Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
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.