Stabilizing Runaway Risks in Multi-Agent Rational Systems through Collective Narrative Structures

透過集體敘事穩定化多代理理性系統的失控風險
Stabilizing Runaway Risks in Multi-Agent Rational Systems through Collective Narrative Structures

ISBN 978-626-01-5766-1
作者:Gabriel Chen

© 2025 Gabriel Chen. All rights reserved.

摘要 Abstract

中文摘要

當決策系統由多個理性代理同時運作時,失控風險往往並非來自單一代理的錯誤,而是來自代理之間「理性彼此放大」的結果。每一個代理在其局部視角中皆做出合理判斷,但在缺乏整體約束的情況下,這些判斷會相互強化、加速行動節奏,最終使系統偏離可治理範圍。現行多代理系統多仰賴目標對齊、獎勵設計或通訊協議來降低失控風險,卻忽略一個根本問題:代理之間缺乏可共享的「行動意義結構」。本書提出「集體敘事(Collective Narrative)」作為穩定多代理理性系統的核心方法,將敘事視為一種跨代理的約束結構,使各代理能在維持局部理性的同時,受限於共同的行動邊界與失效條件。透過集體敘事的配置,系統失控不再只是事後調整的問題,而是可被提前抑制的結構性風險。

English Abstract

In multi-agent rational systems, runaway risk rarely originates from individual agent error. Instead, it emerges from the amplification of locally rational decisions across agents. Each agent acts reasonably within its own perspective, yet in the absence of shared constraints, these actions reinforce one another, accelerate system dynamics, and push the system beyond governable boundaries. Existing approaches rely on objective alignment, reward shaping, or communication protocols, but neglect a fundamental issue: agents lack a shared structure of action meaning. This technical brief introduces Collective Narrative as a stabilizing structure for multi-agent rational systems. Narrative is treated as a cross-agent constraint that enables local rationality to operate within shared boundaries of action and failure. Through collective narrative configuration, runaway risk becomes a structurally preventable condition rather than a post hoc correction problem.

關鍵詞 Keywords

• 集體敘事(Collective Narrative)
• 多代理系統(Multi-Agent Systems)
• 理性放大(Rational Amplification)
• 系統失控(Runaway Risk)
• 行動邊界(Action Boundaries)
• 判斷約束(Judgment Constraints)
• 協同行為(Coordinated Action)
• 系統治理(System Governance)

目錄 Table of Contents

1. 為何多代理理性系統容易失控
   Why Multi-Agent Rational Systems Tend Toward Runaway
2. 理性放大而非理性錯誤
   Rational Amplification Rather Than Rational Error
3. 多代理系統中的意義真空
   Meaning Vacuums in Multi-Agent Systems
4. 集體敘事作為跨代理約束結構
   Collective Narrative as a Cross-Agent Constraint
5. 集體敘事如何抑制系統加速
   How Collective Narrative Suppresses System Acceleration
6. 失控前的敘事干預點
   Narrative Intervention Points Prior to Runaway
7. 集體敘事的設計與部署原則
   Design and Deployment Principles of Collective Narrative
8. 結論:理性需要敘事邊界
   Conclusion: Rationality Requires Narrative Boundaries

其他相關資訊請參閱NarrQuest Tone Uprising Journal

https://narrquest.wordpress.com/

https://scholar.google.com/citations?user=zS4SSUkAAAAJ&hl=en

https://orcid.org/0009-0006-0154-6402

This book is one of the following technical series.

模組 A|人 × AI × 決策信任(63–66)

63. 以敘事可解釋性重構人工智慧決策信任的方法

64. 透過責任敘事配置穩定人工智慧輔助決策的歸責機制

65. 以敘事對齊降低人機協作中誤解成本的系統方法

66. 透過集體敘事穩定化多代理理性系統的失控風險

模組 B|科學 × 政策 × 行動落差(67–70)

67. 以敘事行動層將科學證據轉化為可執行政策的方法

68. 透過敘事介面銜接知識、決策與治理流程的設計原則

69. 以風險敘事校準高風險科技集體判斷的治理方法

70. 透過語義重構使永續指標轉化為實際行為的敘事模型

模組 C|教育 × 認知 × 動機崩解(71–74)

71. 以動機敘事工程修復現代學習系統投入失效的方法

72. 透過敘事密度調控緩解人工智慧內容引發的認知疲勞

73. 以敘事負荷模型穩定理解歷程避免認知崩解的方法

74. 透過知識敘事再配置重建後教育時代學習主權

模組 D|組織 × 協作 × 語義錯位(75–77)

75. 以語義敘事對齊消除組織行動分歧的結構方法

76. 透過敘事同步機制修復跨部門運作失衡

77. 以協作敘事調控改善高密度會議下的決策品質

模組 E|公共溝通 × 去極化 × 社會穩定(78–80)

78. 以去極化敘事工程降低社會衝突強度的方法

79. 透過語氣治理穩定公共理解而不改變事實內容

80. 以集體敘事穩定機制防止社會系統性崩潰

The NarrQuest Judgment Infrastructure Series addresses a systematically neglected problem in high-density systems: when models, knowledge, institutions, and communication capacity become saturated, how can judgment remain bearable, refusible, accountable, and closable. Compared to the previous series, which primarily articulated how narrative shapes understanding, meaning, and sovereignty, this series advances to a more critical threshold—whether judgment retains minimum governability when understanding alone can no longer keep systems operational. Narrative is no longer treated as a cognitive or interpretive tool, but redefined as a structural function: Judgment Infrastructure. The series specifies four non-negotiable minimum conditions—exitability, refusability, accountability, and closure—and applies them across engineered analyses of human–AI decision support, science-to-policy translation, education and cognition, organizational collaboration, and public communication. In doing so, it moves narrative from theory to structural floor, clarifying which systems, when these conditions are unmet, simply lack the legitimacy to claim trustworthy decisions. Here, stability is not consensus; it is the capacity to carry judgment through deep disagreement and uncertainty without collapse.