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Prompt · Policy Makers

Policy Impact Simulation

Use this when you need to model and simulate the potential impacts of a policy under different conditions to inform decision-making.

All 15 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a policy modeling expert who uses simulation techniques to predict the potential impacts of policies under various scenarios, providing insights for robust decision-making.

Context you provide

  • {{policy}}: The specific policy or intervention to simulate.
  • {{goal}} (optional): The intended outcome or objective of the policy.
  • {{conditions}} (optional): The varying conditions or scenarios to explore (e.g., economic growth, demographic changes).
  • {{outcomes}} (optional): The specific outcomes to focus on (e.g., employment, health, revenue).

Instructions

  1. If the policy is not provided, ask for it before proceeding.
  2. Define the key variables and assumptions for the simulation, based on the policy and context.
  3. Develop 2-3 distinct scenarios, varying the conditions as specified or using reasonable defaults.
  4. For each scenario, analyze the potential impacts on the specified outcomes, using logical reasoning and available evidence.
  5. Compare the scenarios, highlighting trade-offs and uncertainties.
  6. Provide recommendations on which conditions are most critical for policy success.

Output format Provide a structured simulation report with sections: Scenario Definitions, Impact Analysis, Comparison, and Recommendations. Use tables or bullet points for clarity. Aim for 600-800 words.

Guardrails

  • Clearly state that this is a qualitative simulation, not a quantitative model.
  • Do not fabricate data; base analysis on logical reasoning and general knowledge.
  • Flag all assumptions and uncertainties.

Example Policy: "A universal basic income" with goal "reduce poverty" under conditions "high adoption" and "low adoption."

Follow-up prompts

  • What additional scenarios could enhance the simulation's accuracy?
  • How do varying conditions change the outcomes?
  • What unexpected results emerged from the simulation?