulation AI
See the broader MiroFish multi-agent simulation workflow.