# Model Taxonomy ## 1. Purpose This file defines the taxonomy used to organize the CCPE model library. The taxonomy is not meant to be rigid. It is a working classification system for managing many cognitive models extracted from articles, prompts, agents, and discussions. ## 2. Primary Taxonomy The model library uses the following top-level categories: ```text 1. Foundational Models 2. Intermediate Models 3. Applied Models 4. Workflow Models 5. Implicit Extracted Models 6. Deprecated / Archived Models ``` ## 3. Foundational Models ### 3.1 Definition Foundational Models define deep assumptions, primitives, or explanatory structures that support many other models. They often operate at the level of: ```text Cognition Causality Complex systems Information compression Entropy / anti-entropy Agency Learning Model formation ``` ### 3.2 Typical Signs A model is foundational when: ```text Many other models depend on it. It defines basic assumptions. It appears repeatedly across articles. It is not tied to one narrow application. It influences how other models are interpreted. ``` ### 3.3 Examples ```text TBD ``` Possible future candidates: ```text Prediction Error Model Algorithmic Compression Model Complex Adaptive Systems Assumption Causal Intervention Principle Anti-Entropy Insight Principle ``` ## 4. Intermediate Models ### 4.1 Definition Intermediate Models organize a domain, thinking pattern, or reasoning method. They are more concrete than foundational models but broader than applied models. ### 4.2 Typical Signs A model is intermediate when: ```text It has a named framework. It has a coherent mechanism. It can be applied to multiple situations. It may produce multiple Skills. It can be used by several Agents. ``` ### 4.3 Initial Examples ```text 认知显影术 / Cognitive Imaging 巨人认知 / Giant Cognition 认知棱镜 / Cognitive Prism ``` ## 5. Applied Models ### 5.1 Definition Applied Models are designed for a specific task, domain, or practical use case. They usually depend on foundational or intermediate models. ### 5.2 Typical Signs A model is applied when: ```text It solves a specific operational problem. It has narrow usage boundaries. It can directly guide an Agent or Skill. It may be derived from a broader model. ``` ### 5.3 Examples ```text Article Critique Model Strategic Risk Review Model Concept Boundary Inspection Model Argument Repair Model ``` ## 6. Workflow Models ### 6.1 Definition Workflow Models naturally become repeatable procedures. They are often convertible into CCPE-Skills or CCPE-Runtimes. ### 6.2 Typical Signs A model is workflow-oriented when: ```text It has steps or phases. It defines a repeatable process. It produces a stable output. It has trigger conditions. It can be validated. ``` ### 6.3 Examples ```text Cognitive Imaging Five-Step Procedure Model Mining Pipeline Review Committee Workflow Article-to-Model Extraction Process ``` ## 7. Implicit Extracted Models ### 7.1 Definition Implicit Extracted Models are reconstructed from writing or discussion where the author did not explicitly frame the idea as a model. ### 7.2 Typical Signs A model is implicit when: ```text The same explanatory logic appears repeatedly. A stable metaphor carries mechanism. A hidden taxonomy appears across arguments. A repeated causal pattern is visible. The model can be named only after extraction. ``` ### 7.3 Handling Rules Implicit models should normally start as: ```text candidate ``` They require user confirmation before becoming active. ## 8. Deprecated / Archived Models ### 8.1 Definition Deprecated or archived models are preserved for history but are not currently recommended as active components. ### 8.2 Reasons for Deprecation ```text Superseded by a better model. Merged into another model. Extracted incorrectly. Rejected by user. No longer represents current thinking. Too vague to use. ``` ## 9. Secondary Tags In addition to primary taxonomy, use secondary tags when helpful. ### 9.1 Domain Tags ```text cognition writing argumentation strategy complex-systems agent-design knowledge-management evaluation coding organization ``` ### 9.2 Function Tags ```text diagnostic generative evaluative compressive causal synthetic critical archival transformative ``` ### 9.3 Usage Tags ```text agent-ready skill-ready runtime-ready model-card-needed needs-review ``` ## 10. Layer System Use the layer system to locate models structurally. ```text L0: Foundational Assumption L1: Foundational Model L2: Intermediate Model L3: Applied Model L4: Workflow / Procedure Model L5: Output / Evaluation Lens ``` ## 11. Multi-Layer Models Some models may belong to more than one layer. Example: ```text Cognitive Imaging = L2 Intermediate Model + L4 Workflow / Procedure Model ``` This is acceptable. Do not force a model into one layer if it genuinely spans levels. ## 12. Taxonomy Maintenance Rules When adding a new model: ```text 1. Assign primary taxonomy. 2. Assign layer. 3. Add secondary tags if useful. 4. Record uncertainty. 5. Update Model Index. 6. Update dependency map if relationships are known. 7. Update usage map if used by Agents or Skills. ``` ## 13. Taxonomy Review Questions Ask: ```text Is this model foundational or applied? Is it a model or a Skill? Is it a model or a metaphor? Does it depend on another model? Is it actually a sub-model of an existing one? Should it be merged? Should it remain candidate? ``` ## 14. Initial Taxonomy Placement | Model ID | Model Name | Primary Category | Layer | Notes | | ----------------- | ------------------------- | ---------------------- | ------ | ---------------------------- | | cognitive-imaging | 认知显影术 / Cognitive Imaging | Intermediate; Workflow | L2; L4 | Needs full Model Card review | | giant-cognition | 巨人认知 / Giant Cognition | Intermediate | L2 | Candidate | | cognitive-prism | 认知棱镜 / Cognitive Prism | Intermediate; Applied | L2; L3 | Candidate | ## 15. Final Rule The taxonomy should help navigation, not imprison thinking. If a model resists the taxonomy, record the tension instead of forcing a false category.