Prediction question
The market event, launch decision, policy move, competitor action, customer behavior, public reaction, or strategic choice you want the AI prediction bot to inspect.
AI prediction workflow
Prediction Bot for AI Forecast Scenarios
Use a prediction bot to turn evidence, actors, signals, constraints, and uncertainty into reviewable AI forecast scenarios before a market, product, policy, or strategic decision.
Built for scenario planning, not certainty. Every output should be reviewed against real evidence before an operating decision.
Scenario tape / illustrative
Prediction bot workflow for a decision under uncertainty
R1
Question and evidenceForecast frame built
R2
Actors and signalsPrediction paths split
R3
Uncertainty driversReview gaps surface
Bot input
Evidence packet
Forecast driver
Actor reaction
Review need
Uncertainty gap
Input
Question, sources, actors, constraints
Engine
Prediction bot + scenario simulation
Output
Forecast paths, assumptions, next checks
What to bring
Bring the prediction question before asking for an answer.
A prediction bot is most useful when it starts from a bounded question, current source material, relevant actors, known constraints, signals, and the assumptions that need to be challenged.
Evidence packet
Research notes, public sources, market context, customer signals, timelines, prior outcomes, assumptions, and unresolved claims that shape the forecast.
Actors and uncertainty
Buyers, competitors, institutions, communities, investors, media, partners, or affected groups whose reactions may change the prediction path.
Where it earns its keep
Use a prediction bot when the outcome depends on reaction, not one variable.
A useful AI prediction bot should not just guess. It should organize evidence, simulate plausible actor responses, expose assumptions, and show what would make the forecast scenario change.
Market forecast01
Inspect market reaction before the story hardens.
Simulate how buyers, investors, competitors, analysts, or public audiences may react as new information changes expectations.
Decision forecast02
Find the assumption carrying the forecast bot output.
Use the prediction bot workflow to identify which evidence, constraint, timing issue, or actor incentive most affects the forecast path.
Scenario review03
Compare scenario prediction paths instead of accepting one answer.
Generate forecast paths with visible uncertainty drivers, follow-up questions, and evidence gaps that should be checked before action.
A prediction bot needs evidence, actors, and uncertainty before it predicts.
MiroFish turns a prediction prompt into an inspectable scenario system: evidence becomes a graph, actors react across rounds, and the forecast bot report explains assumptions instead of hiding them.
Stage 1
Ground the scenario
Upload source material and define the decision, event, or message you want to test.
Stage 2
Build the actor graph
Map the people, groups, incentives, constraints, and memory that shape the reaction.
Stage 3
Run reaction rounds
Let the simulated actors respond over multiple rounds so the second-order path appears.
Stage 4
Question the report
Review the trajectory, risks, weak assumptions, and what evidence would change the conclusion.
What the report should answer
The report should make the bot's prediction reviewable.
- Which evidence or actor reaction drives the forecast
- Which forecast path appears plausible and why
- Which assumption makes the prediction fragile
- Which signal or missing source would change confidence
- Which follow-up simulation, source, or research question should come next
Boundary conditions
Not an oracle, trading system, or autopilot.
- Not guaranteed prediction of future events
- Not investment, financial, legal, medical, or trading advice
- Not an automated betting, trading, or market-making bot
- Not live monitoring unless current sources are uploaded
- Not useful when the prediction question, sources, actors, or timeframe are vague
Why MiroFish
Prediction bots should show their working.
Generic chatbots can give a confident prediction. MiroFish structures the prediction bot workflow as evidence, actors, constraints, reaction rounds, and reviewable uncertainty.
Prompt handling
Generic AI
Answers a broad prediction question directly
MiroFish
Builds a bounded forecast frame from sources and actors
Forecast path
Generic AI
Returns one prediction with limited structure
MiroFish
Shows scenario paths, second-order reactions, and uncertainty drivers
Review
Generic AI
Hard to audit or improve
MiroFish
Exposes assumptions, evidence gaps, and next research checks
FAQ
Prediction bot, without false certainty.
Use MiroFish as an AI prediction bot workflow for scenario forecasting and review, not as a certainty engine.
What is an AI prediction bot?+
An AI prediction bot helps inspect a forecast question by using source material, actors, signals, constraints, and uncertainty. In MiroFish, the prediction bot workflow produces reviewable AI forecast scenarios and evidence gaps instead of claiming certainty.
What makes MiroFish different from a forecast bot?+
A basic forecast bot may return one answer. MiroFish keeps the forecast reviewable by showing the evidence, actor reactions, uncertainty drivers, and follow-up questions behind each prediction path.
How is this different from a normal chatbot prediction?+
A normal chatbot often gives one answer from one prompt. MiroFish structures the prediction into evidence, actor reactions, assumptions, and follow-up questions so the forecast can be reviewed.
Can a prediction bot predict the future exactly?+
No. MiroFish does not claim exact future prediction. It helps teams inspect plausible paths, uncertainty drivers, and weak assumptions before making a decision.
Can I use this for prediction markets?+
You can use the workflow to research prediction market questions by uploading market context, resolution criteria, sources, and competing narratives. It is not trading advice or an automated trading bot.
What should I upload to run a prediction bot scenario?+
Upload the prediction question, source material, relevant actors, current signals, prior context, timeline, constraints, and the assumptions you want the forecast to test.
Related simulation paths
Follow the risk into the next decision.
Most decisions do not stay inside one category. These adjacent use cases help teams test the next market, customer, or public reaction path.
Predictive paths
Predictive Simulation
Use evidence, actors, constraints, and reaction rounds to inspect plausible future paths before a decision.
Open Predictive SimulationForecasting workflow
AI Forecasting Tool
Turn evidence, actors, and uncertainty into inspectable forecast paths before decisions harden.
Open AI Forecasting ToolPrediction markets
Polymarket Market Research
Use MiroFish to inspect Polymarket market narratives, event paths, uncertainty drivers, and evidence gaps.
Open Polymarket Market ResearchForecast with review
Turn a prediction prompt into forecast scenarios you can inspect.
Bring the forecast question, evidence, actors, and constraints. MiroFish will turn them into prediction bot scenarios with visible assumptions and next checks.
Start a prediction bot run