Responsibility Futures

Responsibility Futures

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Located in responsibility-futures/. A Python engine that quantifies responsibility through AI-extracted event analysis, integrated with Cortext.io.

Core Philosophy

Responsibility is measurable through the relationship between Intention and Negligence:

R = I / N

Where:
  I (Intention) = Derived from warm vectors (Positivity, Engagement, Optimism)
  N (Negligence) = Derived from cold vectors (Negativity, Risk, Uncertainty)

This creates a quantifiable responsibility index that moves beyond traditional credit scores to measure "social and scope capital."

How It Works

Data Pipeline

Text Input → Cortext.io NLP → JSON Events → Responsibility Engine → HTML Reports + PNG Charts

1. Input Processing — Accepts Cortext.io JSON reports containing sentences with warm/cold sentiment vectors, extracted subjects and phenomena
2. Entity Analysis — Calculates mention frequency, average warm/cold vectors, and contextual concepts per entity
3. Responsibility Calculation
   • Intention Score: Positivity × 0.4 + Engagement × 0.4 + Optimism × 0.2
   • Negligence Score: Negativity × 0.5 + Risk × 0.3 + Uncertainty × 0.2
   • R-Ratio with risk level classification (Low / Moderate / High)
4. Visualization — Generates responsibility matrix dashboards, vector heatmaps, statistical summaries, and HTML reports

Quick Start

# Complete workflow
./run_analysis.sh

# Or manually
python src/enhanced_workflow.py

# Process a specific file
python src/enhanced_workflow.py --input /path/to/extraction_file.json

Source Files

File	Purpose
src/cortext_integration.py	Main engine — loads Cortext.io JSON, extracts entities, calculates R-ratios
src/report_generator.py	HTML/PNG report generation with matplotlib and seaborn
src/enhanced_workflow.py	Complete workflow manager
src/responsibility-futures.py	Original algorithm implementation
src/responsibility-index.py	Core R = I/N logic
examples/example_usage.py	Usage demonstrations

Theoretical Foundation

The Solidarity Index

Based on "Stockholm Forgiveness of Responsibility: A Futures Market" (2019). Key concepts from the docs/ folder:

• Shorting the Solidarity Index — When joining a community, an individual borrows trust ("innocent until proven guilty") and aims to buy back into the position through proven reliability
• Dignity as a Call Option — Internal threshold of self-worth; the right to exit on your own terms
• Esteem as a Put Option — External reputation; the right to cash in on social capital
• Delta Minimization — The system rewards predictable behavior aligned with the Hegemonic Standard, not maximum R-scores. Over-performance (martyrdom) is penalized just like under-performance
• Rationality as Forgiveness — If behavior can be modeled and hedged, it becomes rationalized and forgivable

Risk Classification

R-Ratio Range	Risk Level
R > 5.0	Low Risk
2.0 < R ≤ 5.0	Moderate Risk
R ≤ 2.0	High Risk

Generated Visualizations

1. Responsibility Matrix Dashboard — Risk scatter plot, top entities bar chart, risk distribution pie, visibility bubbles
2. Vector Analysis Heatmaps — Warm/cold vector patterns per entity
3. Statistical Summary — R-ratio distribution, correlation analysis, mention frequency, risk-level box plots
4. HTML Report — Responsive, mobile-friendly report with embedded charts

Integration Ecosystem

• Upstream: Cortext.io Event Code Extractor
• Downstream (planned): Account Ninja (financial decisions from R-scores), Family RM (governance dashboards), iMASS (manufacturing responsibility metrics)

Dependencies

matplotlib
seaborn
numpy
pandas