Interactive Epistemic Systems Model

AI Proof Churn vs. Generative Inquiry Simulator

"Working on problems has usually led to the asking of yet more questions and the creation of new areas of study. If AI just churns out proofs, that might not happen" — The Economist
Model Presets
Live Parameters
0.85
Propensity to treat conjectures as inert proof obligations
0.30
Probability of spawning open sub-questions during discovery
12
3
Status: Verified Proof Churn Bottleneck
Stagnation Index: 0.74
Root Problem
Spawned Inquiry
Emergent Subfield
Inert Closed Proof
Proofs Generated 142 Deductive closures
Questions Spawned 19 Heuristic branches
Subfields Created 3 New study domains
Stagnation Index 0.74 Inert proof density
Simulation Telemetry: Nodes: 164 | Links: 163 | Active Graph Clustered
Model dynamic: High automated proof churn without interrogation compresses field growth into isolated proof sinks.

Mapping Proof Churn vs. Inquiry Expansion

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In mathematical discovery, each seed problem can either provoke open-ended questions or get sealed into closed proof obligations. At a high proof churn rate of point eight five, automated engines close conjectures rapidly into terminal proof sinks, leaving fewer branching paths. Selecting the Balanced Generative Inquiry preset raises heuristic exploration to point eight zero, spawning open subquestions and new subfields. The stagnation index drops toward point two, shifting the model from an inert deduction bottleneck to continuous epistemic expansion.

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