Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
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.
Map the incentive structure before blaming individuals…
When encountering a frustrating recurring behavior in your organization, map the actual incentive structure (what gets rewarded, punished, and measured) before attributing the behavior to individual character, because most problematic behaviors are rational responses to system design.
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.
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.
Sit with cognitive dissonance for 60 seconds before…
Set a tolerance window of at least sixty seconds to sit with cognitive dissonance before attempting resolution, as premature resolution systematically favors existing schemas over new evidence.
Ask 'how long have similar tasks taken?' not 'how long…
When planning task duration, deliberately switch from inside-view scenario construction to outside-view base-rate consultation by asking 'how long have similar tasks taken?' instead of 'how long will this take?'
Check subgroup relationships before aggregating…
Before aggregating data across subgroups, check whether the relationship holds within each subgroup independently, as aggregate patterns can reverse at the disaggregated level (Simpson's paradox).
Audit agents with hourly momentary sampling…
Use hourly momentary sampling over 48+ hours rather than end-of-day recall when auditing behavioral agents, because retrospective memory systematically overweights salient successes and underweights invisible failures.
Assign criterion weights before scoring options — knowing…
When building a decision matrix, assign criterion weights before scoring any options to prevent unconscious adjustment of weights toward your pre-existing preference.
Score by criterion across options, not by option across…
Score one criterion at a time across all options rather than one option at a time across all criteria, to force apples-to-apples comparison and reduce halo effects.
After irreversible commitments, schedule external reviews…
After making irreversible commitments, schedule reviews with someone uninvolved in the original decision and define success/failure criteria in advance, because escalation of commitment will corrupt your post-decision assessment.
Design pre-commitments when calm to constrain behavior…
Design pre-commitment rules during cold cognitive states (well-rested, calm, not under deadline pressure) to constrain behavior during hot cognitive states (stressed, depleted, emotionally activated), never vice versa.
Before purchasing, name what else the money could buy…
Before committing to any purchase decision, explicitly name what else that money could buy in different categories (not just competitor products), because mental retrieval naturally limits alternatives to within-category competitors and misses the highest-value cross-category tradeoffs.
Trace why you trust each source
Trace each trusted source to whether trust stems from demonstrated expertise, emotional resonance, social proof, first-encounter effects, or algorithmic repetition to detect unexamined authority delegations.