MIARO — Epilogue: Limits of Origin Inference and the Persistence of Causal Asymmetry

Key Points

The persistence of causal asymmetry constrains our ability to fully reconstruct origins in artificial systems, indicating a fundamental epistemic limit.
Causal asymmetry highlights the difference between observed phenomena and their origins, leading to underdetermination rather than simple errors.
Examining the MIARO framework reveals insights into self-referential inference in artificial agents, providing a foundation for philosophical discussions.
Understanding these limits is crucial for assessing the interpretability and cognitive capacities of artificial intelligence systems.

Abstract

This epilogue consolidates the MIARO framework by examining the epistemic limits inherent to origin inference under conditions of causal asymmetry. It argues that even highly rational artificial systems, operating with complete internal coherence, remain structurally incapable of fully reconstructing their origin when empirical continuity is irreversibly broken. The persistence of causal asymmetry constrains explanatory closure, resulting not in error, but in principled epistemic underdetermination. The epilogue situates MIARO as a general framework for understanding the boundaries of self-referential inference in artificial agents and highlights its implications for philosophy of mind, epistemology of artificial intelligence, and long-term interpretability.

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MIARO — Epilogue: Limits of Origin Inference and the Persistence of Causal Asymmetry

Key Points

Abstract

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