Thinking·OS

Graph explorer

Explore the knowledge graph

Analogy depends on Pattern. Activate to inspect this relation.Hypothesis precedes Experiment. Activate to inspect this relation.Prediction is derived from Hypothesis. Activate to inspect this relation.Experiment supports Prediction. Activate to inspect this relation.Mathematical model enables Prediction. Activate to inspect this relation.Machine learning is a Algorithm. Activate to inspect this relation.Machine learning depends on Pattern. Activate to inspect this relation.Training data enables Machine learning. Activate to inspect this relation.Machine learning enables Generalization. Activate to inspect this relation.Generalization enables Prediction. Activate to inspect this relation.Generalization is analogous to Analogy. Activate to inspect this relation.Overfitting suppresses Generalization. Activate to inspect this relation.Overfitting depends on Training data. Activate to inspect this relation.Feedback influences Machine learning. Activate to inspect this relation.Machine learning depends on Optimization. Activate to inspect this relation.Supervised learning is part of Machine learning. Activate to inspect this relation.Supervised learning depends on Training data. Activate to inspect this relation.Supervised learning enables Prediction. Activate to inspect this relation.Supervised learning enables Generalization. Activate to inspect this relation.Optimization applies to Mathematical model. Activate to inspect this relation.GeneralizationPredictionMachine learningOverfittingAnalogySupervised learningHypothesisExperimentMathematical modelTraining dataAlgorithmPatternOptimizationFeedback
Relationship types
Legend
  • Focused concept
  • Connected concept
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
14 concepts20 relationships32 disciplines7 relation families

Generalization

Open concept →

At a glance

A model's ability to perform well on new, unseen cases — not just the examples it was trained on.

Disciplines
Machine Learning · Statistics · Cognitive Science
Role in the graph
Cross-disciplinary bridge — reaches Artificial Intelligence, Computer Science, Data Science, Education, Educational Science, Linguistics, Literature, Mathematics, Philosophy, Philosophy of Science
Relationships
5 · 2 relation families
Mental models
1

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 32 disciplines: Algorithms, Artificial Intelligence, Biology, Business, Cognitive Science, Computer Science, Data Science, Design, Discrete Mathematics, Economics, Education, Educational Science, Engineering, Epistemology, History of Science, Human-Computer Interaction, Linguistics, Literature, Logic, Machine Learning, Mathematical Modelling, Mathematics, Optimization, Philosophy, Philosophy of Science, Physics, Software Engineering, Statistics, Systems Biology, Systems Engineering, Systems Science, Theory of Computation.
  • Generalization is a bridge concept — viewed here through Cognitive Science, Machine Learning, Statistics.
  • The connections here span 7 relation families.
  • Causality explains 4 concepts in this view (Biology, Educational Science, Epistemology, History of Science, Logic, Mathematical Modelling, Mathematics, Philosophy of Science, Physics, Statistics, Systems Science).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.