CESI视角下的黑暗森林广播和威慑度曲线剖析 - Judgment Substitution as Existential Risk: An IWJL-Based Interpretation of The Three-Body Problem

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Authors: Lucas Xiaochun Xu
Cited by: 0

Abstract

Abstract As artificial intelligence and automated coordination systems increasingly mediate human decision-making, a critical class of failures has emerged that cannot be explained by model error, bias, or insufficient human-in-the-loop design. This paper reframes Liu Cixin’s The Three-Body Problem trilogy as a high-density cognitive simulation of judgment failure and survival under extreme irreversibility. Grounded in the Co-Evolutionary Science of Intelligence (CESI) framework, this study interprets the “Dark Forest” paradigm as a Judgment-Centric Epistemic Niche (J-CEN) in which decision-making is constrained primarily by irreversibility, path-dependence, and lethal information asymmetry. Within such environments, judgment cannot be substituted by optimization, collective consensus, or artificial intelligence without inducing systemic collapse. Using the Irreversibility-Weighted Judgment Loop (IWJL) as the core analytical construct, this paper evaluates key characters in The Three-Body Problem as embodied judgment architectures. It demonstrates that existential survival correlates not with intelligence, morality, or technological capability, but with the integrity of judgment execution under irreversibility. Particular attention is given to Judgment Loop Displacement (JLD) as a first-order failure mode, wherein judgment authority is implicitly transferred from human agents to narratives, systems, or deferred processes. This analysis positions The Three-Body Problem not as speculative fiction, but as a rigorous thought experiment revealing structural laws of judgment, governance, and extinction risk in AI-augmented civilizations.

Fields of Study & Subjects

Utopian Dystopian and Speculative Fiction Ethics and Social Impacts of AI Space Science and Extraterrestrial Life

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
27 January 2026
Document type
Preprint