🔍 Focus: Valid, reliable, and transparent psychometric instruments · Algorithmic analysis of social interaction sequences
An open-source personality inventory for transition-class students 📊 Valid · Reliable · Objective · Transparent
Since 2012, the Düsseldorf Student Inventory (DÜSK) has served as an open learning and research platform for:
- Students of the social sciences
- Trainees in market and social research
- Developers and data analysts
- ✅ Full source code (PHP, MySQL, Xamarin, Lazarus, etc.)
- ✅ Open raw data + SPSS / R datasets
- ✅ Cross-platform: Web · PC · Android · iOS
- ✅ Designed for research, teaching, and applied practice
📌 Collaboration welcome Joint development and distribution via app stores (Google Play, App Store, Amazon).
Algorithmic Recursive Sequence Analysis for Explainable AI in Qualitative Social Research
ARS_ExplainableAI is a methodological and software-based framework for Algorithmic Recursive Sequence Analysis (ARS).
It integrates qualitative hermeneutics with formal modeling and contributes to Explainable Artificial Intelligence (XAI) in text and interaction analysis.
This repository contains:
- Complete scientific papers on ARS methodology (German / English)
- Python implementations for grammar induction from terminal symbol sequences
- Advanced network modeling via transformation into Petri nets and Bayesian networks
- Compressing principles: repetition, recursion, symmetry, hierarchy
- Optimization algorithms for iterative adjustment of transition probabilities
- Eight transcripts of sales conversations as an empirical corpus
Qualitative social research faces a methodological dilemma: Generative AI systems promise scalability but evade classical validation due to their opacity.
ARS_ExplainableAI addresses this challenge through:
- Transparent model construction — every interpretative step is explicitly documented
- Formalization of qualitative processes — interpretations are transformed into terminal symbol sequences
- Explainable network models — compressive transformations into Petri and Bayesian networks
- Recursive self-application — AI as an epistemic agent reflecting on its own interpretations
ARS_ExplainableAI ist ein methodologisches und softwaretechnisches Framework zur Algorithmisch Rekursiven Sequenzanalyse (ARS).
Es verbindet qualitative Hermeneutik mit formaler Modellierung und leistet einen Beitrag zur erklärbaren Künstlichen Intelligenz (XAI) in der Text- und Interaktionsanalyse.
Dieses Repository enthält:
- Vollständige wissenschaftliche Aufsätze zur ARS-Methodologie (Deutsch / Englisch)
- Python-Implementierungen zur Grammatikinduktion aus Terminalzeichenketten
- Erweiterte Netzmodellierung durch Transformation in Petri-Netze und Bayessche Netze
- Komprimierende Prinzipien: Wiederholung, Rekursion, Symmetrie, Hierarchie
- Optimierungsalgorithmen zur iterativen Anpassung von Übergangswahrscheinlichkeiten
- Acht Transkripte von Verkaufsgesprächen als empirische Basis
Die qualitative Sozialforschung steht vor einem methodologischen Dilemma: Generative KI-Systeme versprechen Skalierung, entziehen sich jedoch aufgrund ihrer Opazität der klassischen Validierung.
ARS_ExplainableAI begegnet diesem Problem durch:
- Transparente Modellbildung — jeder Interpretationsschritt wird explizit dokumentiert
- Formalisierung qualitativer Prozesse — Überführung von Lesarten in Terminalzeichenketten
- Erklärbare Netzmodelle — komprimierende Transformation in Petri- und Bayessche Netze
A rule-based method for causal inference using action grammars and graphs.
Sales Dialogue Analysis & Grammar Induction
- Optimized transition probabilities (Python)
- Multi-Agent-System (MAS) integration
- LLM-assisted category generation
Key Notebooks
- Grammar tools (Lisp / Scheme)
- Parser implementations (Pascal)
- Original transcripts and audio material (vkg1.mp3)
I provide
- Source versions (PHP, Xamarin, Android Studio, etc.)
- Manuals and documentation
You handle
- Distribution via app stores or web servers
- Revenue-sharing agreement
Ways to collaborate:
- Improve GUI / UX design
- Create tutorials (YouTube, technical documentation)
- Expand calibration samples
- Port software to new environments (Eclipse, NetBeans, etc.)
💬 Let’s collaborate on transparent, evidence-based psychometrics.
ARS bridges
- Karl Popper’s principle of falsifiability
- Ulrich Oevermann’s objective hermeneutics
- Computational rigor (Bayes · Pearl · Chomsky)
“Unlike postmodern hermeneutics, ARS combines Lisp-style recursion, Python-based scalability, and R-driven statistics to model social sequences as explainable graphs.”
Click to expand
- English: Seeking collaborators for open-source psychometric tools and ARS development.
- Français: Recherche de collaborateurs pour des inventaires de personnalité open-source.
- Español: Modelos de gramática accional para el análisis de diálogos.
- 中文: 寻求开源心理测量工具与 ARS 方法的合作伙伴。
This trilogy is not for everyone:
- no explosions or chase scenes
- no heroes or villains
- no confirmation of your worldview
A philosophical thought experiment disguised as a technical thriller — about posthumanism, algorithms, and the future of democracy.
If you expect entertainment, you will be disappointed. If you expect to think, you will be challenged.
“Every page demands your thinking — not just your excitement.”
The Last Freedom / Die letzte Freiheit Your brain will not be spared. / Ihr Gehirn wird nicht verschont.
- GitHub: @pkoopongithub
