Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Under stress, ask AI what you are not seeing…
When using AI during high stress, prompt with 'I am stressed and may be experiencing tunnel vision—what am I likely not seeing?' rather than 'prove my interpretation is right.'
Check whether you are estimating frequency or vividness
When making frequency or probability estimates, pause and ask: 'Am I estimating actual frequency, or how easily I can picture this?' then look up the base rate before deciding.
Before big decisions, separate the recent trigger…
Before any consequential decision, populate two mandatory fields: the recent event driving current feeling, and the base rate/historical trend across the full relevant time window.
When a vivid case drives certainty, ask for the base rate…
When a vivid individual case makes you feel certain about probability, explicitly ask 'what is the actual frequency of this event in the relevant population?' before forming any judgment.
Frame pre-mortems as explanations of failure…
Frame pre-mortem prompts as 'It is [future date]. This has failed completely. Write why.' rather than 'What could go wrong?' to shift cognition from speculation to explanation.
If your search for disconfirming evidence cannot change…
When searching for disconfirming evidence, if your search could not have actually changed your mind, you performed a ritual not genuine disconfirmation—redesign the search until failure is possible.
Classify evidence by diagnostic value before updating…
When new evidence arrives, classify it by diagnostic value before updating—ask whether you'd see this evidence regardless of belief truth versus only if belief were true/false.
After 15-20 judgments, check if your errors are bias…
After accumulating 15-20 judgments in the same domain, analyze whether errors cluster directionally (bias requiring correction factor) or scatter randomly (noise requiring aggregation).
Keep a two-week bias journal to build your personal error…
Conduct a two-week bias journal recording significant judgments with confidence levels, then categorize errors by direction and type to build your personal bias profile.
Write a specific pre-correction procedure for each known…
For each identified bias in your profile, write a specific pre-correction question or procedure to execute before acting on judgments in that domain.
After four weeks, check if your beliefs under-react…
After four weeks of belief tracking, examine whether beliefs barely moved despite evidence (conservatism) or swung dramatically on single data points (base rate neglect) to identify domain-specific updating patterns.
Run a five-question context scan before interpreting…
Before interpreting any piece of information—a message, a metric, a statement, a data point—run a five-question context scan: What environment am I in? What role am I occupying? What just happened that might color my perception? What are the goals (mine and others')? What assumptions am I importing from a different context?
Record five elements at the moment of every significant…
Record decision context at the moment of commitment using five elements: (1) decision statement, (2) forces/constraints/emotions active at choice point, (3) expected consequences with timeline, (4) confidence level 1-10, (5) review trigger date—before hindsight bias can rewrite your reasoning.
Read the original decision context before evaluating…
When reviewing a past decision, read the original context record before evaluating the outcome, because evaluating outcome first allows hindsight bias to contaminate your assessment of whether the reasoning was sound.
If you deliberated for 60+ seconds, the reasoning is worth…
Set the threshold for decision context documentation at any choice where you deliberated between options for more than sixty seconds, because if you considered alternatives consciously, the reasoning is worth preserving against memory reconstruction.
Before adopting a practice from another era or scale…
When importing best practices or frameworks from another era or scale, explicitly verify that the contextual conditions (organizational size, technological infrastructure, market maturity) that made the practice optimal still hold before adopting it.
Require independent operational definitions of key terms…
For each high-stakes word in decisions or commitments (quality, ownership, alignment, done, strategy), require independent operational definitions from each stakeholder before proceeding, then compare and reconcile the definitions explicitly.
Audit the training data before trusting AI decisions about…
When an AI system makes consequential decisions about people (hiring, performance evaluation, resource allocation), audit what organizational context and metrics trained the system before evaluating algorithm quality, because AI inherits and amplifies the biases of the measurement system.
Write your position before group discussion — compare…
Before committing to a private written position for any group decision, externalize your reasoning and conclusion before the group discussion begins, then compare it to your post-discussion position to detect social influence effects.
Chesterton's Fence
Before removing any inherited system, process, or organizational structure, document why it was originally created and what problem it solved—if this context cannot be reconstructed, you lack sufficient information to safely remove it.
Reconstruct the information environment from records…
Before evaluating any past decision, reconstruct the information environment that existed at decision time using contemporaneous records rather than memory, then evaluate the decision against that environment only.
Record decisions with five fields
Document decisions using five fields: what you decided, alternatives considered, information available and missing, optimization criteria, and conditions for revisiting—rather than recording only conclusions.
Have AI argue against your decision before finalizing…
Before finalizing significant decision records, have an AI argue against your reasoning and append the strongest objection to your record, preserving the full deliberation rather than only your preferred conclusion.
Cross-reference domains in weekly review
During weekly reviews, cross-reference externalized domains to detect contradictions—compare stated priorities against time allocation, goals against commitments, assumptions against failure analyses—because isolated review of each domain misses the conflicts that degrade decision quality.