Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
When gut and analysis disagree, investigate for 30 minutes…
When formal and intuitive schemas disagree on a decision, investigate the disagreement for thirty minutes rather than defaulting to either—write what your gut is reacting to and test whether it reveals a pattern your formal criteria missed or a bias you haven't examined.
When forced into a binary decision, document the full…
When forced to make a binary decision after spectrum-based deliberation, document the richer multi-dimensional signal alongside the binary outcome so future analysis can recover what the compression discarded.
Before high-stakes categorization, name the actions…
Before committing to a category assignment in high-stakes decisions, explicitly name what actions that category triggers and what you would do differently if the item belonged to an adjacent category.
Identify the deepest assumption in each decision domain…
For each major domain where you make decisions, explicitly write down the single deepest assumption everything else depends on, then list 5-10 decisions that would change if that root were different to verify you've found an actual root.
After updating a belief, find one decision the new model…
After updating a belief, identify one downstream decision where the revised model produces a different recommendation than the old one to ensure the update becomes operational rather than merely verbal.
When two schemas both hold after evidence review, build…
When schema conflict persists after examining evidence, build a conditional routing rule specifying the exact conditions under which each schema applies rather than attempting to pick a universal winner.
When performance is stable with no bottleneck, stop…
When measurement data shows stable satisfactory performance with no identifiable bottleneck, redirect optimization effort to a different system rather than continuing to optimize the current one.
Scale contradiction holding periods by cascade depth — one…
Set holding periods for contradictions based on cascade depth: one week for low cascade, two to four weeks for medium cascade, one month or longer for high cascade.
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).
Apply the problem-vs-polarity test — if new information…
Before attempting to resolve any persistent organizational tension, apply the problem-vs-polarity test: can new information or analysis make one side permanently win? If no, design oscillation management rather than searching for resolution.
Resolve contradictions at the principle level once…
Address contradictions at the strategic principle level rather than re-adjudicating them at each tactical decision point to prevent decision multiplication.
Only automate decisions that are frequent, stable…
Design agents only for decisions that score high on frequency (recurring often), stability (same answer each time), and low individual stakes, because these three properties determine whether automation saves resources without introducing unacceptable risk.
Never automate decisions with genuine per-instance novelty…
Do not automate decisions where the outcome is genuinely different each instance even if the category recurs (interpersonal conflicts, creative problems, novel diagnoses), because automating decisions with genuine novelty produces rigidity disguised as efficiency.
For once-in-career decisions, suspend your agent…
When a decision situation represents a once-in-career strategic pivot rather than a routine recurrence, suspend your decision agent and return to full deliberation even if the agent would produce an answer.
Update decision agents after every activation based…
After each decision agent activation, update the agent's criteria based on whether they produced a good outcome, treating the agent as a living heuristic that improves with each use rather than a permanent law.
Build nutrition agents at meal prep, not consumption…
Build nutrition agents at the meal preparation layer (Sunday evening meal prep) rather than the consumption layer (what to eat when hungry), because deciding what to eat while hungry and facing an open refrigerator is too late to override depletion.
Resolve inter-agent conflicts with documented priority…
When discovering that your designed agents conflict with each other, resolve the conflict through documented priority hierarchies rather than case-by-case deliberation, making the resolution rule itself part of your agent system.
Scale guard clause complexity to false-positive cost…
Match the number of qualifying conditions to the cost of false positives—use minimal guards for low-cost triggers and multiple defensive checks for high-consequence triggers.
Classify every decision as one-way or two-way door before…
Before beginning deliberation on any decision, classify it as one-way door (irreversible/high-stakes) or two-way door (reversible/low-stakes) and allocate analysis time proportionally—minutes to hours for two-way doors, days to weeks for one-way doors.
Use a weighted decision matrix when options exceed 3…
For decisions involving three or more options and four or more criteria, externalize the comparison into a weighted decision matrix rather than relying on intuitive averaging, because working memory cannot hold all dimensions simultaneously.
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.
Every decision framework needs five explicit components
For each decision framework you build, explicitly include five components: evaluation criteria, sequence of evaluation, time budget, kill conditions (automatic disqualifiers), and decision rights (who decides, who is consulted).
For reversible decisions, act at 50-60% information…
For reversible decisions, act when you have 50-60% of desired information because experiential learning from outcomes typically exceeds information gain from additional deliberation.