The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Classify your evidence for each relationship as verified, plausible, or assumed, and prioritize testing the assumed relationships that drive consequential decisions.
Use the swap test for every relationship: if reversing the direction changes the meaning, the relationship is directed and must preserve the arrow in your representation.
When a relationship appears bidirectional, map it as two separate directed arrows with potentially different strengths and mechanisms rather than collapsing it into a single undirected connection.
For causal relationships, establish direction through intervention or counterfactual reasoning rather than correlation alone, because observational data cannot distinguish causation from association.
Invest maintenance effort disproportionately in weak ties that bridge clusters, because they provide structural value but are most vulnerable to decay through neglect.
For knowledge relationships, separate epistemic confidence (evidence quality) from identity centrality (emotional importance) when scoring strength, because these dimensions often diverge and conflating them corrupts decision-making.
Break learning goals into explicit prerequisite chains before attempting them, working bottom-up from verified fundamentals rather than top-down from aspirations.
When learning fails repeatedly despite effort, trace backward through prerequisite chains to find the missing foundation rather than pushing harder on the target skill.
Identify and invest disproportionately in enabling conditions that create cascading improvements across multiple downstream outcomes rather than targeting each outcome independently.
Validate enabling relationships by articulating the specific mechanism through which one condition creates another, not just their co-occurrence.
Surface and explicitly label contradictions between beliefs rather than resolving them prematurely, treating the tension as data about incomplete frameworks.
Map the boundary conditions under which each side of a contradiction holds true rather than attempting to determine which side is universally correct.
Before attempting to resolve any contradiction, construct the strongest possible argument for each side in a form that advocates of each position would recognize as their own.
Verify that supporting evidence comes from genuinely independent sources by tracing each back to its origin and checking for shared roots.
Ground every abstract concept in at least three concrete examples from different domains to enable pattern recognition and transfer.
Sequence instruction from concrete enactive experience through iconic representation to abstract symbolic notation rather than starting with abstractions.
Test understanding of abstractions by generating explanations using only concrete examples without definitions or jargon.
Re-ground abstractions periodically with fresh concrete examples from recent experience to prevent drift between symbols and referents.
Break complex causal explanations into chains of individually verifiable mechanisms rather than stopping at the first plausible cause.
Test each link in a causal chain by asking whether removing that link would plausibly prevent the downstream effect.
When a causal chain closes into a loop where effects feed back to influence their own causes, predict behavior by analyzing the loop's structure rather than any individual link.
Identify whether a feedback loop amplifies or stabilizes by counting the number of negative (opposite-direction) links: even count including zero produces amplification, odd count produces stabilization.
When a balancing feedback loop contains a delay between action and effect, expect the system to oscillate through overshooting and overcorrecting.
Intervene in feedback loops by changing the structure of connections (adding, removing, or redirecting links) rather than trying harder at individual nodes.