Scenario simulators let users model multiple “what-if” paths for an event instead of focusing on a single forecast. They adjust inputs—such as timing, dependencies, or external signals—and observe how probabilities might shift. This helps users understand uncertainty, trade-offs, and possible ranges of outcomes.
In prediction markets, scenario simulators often rely on existing market probabilities as inputs. By using prediction markets data, these tools can simulate how new information, alternative assumptions, or linked events could change expectations. This turns static probabilities into dynamic exploration, showing how beliefs might evolve under different scenarios.
Scenario simulators are especially useful for complex events where outcomes depend on several factors, such as policy decisions, economic indicators, or multi-step processes.
Scenario simulators help users think beyond a single forecast. They make prediction markets data more useful by showing how expectations change under different assumptions.
They start with market-implied probabilities as a baseline. Then they adjust conditions—such as event timing, dependencies, or external shocks—to see how forecasts might change. This allows users to explore alternative futures while staying grounded in real prediction markets data.
They help decision-makers see downside risks, upside possibilities, and sensitive assumptions. Instead of relying on one number, users can compare multiple paths and understand where uncertainty is highest. This makes prediction markets data more actionable for planning and strategy.
Analysts can identify which variables matter most, where forecasts are fragile, and how expectations respond to different inputs. Scenario simulators also reveal hidden dependencies between events that may not be obvious from a single market. These insights deepen interpretation of prediction markets data.
An analyst uses market probabilities from Polymarket to simulate different regulatory timelines. By adjusting assumptions about vote timing and enforcement delays, the simulator shows how the likelihood of approval changes across scenarios, helping the analyst understand key risk drivers.
Scenario simulators depend on clean, structured probability inputs. FinFeed's Prediction Markets API provides reliable prediction markets data—probabilities, historical paths, and outcome relationships—that developers can use to build scenario simulators, test assumptions, and explore alternative future outcomes.
