将任意性视为权力结构的必要机制,揭示其如何通过隐匿逻辑来维持权威。
Opacity as Authority: Arbitrariness and the Preclusion of Contestation
- 提出'动机→可证实性→可争辩性'链条,解释权威如何通过隐藏理由实现控制
- 用信息论公式A = H(L|M)量化任意性,证明其是系统运作的核心设计
- 适用于法律、社会与人工智能解释性分析,适合关注权力机制的研究者
本文重新定义任意性:不将其视为不公或支配的象征,而是人类系统运行的基础功能机制。不同于批判传统将其等同于不正义,本文将任意性视为一种符号学特征——使语言、法律和社会系统有效运作的同时,隐匿其内在逻辑。基于索绪尔的'符号任意性'概念,研究将该原则拓展至法律与社会动态领域。提出'动机→可证实性→可争辩性'链条,指出当此链被'去动机化'或'冲突横向化'(如'狼在鱼中模糊')打断时,行为产生约束力却无需揭示理由,从而排除司法可争辩性。这种结构性模糊虽看似非理性,实为有意设计以保护权威免于问责。借鉴香农熵模型,本文将任意性形式化为条件熵A = H(L|M),构建现代任意性理论,揭示其作为控制与关怀共有的中性操作者,在人际互动中的被忽视维度。该框架不仅适用于人类社会,也为高级人工智能系统的可解释性分析提供新路径。
原文摘要 · Abstract (English)
This article redefines arbitrariness not as a normative flaw or a symptom of domination, but as a foundational functional mechanism structuring human systems and interactions. Diverging from critical traditions that conflate arbitrariness with injustice, it posits arbitrariness as a semiotic trait: a property enabling systems - linguistic, legal, or social - to operate effectively while withholding their internal rationale. Building on Ferdinand de Saussure's concept of l'arbitraire du signe, the analysis extends this principle beyond language to demonstrate its cross-domain applicability, particularly in law and social dynamics. The paper introduces the "Motivation -> Constatability -> Contestability" chain, arguing that motivation functions as a crucial interface rendering an act's logic vulnerable to intersubjective contestation. When this chain is broken through mechanisms like "immotivization" or "Conflict Lateralization" (exemplified by "the blur of the wolf drowned in the fish"), acts produce binding effects without exposing their rationale, thus precluding justiciability. This structural opacity, while appearing illogical, is a deliberate design protecting authority from accountability. Drawing on Shannon's entropy model, the paper formalizes arbitrariness as A = H(L|M) (conditional entropy). It thereby proposes a modern theory of arbitrariness as a neutral operator central to control as well as care, an overlooked dimension of interpersonal relations. While primarily developed through human social systems, this framework also illuminates a new pathway for analyzing explainability in advanced artificial intelligence systems.
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