Applied Epistemic Engineering: Engineering Truth Under Pressure
A new discipline that operationalizes Hume, Popper, and Nakamoto's concepts into a practical, modular framework for everyday applications.
Someone once asked me: “Are you the professor on Applied Epistemic Engineering?”
I replied: “I didn’t give myself the title, but I suppose I would be considered a professor on it since I invented it.”
Curiously, Aster Vérité and I appear to have coined the term Applied Epistemic Engineering independently, just three months apart. Aster Vérité's first public use of it was on May 25 2025 and my first public use of it was on Aug 29 2025. I have no affiliation with Aster Vérité; I simply give credit where credit is due.
The exchange above is more than banter as it's a small demonstration of the discipline itself.
What is Applied Epistemic Engineering?
Applied Epistemic Engineering (AEE) is a new discipline that treats belief systems like code: codable, testable, and deployable. Its purpose is to make hidden assumptions visible, stress-test them under adversarial conditions, and redesign them for resilience.
Where traditional epistemology asks, “How do we know what we know?” Applied Epistemic Engineering asks, “How do we engineer belief systems so they fail safely and recover quickly?”
It operates as:
A debugging tool for conceptual ambiguity.
A systems discipline for optimizing decision-making under uncertainty.
A resilience framework that prevents epistemic collapse in adversarial environments.
Intellectual Lineage
AEE builds on a rich history of philosophical and technical thought. It draws from intellectual lineages like David Hume's empirical skepticism on the limits of induction (Hume, 1748/2007), Karl Popper's principle of falsifiability as the cornerstone of scientific claims (Popper, 1959/2002), and modern systems thinking, such as the elegant incentive designs in Satoshi Nakamoto's foundational blockchain whitepaper (Nakamoto, 2008). However, AEE uniquely operationalizes these concepts into a practical, modular framework for everyday application.
The Core Method
At its heart, AEE is a four-step loop:
Frame — Identify the concept or belief as it currently stands.
Disassemble — Break it into its underlying assumptions and definitions.
Stress-Test — Run it against edge cases, adversarial challenges, or empirical counterexamples (e.g., using simulations or data audits).
Reconstruct — Rebuild the belief in a clarified, more resilient form.
This is not just philosophy, it’s a design discipline. Each cycle improves the reliability of decisions, predictions, and systems, making it reproducible and scalable.
Example Applications
Crypto Modeling: Designing incentives that withstand manipulation, such as auditing token economics for epistemic decay (e.g., predicting market shake-outs in assets like
$SLOW).Policy Debates: Clarifying contested terms to prevent rhetorical deadlock by disassembling assumptions in arguments.
Personal Cognition: Debugging mental models to avoid recurring errors, like reframing cognitive biases through falsifiable tests.
Judicial Systems: Structuring legal reasoning to reduce epistemic failure in verdicts. For example, stress-testing prosecutorial logic to decrease the odds of wrongful convictions and increase the reliability of guilty verdicts. AEE can help courts distinguish between emotional persuasion and evidentiary rigor, improving justice outcomes.
Medical Diagnostics: Reframing symptom clusters and diagnostic criteria to reduce false positives and negatives. AEE can help identify when clinical heuristics fail under edge cases, improving diagnostic precision.
Education Design: Building curricula that teach falsifiability, adversarial reasoning, and cognitive resilience. AEE can help students learn how to model belief systems and pivot when reality diverges from expectation.
AI Alignment: Embedding epistemic stress-testing into machine learning systems to prevent brittle reasoning and hallucinations. AEE can help design feedback loops that detect and correct flawed inference paths.
Conflict Resolution: Disassembling emotionally charged narratives to reveal structural misunderstandings. AEE can help mediators reframe disputes into solvable epistemic mismatches.
Why It Matters
Unlike pure theory, AEE is built for execution. It uses logic, adversarial modeling, and empirical data to:
Expose structural falsehoods.
Forecast robust outcomes.
Reframe incentives toward “everybody wins” equilibria rather than narrow advantage.
In short, AEE is about engineering truth under pressure. It transforms epistemology from passive reflection into active design, addressing gaps in fields like decision theory and systems engineering.
A Worked Example
Let P = “Jamie and Aster are Professors of Applied Epistemic Engineering.”
Let I = “Jamie and Aster invented Applied Epistemic Engineering.”
Let T = “Title ‘Professor’ is assigned by external consensus.”
If I is true, and P is defined functionally (expertise, teaching, authorship) rather than institutionally (¬T), then P is epistemically justified by I, even if ¬T.
This reframing resolves the contradiction and clarifies the claim. Identifying and stress-testing such definitional boundaries is the essence of AEE in action.
Closing Thought
I never thought I’d be a professor of anything, but here we are. My goal isn’t about titles—I couldn’t care less about them. What matters is impact. I want to make the world a better place, and from where I’m sitting, that requires something radical like a separation of emotion from belief systems.
We need to map it out to catch the not-so-obvious bad choices before they cascade into harm and to ensure the not-so-obvious good choices are easier to see, support, and sustain.
The gap between a well-honed intuitive clarity and the collective ambiguity many experience is precisely where Applied Epistemic Engineering provides its value. This capacity for pattern recognition is not a unique gift; it is a skill, a cognitive muscle that can be exercised. I believe everyone possesses this potential, but like any muscle, it strengthens with focused, consistent training.
AEE is the scaffolding for this much-needed radical shift. It helps us debug the hidden structures of thought, bring invisible errors into the light, and design decision systems where truth and resilience win out.
If epistemology gave us the question, AEE is my attempt to engineer the answer.
Next Steps
To explore AEE further, check out practical applications on my Substack (e.g., Putting Scientific Modeling to the Test in Crypto, Restoring Mining Decentralization: A Geo-Dispersion Proof-of-Location Proposal, and The Reflexive Flip: Kaspa’s Window of Disruption).
Applied Epistemic Engineering isn’t just a framework—it’s a frontier. If you’re building systems, modeling truth, or debugging cognition, you’re already part of it. Let’s make it explicit. Let’s make it resilient.
References
Hume, D. (2007). An enquiry concerning human understanding (P. Millican, Ed.). Oxford University Press. (Original work published 1748). https://doi.org/10.1093/oseo/instance.00032980
Popper, K. R. (1959). The logic of scientific discovery. Basic Books.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf