Aegis: The Temporal Policy Auditor

Inspired by the predictive, cause-and-effect mechanics of 'Tenet', the ethical governance systems of Asimov's 'I, Robot', and the data-driven approach of a 'University Rankings' scraper, Aegis is a niche e-government solution designed for small to medium-sized municipalities.

The Story: City councils and local government agencies often legislate in the dark. They propose a new business regulation, a zoning change, or a public service fee, relying on expensive, slow-moving consultant reports and historical precedent. The true impact is only felt months or years later, when it's too late to easily reverse course. Aegis provides these public servants with a 'time machine' for policy, allowing them to see the probable future before they vote on it.

The Concept: Aegis is a 'Policy Sandbox' where officials can test regulations before they become law. It functions on three core principles derived from its inspirations:

1. Data Aggregation (The 'Rankings Scraper' Principle): The system continuously scrapes and standardizes publicly available data from thousands of municipalities — business registrations, permit approval times, public budgets, census data, citizen satisfaction scores, crime rates, etc. This creates a vast, comparative dataset on what policies have led to what outcomes across the country.

2. Ethical Auditing (The 'I, Robot' Principle): A municipality using Aegis first defines its core 'Laws of Governance'— a set of key performance indicators (KPIs) they want to uphold. For example: 'Law 1: Do not increase the administrative burden on small businesses.' 'Law 2: Must not negatively impact affordable housing availability.' 'Law 3: Must maintain or improve public park accessibility.' These laws form the ethical framework against which all simulations are judged.

3. Temporal Simulation (The 'Tenet' Principle): This is the core feature. A policymaker inputs a proposed change (e.g., 'Increase commercial property tax by 0.5%'). Aegis uses its aggregated dataset and a machine learning model to run a simulation, forecasting the cascade of effects over the next 1, 3, and 5 years. It presents a report showing the predicted impact on the local economy, social metrics, and, most importantly, how the proposal scores against the city's own 'Laws of Governance.' It essentially shows the 'inverted' or future consequences of a present-day action.

How it Works:
An individual can start this project by focusing on a single policy area, like building permits or business licensing, in a single state. The MVP would involve: a Python-based scraper (e.g., Scrapy) to collect historical data from city government websites; a machine learning model (e.g., using Scikit-learn or TensorFlow) trained on this data to find correlations between policy changes and outcomes; and a simple web interface (e.g., Flask or Django) where a user can input a hypothetical policy change and see a dashboard of predicted results. The business model is B2G (Business-to-Government) SaaS, with tiered monthly subscriptions based on the municipality's size and the number of policy areas to be analyzed. It's low-cost to start, highly niche, and offers immense value by helping governments avoid costly policy mistakes, making it a high-potential venture.