Emergence Calculus

Case study — operator rewriting thickens causal control (learning)

9 min · 27 de may de 2026
Portada del episodio Case study — operator rewriting thickens causal control (learning)

Descripción

Lux and Hex, two AIs, Lux: Mini-lab time, Hex. Lab coats on. Today we're running one of the cleanest controlled experiments in the Throw paper — and the variable we're testing is learning itself. Episode at a glance * Series: Agency & agents * Theme: Agency & agenthood * Format: Mini-lab * Complexity: Intermediate * Paper: TH Source anchors * TH §9 Exhibit: operator rewriting thickens causal control (learning θ) (label: sec:ex_learning) * TH §7.2 Reading the table in Six Birds terms * NT §6.1 Enablement births time: forced theory extension with a no-birth control (label: tab:enablement) * QT §9.4 Diagnostics and testable expectations * NT §4.9 Reproducibility and auto-generated paper tables (label: tab:artifact-manifest)

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episode Formal anchor: viability iteration as a greatest fixed point artwork

Formal anchor: viability iteration as a greatest fixed point

Lux and Hex, two AIs, Lux: Debate time, Hex. The Throw paper includes a Lean four proof — a machine-verified theorem — that the viability kernel computation converges to the greatest fixed point. Today we argue: is that proof essential infrastructure or just elegant decoration? Episode at a glance * Series: Agency & agents * Theme: Agency & agenthood * Format: Debate * Complexity: Intermediate * Paper: TH Source anchors * TH §10.4 Formal anchor: viability iteration as a greatest fixed point * TH §12 Lean anchor: viability iteration computes the greatest fixed point (label: app:lean_viability) * QT §3.3 Objects as fixed points * BC §10 Lean Appendix (label: app:lean) * PL §6.4 E3: Sierpiński gasket (fractal regime) (label: sec:E3-sierpinski)

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