scenario-simulator

To run a multi-agent simulation for scenario planning, you define a topic and generate diverse AI personas with distinct viewpoints. The system then executes multi-round dialogue deliberation between these agents to explore counterfactual outcomes and strategic risks.

Counterfactual reasoning in what-if analysis involves altering key assumptions in a simulated scenario to explore divergent future outcomes. It helps users understand the potential impact of different decisions or events by comparing baseline projections against modified variables.

Yes, you can simulate market reactions by generating agent personas representing different stakeholders, such as a skeptical analyst, an optimistic strategist, and a regulatory expert. These agents deliberate across multiple rounds to reveal varied market perspectives.

The best way to explore strategic risks is to establish a baseline scenario and then systematically alter key assumptions to create branching logic. This assumption-based approach reveals how different variables impact potential future outcomes and risk exposure.

Yes, structured persona generation is a core requirement for effective scenario simulation. Defining distinct agent viewpoints and characteristics ensures the multi-round dialogue produces diverse, realistic perspectives for accurate deliberation and risk assessment.

You should avoid multi-agent simulation for straightforward decisions with clearly defined single outcomes. It is designed for complex decision analysis requiring diverse perspectives and assumption-based branching, not simple linear forecasting or deterministic calculations.