https://molsen.ca/tags/epistemic-rigor/Recent content in Epistemic Rigor on Mike OlsenHugo -- gohugo.ioen-caSun, 08 Feb 2026 00:00:00 +0000https://molsen.ca/writing/answers-to-critics/Sun, 08 Feb 2026 00:00:00 +0000https://molsen.ca/writing/answers-to-critics/An adversarial review identified weaknesses that cannot be answered by saying the critic misread the framework. Some require refinement. Others concern precision without changing the substance, and some concern tone rather than argument. The AI-First / EKA Tension AI-First Software says the system can “dynamically select algorithms and approaches” and that “the user describes outcomes; the system determines approach.” Executable Knowledge Architecture insists that expert judgment stays in the method, and the AI translates that method into execution.https://molsen.ca/writing/tacit-space-shrinkage/Tue, 17 Dec 2024 00:00:00 +0000https://molsen.ca/writing/tacit-space-shrinkage/Every organization has knowledge that nobody can quite put into words. The machinist who “just knows” when a tolerance is off. The sales director who senses when a deal is going sideways. The nurse who recognizes deterioration before the monitors do. We call this tacit knowledge, and for decades we’ve treated it as a fixed feature of organizational life. Valuable, irreplaceable, and resistant to capture. As AI capabilities expand, less knowledge remains practically inexpressible.https://molsen.ca/writing/agent-relative-tacitness/Tue, 03 Dec 2024 00:00:00 +0000https://molsen.ca/writing/agent-relative-tacitness/Your dog knows the boundaries of its territory, reliable routes through the neighborhood, and where interesting smells accumulate. The dog demonstrates that knowledge through behavior but cannot describe it. Michael Polanyi, who developed the concept of tacit knowledge, used examples like this to describe knowledge we possess but cannot fully articulate: we know more than we can say. Tacit knowledge needs to be understood relative to the agent who has it.https://molsen.ca/writing/strong-oracle-trap/Tue, 26 Nov 2024 00:00:00 +0000https://molsen.ca/writing/strong-oracle-trap/When I describe oracle pattern as ungovernable, people often push back. “That’s not how I use it,” they say. “I don’t just ask a question and accept the answer. I have a conversation. I probe. I challenge. The AI asks me clarifying questions. We iterate.” I call this “strong oracle pattern.” It improves answer quality but leaves the verification problem unresolved. The Oracle Spectrum Oracle use ranges from accepting a single answer to examining a problem through extended dialogue.https://molsen.ca/writing/knowledge-as-capability/Tue, 12 Nov 2024 00:00:00 +0000https://molsen.ca/writing/knowledge-as-capability/For organizations, I define knowledge as the capability to reliably produce outcomes. I use the two expressions interchangeably. I use this as a practical definition for governing human-AI cooperation, without proposing a metaphysical account of what knowledge is. Whether AI has mental states remains largely unanswerable and secondary to organizations’ practical need to work with these systems. For nearly a century, machines have been producing outcomes we’d call “knowledge work” if produced by humans.https://molsen.ca/writing/ai-oracle-vs-assistant/Tue, 05 Nov 2024 00:00:00 +0000https://molsen.ca/writing/ai-oracle-vs-assistant/There are two fundamentally different ways to deploy AI in professional work. The choice between them determines whether you can govern what you’re doing. Oracle pattern: You ask the AI a question. It gives you an answer. You use that answer. Assistant pattern: You tell the AI what you want to accomplish. It produces artifacts that help you accomplish it. You verify those artifacts before using them. Most people default to Oracle pattern.https://molsen.ca/writing/llm-stochasticity-determinism/Fri, 01 Nov 2024 00:00:00 +0000https://molsen.ca/writing/llm-stochasticity-determinism/The criticism that LLMs are “too stochastic for production use” misunderstands their nature. I ran a systematic experiment to demonstrate this. This experiment demonstrates the assistant-pattern mechanism on one task. For broader evidence across reasoning models, retrieval-augmented generation systems, and production deployments, see Update for February 2026. The Experiment Hypothesis: LLM “unreliability” is a usage problem, not a tool limitation. Seven models tested: GPT-4, GPT-4.1, plus mini variants of each Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku 100 iterations per model.https://molsen.ca/writing/critical-thinking-rules/Tue, 29 Oct 2024 00:00:00 +0000https://molsen.ca/writing/critical-thinking-rules/I’m seeing a lot of references to “critical thinking” in my social media feeds, but often those most excited about critical thought also hold ideas that are strong evidence of its absence. “Think critically! Examine everything! The world is flat!” Those people define “critical thinking” differently than I do. I’ve organized my working rules into three domains: Foundation Rules: The attitudes and intellectual humility required for critical thinking. Analytical Rules: Methods for evaluating information, weighing evidence, and reaching conclusions.