BettaFish is agent-based modeling software for qualitative decision rehearsal, not a programmable ABM framework. Use it when actors, incentives, relationships, and interpretation shape a decision. Upload source material, inspect the actor graph, run reaction rounds, read the report, and turn unresolved claims into validation tasks.
Live decision rehearsal
Multi-role reaction path
Direct answers
Start with the evidence needed for the decision. Use simulation to expose uncertainty, not to hide it behind generated volume.
BettaFish supports qualitative actor modeling for strategic, product, market, policy, and stakeholder decisions.
Use BettaFish when the team needs fast decision rehearsal before research, modeling, experiments, or executive review.
Use a formal ABM framework when you need code, calibrated parameters, repeated experiments, spatial models, or statistical outputs.
Five-step workflow
Every step leaves something inspectable: the source, the actor assumptions, the reaction path, or the next human check.
Name the decision, actors, system boundary, and time horizon.
Upload market, customer, policy, product, competitor, or stakeholder evidence.
Review actor relationships, conflicts, incentives, knowledge, and missing context.
Use simulation to expose how one actor's response can change another actor's path.
Ask follow-up questions and turn fragile claims into validation tasks.
Useful output makes the next decision or validation step more specific.
These boundaries apply even when the output looks detailed or consistent.
Frequently asked questions
BettaFish 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.
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.
The graph makes actors, relationships, conflicts, knowledge, and influence paths visible before the report becomes persuasive, so weak assumptions are easier to challenge.
No. AI agents can rehearse plausible reactions and expose assumptions, but real behavior requires validation through people, data, experiments, or accountable domain review.
Bring the current evidence, a bounded question, and the assumptions your team is willing to challenge.
Related methods and scenarios
Simulate actor incentives, constraints, relationships, and reaction rounds before committing to a decision.
Compare traditional ABM, multi-agent systems, LLM simulation, and where BettaFish fits.
Review BettaFish's existing agent-based simulation AI page.
