Simulate the impact of a policy or regulation before it lands
Model how stakeholders, industries, and the public react to a rule change — with estimated-probability outcomes and second-order effects.
MiroFish simulates how a policy or regulation will play out before it takes effect. You describe the rule and who it touches, and the engine models the affected stakeholders — businesses, workers, regulators, the public, the press — as AI agents that react to the policy and to each other. It returns a structured report: the most-likely reaction with an estimated probability, the second-order ripple effects, which groups adapt versus get hurt, the likely public and political narrative, and the risks to prepare for. It reasons about the policy from your description rather than modeling legal detail or real economic data, so treat it as directional decision support for scenario planning, not legal or economic advice.
How MiroFish simulates it
A policy simulation maps a rule to the actors it moves:
- 1
Describe the policy and who it touches
The rule or proposal, the affected groups, the jurisdiction, and the time horizon. Attach the policy text or a summary to ground the agents in the actual language.
- 2
Model the stakeholder web
Businesses, workers, regulators, advocacy groups, and press become interconnected agents. Influence travels along realistic paths so ripple effects can form.
- 3
Simulate reactions and adaptation
Agents comply, resist, lobby, and adapt over multiple rounds. Second-order effects — a compliance-cost pass-through, a workaround, a coalition — emerge from the interactions rather than being scripted.
- 4
Read the impact report
Most-likely reaction with estimated probability, the ripple effects, which groups adapt versus get hurt, the public and political narrative, and the risks to prepare for — with a chat to test alternative policy framings.
A worked example
The EU mandates clear labeling of AI-generated content in advertising. Question: impact on small e-commerce brands that rely on AI product photography.
What you give it
- Policy
- Mandatory AI-content labeling in ads
- Focus group
- Small e-commerce brands
- Jurisdiction
- EU
- Horizon
- First 12 months
What the report estimates
- Most-likely reaction
- Compliance friction, uneven enforcement
- Ripple effect
- Shift toward hybrid/real photography
- Who’s hurt most
- Smallest brands (labeling overhead)
- Public narrative
- Split: transparency win vs. red tape
Illustrative output. MiroFish does not model legal detail or real economic data — use for scenario planning, not compliance or legal advice.
What it can’t do (honest limits)
- —It is not legal, compliance, or economic advice — it estimates reactions, not statutory interpretation or GDP effects.
- —It reasons from your description and any attached text; it does not track real legislative status or enforcement data.
- —It estimates directional impact and narrative, not precise costs, headcounts, or market-size changes.
- —It complements expert legal and policy analysis; it is a fast way to map the reaction space, not to replace them.
Questions people ask
How will a new regulation affect my business?
A policy simulation estimates the reaction and ripple effects for the specific rule and business you describe — which parts of your operation face friction, how customers and competitors respond, and the most-likely outcome with an estimated probability. It maps the reaction space quickly, but it is scenario planning, not legal or compliance advice, so pair it with expert review before acting.
Can I predict the impact of a policy change before it lands?
Yes — because a policy is a novel event, statistical forecasting has no data, but a multi-agent simulation can reason through how affected stakeholders react over time. You describe the rule and the groups it touches, and MiroFish returns an estimated-probability report of reactions and second-order effects. Treat it as a directional map of what could happen, not a certainty.
What happens to small businesses under a new law?
The simulation models small businesses as a distinct stakeholder group and estimates the friction they face relative to larger players — often flagging that fixed compliance overhead hits the smallest hardest. The report gives the most-likely reaction and the adaptations that reduce the burden. These are directional estimates for planning, not calculated compliance costs.
How do I model the second-order effects of a policy?
Second-order effects are exactly what multi-agent simulation surfaces: a compliance cost that gets passed to customers, a workaround that spreads, a coalition that forms to lobby. Because agents react to each other over multiple rounds, these chains emerge rather than being scripted. The report names the most-likely ripple effects with the reasoning, as estimates to prepare for.
How do stakeholders and industries react to a policy announcement?
Each stakeholder group is modeled as agents with distinct incentives, so the report estimates how businesses, workers, advocacy groups, and press each respond, and where their reactions conflict. It shows which groups adapt and which resist, with the likely narrative. This is an estimate of reaction patterns to inform your response, not a forecast of any single organization’s official position.
How do I assess regulatory risk in advance?
Run the proposed rule as a simulation and read the ranked risks and the groups most likely to be hurt, then use the chat to test how alternative framings or timelines change the outcome. It turns a vague “this could be bad” into an estimated-probability picture of specific risks and mitigations you can prepare before the rule takes effect.
What’s the downstream effect of an AI-labeling or disclosure law?
The simulation estimates the behavioral shift such a law triggers — for example a move toward hybrid or real media, uneven early enforcement, and a public narrative that splits between “transparency win” and “red tape.” It ties each effect to the stakeholders driving it. These are directional estimates of the reaction, useful for planning a response rather than predicting exact market shifts.
How do I predict public reaction to a government policy?
Public and press agents react to the policy in the simulation, so the report estimates the most-likely public sentiment, how it divides across groups, and the flashpoints. Because it models a crowd rather than surveying one, treat the sentiment as a calibrated estimate — strong on direction (who supports, who opposes, and why) and weaker on exact polling numbers.
Which parts of my industry adapt versus get hurt?
The report separates the industry into stakeholder segments and estimates which adapt (often larger, better-resourced players) and which struggle, with the reasoning for each. It is a directional read on the distribution of impact across your industry, meant to guide where you focus preparation, not a precise measure of winners and losers.
Go deeper on the method: read the full guide on the blog →
Run it on your own scenario
No install, no API keys. Credit packs from $2.99 — results in 5–10 minutes, credits never expire.
Start predicting