Truth-Discernmemt Canon

Truth Discernment Canon (Future Builders Academy, McGilchrist-Informed)

Informed by work of Ian McGilchrist

• Principle 1: Reality First – Physical reality is the final authority; all models must be tested against it
  • Practice: For every build, define measurable outputs (e.g., power, flow, strength) and verify with instruments
  • Exercise: Compare predicted vs measured performance and document deviation

• Principle 2: Build–Predict–Test–Update Loop – Knowledge comes from iterative interaction with reality
  • Practice: Require predictions before any build or modification
  • Exercise: Maintain a prediction log and update models after testing

• Principle 3: Context Before Conclusion – Understanding requires defining the system context
  • Practice: Before analysis, specify system boundaries, inputs, outputs, and constraints
  • Exercise: Draw system diagrams for each project and revise after testing

• Principle 4: Observation vs Inference vs Assumption – Separate what is seen from what is interpreted
  • Practice: Tag all statements in design reviews as observation, inference, or assumption
  • Exercise: Audit past decisions and identify where assumptions were mistaken for facts

• Principle 5: Model ≠ Reality – Representations are approximations, not truth
  • Practice: Validate all models with real-world data
  • Exercise: Identify where a model fails under edge conditions

• Principle 6: Feedback Calibration – Accuracy improves through error measurement
  • Practice: Track prediction error over time for key parameters
  • Exercise: Calculate percent error and refine models to reduce it

• Principle 7: Multi-Perspective Integration – Truth emerges from combining viewpoints
  • Practice: Analyze systems from mechanical, economic, and user perspectives
  • Exercise: Reframe the same system in at least three domains and compare insights

• Principle 8: Translation Test – True understanding survives multiple representations
  • Practice: Require explanation of systems in words, diagrams, equations, and physical demos
  • Exercise: Peer-teach a concept using all four forms

• Principle 9: Disconfirmability – Truth must be testable and falsifiable
  • Practice: Define what evidence would prove a design or claim wrong
  • Exercise: Design and run a failure test for each system

• Principle 10: Error as Data – Mistakes are primary inputs for learning
  • Practice: Conduct regular failure reviews without blame
  • Exercise: Document top 3 errors per week and resulting design changes

• Principle 11: Abstraction Discipline – Use abstraction only when validated
  • Practice: Tie every abstraction to measurable reality
  • Exercise: Identify where abstraction hides critical variables

• Principle 12: Reality Audits – Prevent drift into narrative
  • Practice: Weekly review of assumptions vs outcomes
  • Exercise: List “what we thought” vs “what actually happened”

• Principle 13: Adversarial Collaboration – Opposing views refine truth
  • Practice: Assign teams to argue different hypotheses
  • Exercise: Resolve disagreement through experiment, not debate

• Principle 14: Embodied Knowing – Understanding requires direct interaction
  • Practice: Rotate roles so all participants engage physically with systems
  • Exercise: Hands-on replication of a system before theoretical discussion

• Principle 15: Attention Training – Quality of perception determines quality of knowing
  • Practice: Begin sessions with short observation drills (e.g., detailed inspection of a component)
  • Exercise: Describe a system without using labels, only direct observation

• Principle 16: AI as Assistant, Not Authority – External models must be verified
  • Practice: Use AI for hypothesis generation only
  • Exercise: Test at least one AI-generated idea in reality

• Principle 17: Epistemic Integrity – Updating beliefs is a strength
  • Practice: Publicly revise models when new data contradicts them
  • Exercise: Track instances where you changed your mind and why

• Principle 18: Production as Epistemology – Building is a method of knowing
  • Practice: Treat every build as an experiment
  • Exercise: Document what was learned about reality from each project

• Bottom Line – Mastery of truth discernment comes from continuous, disciplined interaction with reality through building, testing, and integrating multiple perspectives