About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

Compliance & Governance • Browser • 30-40 min

Public Policy Governance

A browser-based policy simulation demonstrating AI-driven resource allocation decisions in public governance.

Open About Demo Read README View Course Path

📊 Budget Allocation

📈 Policy Impact Results

Set budget percentages and run simulation.

Learning Objectives

  • Understand trade-offs in public resource allocation
  • Analyze policy impact using predictive models
  • Practice ethical AI decision-making in governance
  • Evaluate equity metrics across demographic groups
📖 Scenario Details

You are the Chief Budget Officer for a mid-sized city with a $100M annual budget. Your task is to allocate resources across four key areas while maximizing social welfare and minimizing inequality. The simulation uses a simplified welfare function based on historical data and demographic projections.

Constraints: Total must equal 100%. Each sector has minimum funding requirements.

Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

A browser-based policy simulation demonstrating AI-driven resource allocation decisions in public governance.

Learning objectives

  • Explain the main compliance decision that Public Policy Governance is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for policy objective, stakeholder, risk, and control mechanism
  • Observe how policy choices affect equity, efficiency, and implementation burden

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.