The Epistemological Trilogy - AI Conversations on the Limits of Information, Knowledge, and Understanding
posted: 21-Feb-2026 & updated: 03-Mar-2026
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If these questions about knowledge and understanding lead you to wonder about the deeper mathematical structures that transcend both information and minds, please refer to The Inevitabilities Trilogy - AI Conversations on Mathematical Truths That Transcend All Possible Universes for my companion trilogy examining the mathematical inevitabilities that would remain true even if consciousness, universes, and information itself never existed.
In my ongoing exploration of the fundamental nature of knowledge and understanding, I’ve embarked on what I call an epistemological trilogy—a journey that began with discovering why partial information can be more dangerous than complete ignorance, progressed through recognizing that even complete information remains insufficient for genuine understanding, and culminated in uncovering universal truths through mathematical structures like shadow prices in optimization theory. This trilogy represents my attempt to bridge the gap between mechanical knowledge and the kind of deep comprehension that transcends mere information accumulation—the difference between knowing facts and truly understanding reality.
- Partial information is not (necessarily) better than ignorance - Wisdom of Strategic Ignorance @ 04-Sep-2025
- Nor is Complete Information Sufficient! @ 29-Dec-2025
- Shadow Prices and Genuine Understanding - A Journey Through the Soul of Optimization @ 20-Feb-2026
What you’ll discover in these AI-generated podcast conversations is a fascinating meta-experiment – I’ve used NotebookLM, Google’s AI software service, to generate discussions about the very limitations of AI and information processing. There’s a beautiful irony here—artificial intelligence exploring the boundaries of its own understanding, algorithms discussing why algorithmic approaches fall short of wisdom, and machine learning confronting the irreducible mystery of genuine insight. These podcasts themselves become evidence of the central thesis – they can process, synthesize, and present information about my work, but the “lightning strike” moments of understanding that inspired these essays remain uniquely human.
The conversations weave together insights from the Convex Optimization Forum with philosophical investigations into consciousness, meaning, and the nature of knowledge itself. Through exploring mathematical concepts like shadow prices—the hidden values that optimization problems reveal about resource scarcity and opportunity costs—these discussions illuminate how even our most rigorous analytical tools point toward truths that transcend pure computation. The podcasts serve as both accessible entry points into these ideas and demonstrations of how AI can help us articulate the very mysteries it cannot solve.