OIDA is a proposed framework that represents organizational knowledge as epistemic Knowledge Objects with class-specific importance decay and signed contradictions, plus a QUESTION mechanism that surfaces modeled ignorance via inverse decay.
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2 Pith papers cite this work. Polarity classification is still indexing.
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LLMs relocate rather than eliminate trade-offs among generality, accuracy, and simplicity, shifting complexity to infrastructure, compliance, and expertise and redefining competitive advantage around managing that shift.
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Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure
OIDA is a proposed framework that represents organizational knowledge as epistemic Knowledge Objects with class-specific importance decay and signed contradictions, plus a QUESTION mechanism that surfaces modeled ignorance via inverse decay.
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From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI
LLMs relocate rather than eliminate trade-offs among generality, accuracy, and simplicity, shifting complexity to infrastructure, compliance, and expertise and redefining competitive advantage around managing that shift.