Reusable cognitive primitives
Information
Anything that reduces uncertainty. It can be encoded into a signal, sent across a channel, and decoded — and noise can corrupt it on the way.
- Claude Shannon — Founded · 1948
Shannon's 1948 paper “A Mathematical Theory of Communication” founded information theory and defined information in terms of entropy.
Sources: Encyclopaedia Britannica
Where it appears
Dependencies & synergies
Derived from the graph’s real structure — 33 concepts across 38 disciplines carry this pattern. Every figure is a count, not a score.
How many concepts carrying this pattern are seen through each discipline.
Other thinking patterns that recur on the same concepts — the more shared concepts, the more often they co-occur.
What kinds of relationships the carrying concepts form — the relational signature of the pattern.
Concepts where this pattern does the most cross-disciplinary work — each reaches disciplines beyond its own.
- Machine learningreachesAlgorithmsBiochemistryBiologyBiotechnologyBusinessCognitive ScienceData ScienceDesignDiscrete MathematicsEconomicsEducationEducational ScienceEngineeringEvolutionary BiologyHuman-Computer InteractionLogicMathematical ModellingMathematicsNeuroinformaticsNeuroscienceOptimizationProbabilitySoftware EngineeringSystems BiologySystems EngineeringSystems ScienceTheory of Computation
- AlgorithmreachesArtificial IntelligenceBiochemistryBioinformaticsBiologyCell BiologyComputational BiologyComputational ComplexityData StructuresEvolutionary BiologyGeneticsHistoryInformation TheoryMachine LearningMolecular BiologyNumerical AnalysisOptimizationStatistics
- DNAreachesAlgorithmsAstrobiologyBiologyBiotechnologyCommunication ScienceComputational BiologyComputer ScienceData StructuresDiscrete MathematicsHistoryInformation TheoryLogicMathematicsMicrobiologySoftware EngineeringTheory of Computation
- Neural signalreachesAnthropologyCognitive ScienceCommunication ScienceComputer ScienceDiscrete MathematicsElectrical EngineeringHuman-Computer InteractionInformation TheoryLinear AlgebraMachine LearningMathematicsNetwork ScienceSociologyUrban Planning
- SequencereachesAlgorithmsBiochemistryBiologyCell BiologyCognitive ScienceDesignEducationEducational ScienceEvolutionary BiologyGeneticsLogicMachine LearningSoftware EngineeringTheory of Computation
- NeuronreachesAnatomyBiophysicsBiotechnologyCell BiologyCognitive NeuroscienceCognitive PsychologyMedicineNeuroinformaticsNeurologyPhysiologyPsychology
Carrying concepts that others in the same pattern build on (they depend on or follow from these).
- Algorithm — 2 other concepts build on it
- Gene — 1 other concept build on it
- Training data — 1 other concept build on it
Examples across disciplines
A file is a string of bits; each bit halves the receiver's remaining uncertainty.
DNA encodes the instructions for building proteins, copied and passed on across generations.
A score encodes a performance so a musician far away can reconstruct the composer's intent.
How this pattern travels
Interpreted from where the pattern recurs in the atlas — structural transfer and coverage, not a claim it is universally the "best" model.
Recurs across 38 disciplines over 33 carrier concepts — a broadly transferable pattern.
structural · Structural recurrence in the atlas — a pattern is a reasoning lens, not a law.
16 of its 33 carrier concepts are themselves cross-disciplinary connectors.
structural · Structural recurrence in the atlas — a pattern is a reasoning lens, not a law.
Explicit "where it breaks" notes exist for 3 of 5 annotated assignments.
curated · Curated boundary annotations — absence is a representation gap, not evidence the model has no limits.
Coverage matrix
How these concepts distribute across domains and concept families — real counts, not a score.
Knowledge timeline
Real, sourced key dates of these concepts.
- c. 300 BCEAlgorithm · First use · Computer Science
- c. 820 CEAlgorithm · Formalization · Computer Science
- 1936Turing machine · Formalization · Computer Science
- 1953DNA · Discovery · Molecular Biology
The statistical fingerprint
How the 33 concepts that exercise this pattern distribute — from the graph, not a ranking.
Disciplinary fingerprint
Carrier concepts under each illuminating lens.
How settled its carriers are
Epistemic status of the concepts that exercise this pattern.
Where the model breaks
This pattern is a reasoning lens, not a law. Here is where it stops helping:
- Bit depth (structural pattern): Bit depth bounds dynamic range and quantisation noise, not fidelity as such — more bits past the noise floor of the source add capacity, not information.
- Spectrogram (reasoning tool): A spectrogram displays information but imposes a time–frequency trade-off (the window): sharper in time means blurrier in frequency, and vice versa.
- Turing machine (structural pattern): A model of what is COMPUTABLE, not what is feasible — it ignores time and space cost, concurrency and interaction with the world.
1 of 5 explained assignments cite a source; the rest are editorial interpretations. None is externally validated.
✕ That more data always means more information; repeated or predictable data adds little.
Journeys where it shows up
You have seen this model in one place. Where else could it apply — and where would the analogy break?
Algorithm →Ask: what did the receiver not know before, and what does the message let them rule out?
- Recurs across 38 disciplines.
- 95 concepts exercise this thinking pattern.
- 3 of them explicitly note where the model breaks down.
- It is practised in 1 learning journey.
Derived from the graph — a pattern is a reasoning lens, not a law.
What did the receiver not know before this message, and which possibilities does it now let them rule out?
Keep this question handy when you meet something new — it helps you notice the pattern, not just name it.