The Computational Epistemology of Dyadic Formation is a computational philosophy project using Agent-Based Modeling to explore how knowledge and logic shape social dyads. The research investigates the critical impact of information asymmetry and bounded rationality on the fairness and efficiency of dyad formation.
- Koorosh Nobakhtfar (@KCyrusNF) — Lead Researcher
This research utilizes a dual-licensing framework designed to safeguard the integrity of empirical findings while facilitating the reuse of its open-source software and underlying agent-based models.
1. Source Code (CC BY-NC-SA 4.0)
All functional source code and logic located within the models/ directory and its sub-directories are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
This includes:
- Python Scripts: All
*.pyfiles containing agent logic and simulation frameworks.
Terms:
- Modification & Sharing: You are free to adapt and build upon the code.
- Non-Commercial: You may not use the code for commercial purposes.
- ShareAlike: If you build upon this material, you must distribute your contributions under the same license.
2. Research Output (CC BY-NC-ND 4.0)
To maintain the integrity and provenance of the study, the primary project documentation, all materials within the docs/ and logs/ directories (whether located at the repository root or nested within individual model sub-directories), and all explanatory README files throughout the repository are licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International.
This includes, but is not limited to:
- Manuscripts & Documentation: The main project
README.md, all formal reports, manuscripts, PDFs, LaTeX source files (.tex), and descriptiveREADME.mdfiles located within model sub-directories. - Visual Assets: All research figures, charts, diagrams, plots, and vector graphics (
.png,.svg,.pdf). - Empirical Evidence: All raw simulation outputs, interaction records, system logs, and generated datasets (
.log,.csv,.json).
Terms:
- Integrity: This prevents the modification of raw evidence and research conclusions while allowing for broad, non-commercial academic distribution.
- Non-Commercial: The research findings, data, and visual assets may not be used for commercial purposes.
- No-Derivatives: If you remix, transform, or build upon these materials, you may not distribute the modified material. This ensures the data remains an immutable record of the study.
| Asset Category | Primary Locations | Local License | Official Deed |
|---|---|---|---|
| Simulation Code | models/ |
LICENSE-CODE | CC BY-NC-SA 4.0 |
| Documentation | README.md, models/**/README.md |
LICENSE | CC BY-NC-ND 4.0 |
| Research Output | docs/, logs/ |
LICENSE | CC BY-NC-ND 4.0 |
If you use this software, raw data, or research findings in your own work, please cite it as follows:
APA 7 Style: Nobakhtfar, K. (2026). Dyad Epistemics: The Computational Epistemology of Dyadic Formation [Research repository]. GitHub. https://github.com/KCyrusNF/Dyad-Epistemics
IEEE Style: K. Nobakhtfar, Dyad Epistemics: The Computational Epistemology of Dyadic Formation, research repository, GitHub, 2026. [Online]. Available: https://github.com/KCyrusNF/Dyad-Epistemics
MLA 9 Style: Nobakhtfar, Koorosh. Dyad Epistemics: The Computational Epistemology of Dyadic Formation. Research repository, GitHub, 2026, github.com/KCyrusNF/Dyad-Epistemics.
MHRA Style: Koorosh Nobakhtfar (@KCyrusNF), ‘Dyad Epistemics: The Computational Epistemology of Dyadic Formation’, GitHub, [Pub-Day] [Pub-Month] [Pub-Year] https://github.com/KCyrusNF/Dyad-Epistemics [accessed [Acc-Day] [Acc-Month] [Acc-Year]].