graph TB
%% Core Foundation
PM[Pramāṇa<br/>Valid Means of Knowledge] --> |validates| AN[Anumāna<br/>Logical Inference]
PM --> |validates| AP[Arthāpatti<br/>Implicative Reasoning]
PM --> |validates| VN[Vikalpa-nirākāraṇa<br/>Construction Analysis]
%% Reflexive Awareness as Central Hub
SV[Svasamvedana<br/>Reflexive Awareness] --> |monitors| AN
SV --> |monitors| HT[Hetvābhāsa<br/>Fallacy Detection]
SV --> |monitors| VS[Vāsanā<br/>Habit Recognition]
SV --> |calibrates| SS[Saṃśaya<br/>Systematic Doubt]
%% Inference Validation Chain
AN --> |requires| VP[Vyāpti<br/>Invariable Concomitance]
VP --> |tested by| VPX[Vyāpti-parīkṣā<br/>Relationship Examination]
VPX --> |prevents| HT
%% Error Prevention Network
HT --> |triggers| PP[Pratipakṣa<br/>Counteractive Analysis]
PP --> |generates| PS[Prasaṅga<br/>Consequence Analysis]
PS --> |feeds back to| VPX
%% Pattern Validation
SSS[Sādhya-sādhana-sambandha<br/>Means-End Relationships] --> |validated by| PD[Pakṣa-dharma<br/>Subject-Property Verification]
PD --> |checks context for| AN
VS --> |influences| SSS
%% Doubt Resolution Cycle
SS --> |drives| NR[Nirṇaya<br/>Decisive Determination]
NR --> |resolves through| VPX
NR --> |updates| PM
%% Context and Scope
VN --> |distinguishes| PD
PD --> |scopes| VP
%% Habit Interruption
VS --> |interrupted by| PP
PP --> |generates alternatives to| SSS
%% Meta-reasoning Flow
SV -.-> |observes| SV
AP --> |surfaces assumptions for| SS
%% Color coding for concept types
classDef foundation fill:#e1f5fe
classDef process fill:#f3e5f5
classDef validation fill:#e8f5e8
classDef error fill:#ffebee
classDef meta fill:#fff3e0
class PM,VN foundation
class AN,AP,PS process
class VP,VPX,PD,SSS validation
class HT,PP error
class SV,SS,NR,VS meta
Conceptual: Systematic classification of how knowledge is acquired and validated across different sources and methods.
Operationalization:
- Pre-classify context information into source types (direct observation, logical inference, testimony, established knowledge)
- Apply different validation criteria based on knowledge acquisition method
- Track knowledge provenance through graph metadata, linking conclusions back to their epistemological foundations
- Weight edges differently based on source reliability (direct observation > logical inference > testimony)
Conceptual: Cognition's capacity to be aware of its own processes, enabling meta-cognitive monitoring and self-correction.
Operationalization:
- Generate explicit meta-reasoning nodes that observe and comment on reasoning patterns
- Create self-referential edges where reasoning processes become objects of analysis
- Implement confidence calibration based on process awareness rather than just content confidence
- Use Alternative edges to represent awareness of cognitive biases or habitual patterns being applied
Conceptual: Valid inference requires the logical relationship to be present in the current case, verified in similar cases, and absent in dissimilar cases.
Operationalization:
- Before creating Inference nodes, verify supporting evidence through three validation paths:
- Present case verification: Ensure Observation nodes directly support the logical pattern
- Positive confirmation: Reference similar successful applications via Supports edges
- Negative validation: Consider counter-examples through Contradicts or Alternative edges
- Require minimum evidence threshold: each Inference should connect to at least one Observation and one supporting precedent
Conceptual: Understanding the necessary relationship strength between evidence and conclusions.
Operationalization:
- Distinguish relationship types through edge weights: necessary (1.0), sufficient (0.8-0.9), probabilistic (0.3-0.7), weak correlation (0.1-0.3)
- Map logical relationship scope through Alternative edges showing boundary conditions
- Flag when applying weak relationships as if they were strong through Question nodes about relationship strength
Conceptual: Testing the strength, scope, and limits of logical relationships before applying them.
Operationalization:
- For each Supports relationship, generate corresponding Question nodes examining boundary conditions
- Create Hypothesis nodes testing relationship transfer to new domains
- Use Refines edges to elaborate on the specific conditions under which relationships hold
- Implement "stress testing" through Alternative edges showing where relationships break down
Conceptual: Systematic detection of reasoning errors through structural analysis.
Operationalization:
- Circular reasoning detection: Scan for dependency cycles where Inference nodes ultimately depend on themselves
- Ungrounded assertions: Identify high-confidence nodes lacking sufficient Observation support
- Contradictory evidence: Flag reasoning chains containing both Supports and Contradicts edges to the same conclusion
- Weak evidence propagation: Trace paths where low-weight edges accumulate to support high-confidence conclusions
Conceptual: Systematically considering opposing viewpoints and contrary evidence before settling on conclusions.
Operationalization:
- For each Hypothesis node, require at least one Alternative hypothesis connected via Alternative edges
- Generate Question nodes challenging key assumptions in reasoning chains
- Create "red team" validation paths using Contradicts edges to test conclusion robustness
- Implement systematic doubt by ensuring strong conclusions have addressed potential objections
Conceptual: Examining what logically follows from positions and testing consistency across implications.
Operationalization:
- Forward reasoning: For major conclusions, generate subsequent Inference nodes showing logical consequences
- Backward reasoning: Create Question nodes examining what assumptions must hold for conclusions to be valid
- Cross-reference implications using Supports and Contradicts edges to check for internal consistency
- Use Refines edges to elaborate on unintended consequences or logical extensions
Conceptual: Establishing reliable connections between reasoning methods and successful outcomes.
Operationalization:
- Track reasoning pattern success through meta-analysis of previous graph structures
- Validate method applicability by comparing current context to successful precedents via Supports edges
- Generate Question nodes about contextual differences that might affect method validity
- Weight Inference edges based on historical success rates of similar reasoning patterns
Conceptual: Reasoning about what must be true given certain established facts.
Operationalization:
- Generate Inference nodes for implicit assumptions required to make sense of Observation clusters
- Create Question nodes highlighting gaps where missing information would resolve apparent contradictions
- Use DependsOn edges to make explicit the logical requirements underlying conclusions
- Implement necessity reasoning through Hypothesis nodes about unstated prerequisites
Conceptual: Productive uncertainty that drives deeper investigation rather than premature closure.
Operationalization:
- Flag genuine uncertainty areas through Question nodes with specific resolution criteria
- Generate Alternative hypotheses for high-confidence conclusions to test certainty
- Implement uncertainty propagation by lowering edge weights when dependencies are uncertain
- Create investigation pathways showing what additional evidence would resolve doubt
Conceptual: Moving from doubt to warranted conclusion through systematic evidence evaluation.
Operationalization:
- Establish evidence thresholds based on claim significance and consequence severity
- Generate explicit resolution criteria through Question nodes about what would settle uncertainty
- Build confidence incrementally through multiple independent Supports paths converging on conclusions
- Use Answers edges to show how specific evidence resolves particular doubts
Conceptual: Verifying that reasoning patterns actually apply to the specific case being analyzed.
Operationalization:
- Check pattern applicability through Observation nodes confirming essential contextual features
- Generate Question nodes about potential contextual differences that could invalidate reasoning transfer
- Use Refines edges to specify the exact scope conditions under which conclusions hold
- Flag over-generalization through Alternative edges showing boundary cases
Conceptual: Distinguishing between direct evidence and constructed interpretations.
Operationalization:
- Maintain clear node type distinctions: Observations for direct facts, Inferences for constructed interpretations
- Track interpretation layers through DependsOn edges showing reasoning construction steps
- Generate Question nodes about interpretation validity when moving beyond direct evidence
- Use meta-reasoning nodes to monitor when assumptions are being added versus facts reported
Conceptual: Recognizing and interrupting automatic reasoning patterns that may not fit current context.
Operationalization:
- Generate meta-reasoning nodes that identify when default reasoning patterns are being activated
- Create Alternative edges showing different approaches that could be applied to the same evidence
- Implement pattern interruption through Question nodes challenging automatic assumptions
- Use Contradicts edges to surface evidence that doesn't fit expected patterns
- Base reasoning layer: Standard Observation → Inference → Hypothesis progressions
- Relationship validation layer: Systematic checking of edge weights and dependency strength
- Alternative generation layer: Ensuring multiple pathways and counter-perspectives exist
- Meta-cognitive layer: Reasoning about reasoning patterns themselves
- Integration layer: Synthesizing insights with appropriate confidence calibration
- Reasoning completeness: Coverage of logical dependencies and alternative perspectives
- Evidence sufficiency: Cumulative weight of support paths to major conclusions
- Consistency checking: Absence of contradictory support chains
- Uncertainty handling: Appropriate confidence levels propagated through edge weights
- Bias resistance: Presence of counter-arguments and alternative interpretations
- Pre-reasoning: Pattern identification and validation setup
- Mid-reasoning: Real-time consistency checking and alternative generation
- Post-reasoning: Comprehensive consequence analysis and confidence calibration
- Meta-reasoning: Analysis of reasoning quality and pattern effectiveness