V2 Silence as a Formal Output Why Some Futures Must Not Be Predicted

This paper establishes silence as a formally legitimate output in systems subject to irreversible structural constraints. Contrary to dominant assumptions in forecasting, decision theory, and artificial intelligence—where every input is expected to yield a prediction, recommendation, or optimization—we show that certain regimes structurally forbid any coherent algorithmic or causal output. Silence is not treated as ignorance, lack of data, or computational failure, but as a necessary and principled outcome when accessibility, causal closure, or descriptive coherence collapses within a given reference frame. Building on recent results concerning relative causal closure and accessibility regimes, the paper demonstrates that forcing predictions beyond specific thresholds produces contradictions, false authority, or ethical violations. Silence is thus introduced as a boundary condition of legitimate description, with implications for futures studies, AI governance, and the ethics of anticipation.