Knowledge Packs — Stunspot’s Guide to Game Theory
This repository separates navigation from corpus.
docs/contains GitHub Pages navigation and usage guidance.knowledge-packs/by-report/contains the canonical individual source-report corpus.knowledge-packs/compiled-packs/contains grouped upload packs.knowledge-packs/omnibus/contains the full canon as one bundle.
Do not create or expect docs/reports/. The reports live in knowledge-packs/by-report/.
Recommended Default
Use the compiled packs unless you have a specific reason not to. They preserve the canon sequence while keeping upload count low.
| Pack Type | Files | Best Use | Use When |
|---|---|---|---|
| Source reports | 12 | Precise retrieval, selective upload, citation, editing, source repair. | Your tool handles many files well, or you need report-level provenance. |
| Compiled packs | 4 | Default AI/RAG upload format. Balanced coverage with lower file count. | You want strong structure without a single giant file. |
| Omnibus | 1 | One-file import, local archive, full-corpus search, strong long-context systems. | Your tool prefers one large source or you want an archival copy. |
Pack Selection by Workflow
| Workflow | Recommended Pack | Rationale |
|---|---|---|
| ChatGPT Project / custom assistant knowledge | Compiled packs | Four files usually fit upload constraints while keeping the sequence legible. |
| NotebookLM-style corpus work | Compiled packs or source reports | Use compiled packs for fewer files; source reports when source-level citation matters. |
| Vector database / RAG ingestion | Source reports | Smaller canonical units make retrieval, metadata filtering, and citation auditing cleaner. |
| Long-context manual analysis | Omnibus | One complete file reduces file handling friction. |
| Corpus editing or source repair | Source reports | Edit canonical source units, not compiled convenience bundles. |
| Local search or archive | Omnibus + manifest | One searchable file plus explicit mappings. |
Source Reports
These are the canonical individual units of the corpus.
- A. The Geometry of Strategic Conflict — An Ontology of Incentives, Interdependence, and Equilibrium.md
- B. Equilibrium and Information — Stability, Beliefs, and Strategic Knowledge Systems.md
- C. Strategic Representation and Formalization — Modeling Strategic Reality with Matrices, Trees, Information Sets, and Type Spaces.md
- D. Sequential Strategy and Dynamic Commitment — Timing, Credibility, and Subgame Structure.md
- E. Bargaining, Coalition Pressure, and Surplus Division — Strategic Settlement Under Conflict and Mutual Dependence.md
- F. Repeated Interaction, Reputation, and the Shadow of the Future — Cooperation, Punishment, and Relational Stability.md
- G. Mechanism Design and Institutional Engineering — Designing Rules So Incentives Produce Desired Outcomes.md
- H. Evolutionary and Population Game Dynamics — Strategy Without Deliberation.md
- I. Networked Games, Externalities, and Systemic Inefficiency — Coordination Failure, Congestion, and the Price of Anarchy.md
- J. Strategic Failure and Diagnostic Analysis — Hidden Incentives, Fragility, and System Reconstruction.md
- K. System Reconstruction and Applied Strategic Method — Modeling Workflow, Validation, and Decision Support.md
- L. Institutional Evolution and Strategic Ecology — Long-Run Adaptation, Succession, and Civilizational Stability.md
Compiled Packs
These are grouped convenience files for upload-constrained systems.
- [KNOWLEDGE] - Game Theory - Vol. 1 A-C Foundations of Strategic Reality.md — foundations, equilibrium, information, and representation.
- [KNOWLEDGE] - Game Theory - Vol. 2 D-G Major Operating Domains.md — sequence, commitment, bargaining, repeated interaction, reputation, and mechanism design.
- [KNOWLEDGE] - Game Theory - Vol. 3 H-J Constraint and Specialization Layers.md — evolutionary dynamics, networks, externalities, systemic inefficiency, and failure diagnosis.
- [KNOWLEDGE] - Game Theory - Vol. 4 K-L Diagnostic and Execution Layers.md — reconstruction, applied method, validation, decision support, and institutional evolution.
Omnibus
The omnibus is the whole canon in one file. Use it for archive, local search, and long-context systems that perform well with a single large source.
Ingestion Notes
For RAG pipelines, attach metadata before chunking where possible:
{
"canon": "Stunspot's Guide to Game Theory",
"version": "1.0",
"pack_type": "source_report | compiled_pack | omnibus",
"report_code": "A-L where applicable",
"repository_path": "knowledge-packs/...",
"license": "CC BY-NC-SA 4.0"
}
The report code is useful because the canon is sequential. A model answering from Report K should know that Reports A-J define the primitives it is using.