Principles
2,201 principles · page 36 of 46
Track override frequency as diagnostic data—when multiple…
Track override frequency as diagnostic data—when multiple children override the same parent property, restructure the hierarchy rather than accumulating exceptions.
Restructure hierarchies at the point of friction rather…
Restructure hierarchies at the point of friction rather than proactively across the entire system to keep costs small and signals fresh.
Flatten hierarchies before deepening them—add nesting levels
Flatten hierarchies before deepening them—add nesting levels only when flat organization with rich cross-references proves insufficient.
When cross-cutting concerns dominate your hierarchy…
When cross-cutting concerns dominate your hierarchy, reorganize along a different axis rather than forcing items into categories they only partially fit.
Design your primary hierarchy to optimize for the access…
Design your primary hierarchy to optimize for the access pattern you use most frequently, then use tags, links, or AI search to surface alternative organizations on demand.
Generate alternative hierarchical organizations of the same…
Generate alternative hierarchical organizations of the same data periodically to identify blind spots in your default structure.
Recognize that choosing a primary hierarchy is a priority…
Recognize that choosing a primary hierarchy is a priority decision that reveals what you are optimizing for by making certain retrievals easy and others difficult.
Design every information artifact with explicit compression…
Design every information artifact with explicit compression layers where each level provides sufficient context to decide whether to drill deeper, following the pattern: executive summary (30 seconds), section abstracts (5 minutes), full detail (on demand).
When presenting complex information to diverse audiences…
When presenting complex information to diverse audiences, create multiple entry points at different abstraction levels rather than a single linear document, allowing each reader to start at their appropriate depth.
Contain information within a document when the reader must…
Contain information within a document when the reader must understand it to grasp the parent context; reference information when it serves multiple contexts, changes independently, or would bloat the parent beyond usability.
When building retrieval systems, limit each hierarchical…
When building retrieval systems, limit each hierarchical level to 5-8 choices and total depth to 3-4 levels, as exceeding these bounds degrades both retrieval performance and cognitive processing regardless of content quality.
When multiple valid hierarchies exist for the same data…
When multiple valid hierarchies exist for the same data, choose the one that surfaces what you need to act on most frequently at the top level, not what seems most 'logical' or academically correct.
Articulate schemas as falsifiable claims with specified…
Articulate schemas as falsifiable claims with specified conditions, measurements, and thresholds before exposing them to evidence.
Seek disconfirming evidence first when testing schemas…
Seek disconfirming evidence first when testing schemas, because cognitive default is confirmation-seeking and only disconfirming evidence can distinguish valid from invalid models.
Restate personal identity schemas as behavioral predictions…
Restate personal identity schemas as behavioral predictions with specified conditions to convert unfalsifiable beliefs into testable hypotheses.
When auxiliary hypotheses are adjusted after prediction…
When auxiliary hypotheses are adjusted after prediction failure, require the adjustment to generate new testable predictions rather than merely explain away the original failure.
Design experiments where you specify the falsification…
Design experiments where you specify the falsification criteria and record predictions before running the test, because memory reconstruction will otherwise align past beliefs with known outcomes.
Record predictions with specified confidence levels before…
Record predictions with specified confidence levels before outcomes are known, then track calibration across predictions to identify domains of systematic over- or under-confidence.
Diagnose which component of your schema failed by examining…
Diagnose which component of your schema failed by examining the pattern of prediction errors rather than treating each failure in isolation.
Require schemas to make specific, falsifiable predictions…
Require schemas to make specific, falsifiable predictions about future observations rather than merely explaining past events post-hoc.
Treat prediction errors as diagnostic information about…
Treat prediction errors as diagnostic information about schema structure rather than evidence about personal capability, to maintain the psychological conditions that allow learning from failure.
Document the schema that generated a failed prediction along
Document the schema that generated a failed prediction along with the prediction itself, so you can identify which specific assumptions or variables were wrong rather than just noting the error.
When adjusting schemas after prediction failures…
When adjusting schemas after prediction failures, distinguish between calibration adjustments (schema correct in structure, wrong in parameters) and structural revisions (missing variables or wrong causal model).
Design validation tests to target the boundaries where…
Design validation tests to target the boundaries where your schema is most likely to break rather than the center where it is most likely to hold.
Treat explanations of your schemas to others as active…
Treat explanations of your schemas to others as active validation tests rather than passive information transfer, using objections as diagnostic data about hidden assumptions.
Seek interlocutors with different experiential bases…
Seek interlocutors with different experiential bases and cognitive frameworks from your own rather than those who share your priors when validating schemas.
Design action-based schema tests with falsifiable…
Design action-based schema tests with falsifiable predictions, defined actions, observation protocols, and pre-specified revision triggers to distinguish genuine testing from confirmation theater.
Decompose complex schemas into independently testable atomic
Decompose complex schemas into independently testable atomic claims and validate each atom before trusting the compound structure.
Validate schemas at progressively larger scales, holding…
Validate schemas at progressively larger scales, holding constant what has already been tested while introducing one new variable or context at a time.
Make small, affordable tests of schema components where…
Make small, affordable tests of schema components where failure produces information rather than catastrophe, treating each test as an option to continue, revise, or abandon.
Actively search for conditions under which your schema would
Actively search for conditions under which your schema would fail and test those conditions specifically, rather than testing only where success is likely.
Define in advance what evidence would falsify your schema…
Define in advance what evidence would falsify your schema and commit to that standard before collecting data, preventing post-hoc rationalization of ambiguous results.
Structure your knowledge system to preserve and surface…
Structure your knowledge system to preserve and surface contradictions, counterarguments, and disconfirming evidence rather than curating only supporting material.
Attack your highest-confidence schemas most aggressively…
Attack your highest-confidence schemas most aggressively, because confidence indicates attachment that blinds you to flaws.
Shift validation questions from future-conditional ('what…
Shift validation questions from future-conditional ('what could go wrong?') to past-definite ('it failed—why?') to bypass motivated reasoning.
Allocate validation effort by multiplying probability…
Allocate validation effort by multiplying probability of being wrong by impact of being wrong, not by treating all uncertainties equally.
Generate multiple independent observable consequences…
Generate multiple independent observable consequences of a schema, then evaluate whether those consequences converge or diverge across different evidence types.
When direct testing is impossible, look for the schema…
When direct testing is impossible, look for the schema that explains the most diverse types of observations under a single mechanism.
Validate important self-schemas using at least three…
Validate important self-schemas using at least three independent evidence types: behavioral observation, external feedback, and outcome tracking.
When one piece of evidence contradicts four confirming…
When one piece of evidence contradicts four confirming pieces, treat the divergence as diagnostic of either measurement error or a boundary condition, not as noise to dismiss.
Choose schema reviewers who think differently…
Choose schema reviewers who think differently from you, not those who share your framework, because divergent perspectives surface blind spots that similar perspectives cannot see.
Present schemas for review using three diagnostic questions
What assumption am I missing? What would disconfirm this? What alternative explains the same observations?
Receive schema feedback without defending for at least 24…
Receive schema feedback without defending for at least 24 hours, because immediate response activates argumentation mode rather than evaluation mode.
Document validation results immediately…
Document validation results immediately and contemporaneously, before hindsight bias rewrites your memory of what you predicted or expected.
Record five components for each validation
the schema before testing, the test performed, what you predicted, what actually happened, and what it means for the schema.
Write validation records in specific observational language…
Write validation records in specific observational language ('she pushed back on three of five points') not interpretive language ('she mostly agreed'), to prevent retroactive interpretation drift.
Review validation logs periodically to identify…
Review validation logs periodically to identify meta-patterns—systematic tendencies in how your schemas fail—rather than treating each validation as an isolated event.
Document disconfirmations more carefully than confirmations…
Document disconfirmations more carefully than confirmations, because confirmations are naturally retained in memory while disconfirming evidence is filtered out.