standard-block-auditor 0.1.2

A quantitative framework for detecting budgetary distortion.

Standard Block Comparison Method

A quantitative auditing framework to break free from "number magic" and measure the true effectiveness of administrative measures.

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📖 Overview

Huge figures announced by governments and corporations, such as "cumulative total of X people" or "budget of Y billion yen," are often used to obscure the reality (ROI). This project provides the "Standard Block Comparison Method (SBCM)," a technique to mathematically determine whether a measure functions as social infrastructure or is merely a statistical "error" by breaking these numbers down to the smallest unit: the "Standard Block" (Basic Municipality).

This repository contains the SBCM calculation tools and a Python library for detecting "budgetary distortion" in financial data.

🚀 Installation

This tool is available as a Python package. You can install it using pip:

pip install standard-block-auditor

(Note: If you registered it under a different name, please replace the package name accordingly.)

⚡ Quick Start

After installation, you can use the following commands directly from your terminal.

1. Verify a Single Metric (sbcm-calc)

Calculate the impact of a number (users or budget) announced by the government on a national scale.

# Example: "3,000 users" (Target: Total population)
sbcm-calc --value 3000 --target_ratio 1.0

# Example: "10 Billion JPY Budget"
sbcm-calc --value 10000000000

2. Audit Financial Statements (sbcm-audit)

Load financial data and automatically detect projects that are "High Cost but Low Reach" (Quadrant 4).

1. Prepare a CSV file like example_data.csv (can be generated via AI prompts).
2. Run the command:

# Specify city population (e.g., Kashiwa City: 435,000)
sbcm-audit example_data.csv --pop 435000

This outputs an analysis file (distortion_analysis_result.csv) and a visualization graph (distortion_matrix.png).

3. Statistical Verification (sbcm-verify)

Verify the robustness of the results using Monte Carlo simulations to counter arguments about estimation errors.

# Example: 3,000 users, Target ratio 15% (±3% uncertainty)
sbcm-verify --value 3000 --target_ratio 0.15 --ratio_sd 0.03

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📐 Methodology

Effectiveness Impact ($I$)

The index measuring the reach of a policy.

$$ I = \frac{V}{B} $$

The Standard Block ($B$) is defined based on Japan's demographics and administrative structure:

$$ B = \frac{P \times R}{N} $$

• $P$: Total Population of Japan (124 Million)
• $N$: Total Municipalities (1,718)
• $R$: Target Attribute Ratio
• $B \approx 72,176 \times R$ (people)

The Verdict Criteria

Impact ($I$)	Verdict	Benchmark
$I < 1.0$	Error Level	Out of scope. Cannot even cover one standard municipality.
$1.0 \le I < 17$	Localized	Experimental phase. Not even reaching early adopters.
$172 \le I < 859$	Infrastructure	Becoming a foundational public service (e.g., Water, Electricity).
$I \ge 859$	Social OS	Majority adoption. A prerequisite for modern society.

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📊 Budget Portfolio Analysis

An extension of SBCM that visualizes the balance between "Invested Tax (Budget)" and "Achieved Outcome (Coverage)."

Budget Distortion Index ($D_{index}$)

$$ D_{index} = \frac{I_{budget}}{I_{coverage}} $$

Based on this index, projects are classified into four quadrants:

Quadrant	Status	Verdict
Q1 (High Cost / High Reach)	Infrastructure	Appropriate. Expensive but used by everyone (Roads, Waste collection).
Q2 (Low Cost / High Reach)	Innovation	Excellent. Low budget, high impact (Digital transformation).
Q4 (High Cost / Low Reach)	【Distortion】	Audit Required. Huge taxes vanishing into the pockets of a few.

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📂 Directory Structure

Standard-Block-Comparison-Method/
├── sbcm/                  # Python Package Source Code
│   ├── __init__.py
│   ├── block_calculator.py
│   ├── budget_distortion_analyzer.py
│   ├── sensitivity_analysis.py
│   ├── config.py          # Shared Constants
│   └── visualizer.py      # Graph Plotting Module
│
├── prompts/               # AI Auditor Prompts
├── reports/               # Case Studies & Reports
├── example_data.csv       # Sample Data
├── google_sheets_script.js # Google Apps Script
├── pyproject.toml         # Package Configuration
└── README_EN.md           # This Document

📝 License

This project is released under the MIT License. Feel free to use it for administrative verification and auditing.

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Author

Melnus (GitHub: Melnus)