Information Parasitism and Architectural Ecosystems: A Study of Power Control Mechanisms in Network Environments

Abstract

This paper explores the phenomenon of “information parasitism" in internet information environments, building upon Hiroshi Hamano’s theory of “architectural ecosystems." By integrating ecological perception theory, legal sociology’s architectural control theory, and genetic meme transmission theory, this study proposes the concept of “parasitic chains" to analyze hierarchical power control mechanisms in information environments. The paper argues that in the contemporary era of rapid AI and network technology development, information parasitism has become a crucial conceptual framework for understanding digital social order.

Keywords: Information parasitism, architectural ecosystems, Hiroshi Hamano, information environment theory, power control, meme transmission

1. Introduction

In contemporary digital society, the production, dissemination, and consumption of information have formed complex ecosystems. Hiroshi Hamano, a prominent Japanese information society theorist specializing in information society theory, media theory, network communities, and information environment research, has proposed the “architectural ecosystem" theory that provides an important perspective for understanding network environments. However, existing research primarily focuses on the “evolutionary" aspects of technological architecture, with less attention to “parasitic" phenomena and their power control mechanisms in information environments.

This paper aims to expand Hamano’s information environment theory by proposing the concept of “information parasitism" to explore hierarchical control structures in network ecosystems. Through interdisciplinary theoretical integration, this study attempts to answer: In the context of rapid AI technological development, how does information parasitism become a hidden control mechanism for digital social order?

2. Theoretical Foundation: From Biological Perception to Digital Perception

2.1 The Pluralistic Worldview of Biological Perception

Jakob von Uexküll, in “A Foray into the Worlds of Animals and Humans," proposed the concept of “Umwelt," emphasizing that different organisms construct meaningful environmental worlds distinct from humans through their respective sensory organs. This perspective breaks through the limitations of anthropocentric perception, revealing the relativity and plurality of perception.

Edward Yong, in “An Immense World: How Animal Senses Reveal the Hidden Realms Around Us," further elaborates on the differences in “perceived worlds," pointing out that different organisms form drastically different world experiences due to variations in perceptual depth or bandwidth. Bat echolocation, avian magnetic field sensing, and bee polarized light vision all constitute perceptual dimensions that humans cannot directly comprehend.

2.2 AI Perception and Digital Environmental Worlds

Extending this thinking to the field of artificial intelligence, when AI systems can perceive and process information through APIs (Application Programming Interfaces), the “digital environmental worlds" they construct may contain perceptual dimensions incomprehensible to humans. AI can simultaneously process multiple data streams, identify pattern correlations imperceptible to humans, and perform computations in high-dimensional spaces, forming unique “AI perceptual worlds."

The development of these differential perceptual capabilities provides new perspectives for understanding the complexity of information environments. Just as different organisms experience different environmental worlds in the same physical space, AI and humans may perceive vastly different information environments within the same information space.

2.3 The Ecological Perspective of Information Environment Theory

Hiroki Azuma and Hiroshi Hamano introduced ecological cognitive frameworks in “Information Environment Theory," understanding cyberspace as a dynamic ecosystem. However, traditional ecosystem research often focuses on “evolutionary" mechanisms—the competitive elimination process of survival of the fittest.

This study proposes a different analytical perspective: “evolution" is essentially a result-oriented explanation that describes adaptive processes that have already occurred but struggles to predict future developmental trajectories. In contrast, “parasitism" as a relational mode better reveals power dynamics and control mechanisms in information environments.

3. Conceptual Construction of Information Parasitism

3.1 Self-Organizing Order in Network Environments

Users of the World Wide Web often unconsciously participate in constructing some form of overall order through their daily random actions. This phenomenon embodies characteristics of autonomous, distributed, and coordinated non-centralized existence. Individual users’ microscopic behaviors—clicking, sharing, commenting—form macroscopic patterns and trends in information flow.

This self-organizing process can lead either toward constructive collective intelligence or destructive group polarization. The opposition between good and evil, optimism and pessimism, through mutual influence of human behaviors, may bring about social decline or elevation. Understanding the nature of this “force" is key to analyzing information parasitism phenomena.

3.2 Architecture as Control Mechanism

Lawrence Lessig, in “Code and Other Laws of Cyberspace," proposes the concept of “architecture," placing it alongside norms (customs), law, and market as four methods for controlling human behavior and social order. Lessig emphasizes that cyberspace is not naturally unregulable but shapes user behavior through software and hardware code design.

Within this theoretical framework, “parasitic" tendencies are equivalent to “normative" or “power" control laws. This control law is an “existential" rule that guides behavior not through external coercion but through internal structural arrangements.

3.3 Memes and the Genetic Transmission of Information

Richard Dawkins, in “The Selfish Gene," proposes the concept of “memes," likening the process of information transmission and fixation in culture and society to gene-like existence. This “selfish gene" is precisely what this study calls the “meta-information" of parasitic existence.

Meta-information possesses characteristics of self-replication, mutation, and selection. It seeks suitable “hosts" (content, platforms, users) in information environments, achieving its own transmission and continuation through parasitic relationships. In this hierarchical information structure, bottom-layer meta-information parasitizes upper-layer expanded information, forming a hierarchical system structure.

4. Parasitic Chains: Hierarchical Control in Information Environments

4.1 Structural Characteristics of Parasitic Chains

The parasitic chain is the core concept proposed in this study, describing hierarchical parasitic relationship structures in information environments. In this system:

1. Meta-information Layer: Contains basic cognitive patterns, value judgments, behavioral norms, etc.—the deepest “parasitic sources"
2. Content Information Layer: Specific texts, images, audio-visual content “infected" by meta-information and carrying its transmission
3. Platform Architecture Layer: Social media, search engines, recommendation systems, and other technological platforms providing “environments" for parasitism
4. User Behavior Layer: Individual clicking, sharing, commenting behaviors becoming “carriers" of parasitic chains

4.2 Operating Mechanisms of Parasitic Chains

The operation of parasitic chains embodies characteristics of “normative" or “power" control laws:

• Concealment: Parasitic relationships are often hidden within seemingly neutral technical architectures
• Automation: Through algorithms and system design, automatically guiding user behavior
• Universality: Penetrating all aspects of information environments, forming omnipresent influence
• Adaptability: Adjusting parasitic strategies according to environmental changes, maintaining influence

4.3 New Forms of Power Control

Traditional power control relies on legal coercion, economic incentives, or social pressure, while parasitic chains achieve more hidden but effective control through “architecture." They require no explicit commands or punishments but “naturally" guide behavior through environmental design.

The uniqueness of this control mode lies in its frequent appearance under the guise of “service," “convenience," and “personalization," making the controlled feel “free choice" rather than “external coercion."

5. New Challenges of Information Parasitism in the AI Era

5.1 The Unpredictability of Network Development

The rapid development of network technology, particularly AI breakthroughs, makes network ecosystem evolution increasingly unpredictable. Traditional linear development models can no longer adapt to exponential technological changes, making exploration of network ecosystems more important and urgent.

5.2 The Dilemma of Defining “Excellence"

In network environments, “evolution" means “excellent" entities gain survival opportunities. However, what constitutes “excellent" network content or services? This question involves deep value judgments:

• Technical Excellence: Does it mean higher click rates, longer usage time, stronger user stickiness?
• Content Excellence: Does it equal broader dissemination, stronger emotional responses, higher commercial value?
• Social Excellence: Does it manifest as positive contributions to social development, promotion of public interests?

These standards often conflict, making the definition of “excellence" a focal point of power struggles.

5.3 “Generative Force" in Pluralistic Ecosystems

The “generative force" in network pluralistic ecosystems is the driving force of the entire system, originating from:

• Technological Innovation: Emergence and application of new technologies
• Content Creation: Generation and dissemination of new content
• User Participation: Changes and interactions in user behavior
• Business Models: New value creation and distribution methods

Behind this driving force operates an invisible parasitic architecture. The “primal force" contained in meta-information is the fundamental source of “generative force."

6. Empirical Analysis: Contemporary Information Parasitism Phenomena

6.1 Algorithmic Parasitism in Social Media

Social media platform recommendation algorithms embody typical parasitic chain structures:

• Meta-information: Design logic of “engagement maximization"
• Content Filtering: Prioritizing content that provokes strong reactions
• User Behavior: Unconsciously guided into “echo chambers" or “polarization cycles"
• Social Effects: Intensification of information cocoons, cognitive biases, and social division

6.2 Knowledge Parasitism in Search Engines

Search engines seemingly provide “objective" information retrieval services, but their parasitic mechanisms include:

• Meta-information: Judgment standards of “relevance" and “authority"
• Ranking Logic: Influencing information visibility through SEO and other technical means
• User Cognition: Search result order influences user judgment of information importance
• Knowledge Construction: Information from specific viewpoints or interest groups receives priority display

6.3 Memetic Parasitism in AI-Generated Content

The proliferation of AI-generated content introduces new parasitic modes:

• Meta-information: Biases and patterns in AI training data
• Content Generation: AI replicates and amplifies existing cognitive frameworks
• Transmission Mechanisms: AI-generated content spreads widely across networks
• Cognitive Impact: Users struggle to distinguish AI-generated from human-created content

7. Conclusion and Prospects

This study, through integrating multidisciplinary perspectives from ecology, legal sociology, and information theory, proposes the conceptual framework of “information parasitism." Main findings include:

1. Relativity of Perception: AI and humans may perceive vastly different environmental worlds within the same information space
2. Concealment of Parasitism: Information parasitism achieves hidden but effective power control through architectural design
3. Hierarchy of Control: Parasitic chains form hierarchical control structures from meta-information to user behavior
4. Complexity of Evolution: Network ecosystem development is profoundly influenced by parasitic relationships

The theoretical contributions of this study include:

• Conceptual Innovation: Proposing concepts of “information parasitism" and “parasitic chains"
• Interdisciplinary Integration: Combining theoretical resources from ecology, law, and information science
• Analytical Framework: Providing new analytical tools for understanding power mechanisms in digital society
• Contemporary Response: Offering theoretical responses to new challenges in the AI era

References

Chinese Literature

• Hamano, H. (2008). Architecture no Seitaikei: Jōhō Kankyō wa Ika ni Sekkei sarete Kita ka [Architectural Ecosystems: How Information Environments Have Been Designed]. NTT Publishing.
• Lessig, L. (2002). Code and Other Laws of Cyberspace (Trans. J. Liu). Business Weekly Publications. (Original work published 2000)

English Literature

• Dawkins, R. (1976). The Selfish Gene. Oxford University Press.
• Lessig, L. (2006). Code Version 2.0. Basic Books.
• Uexküll, J. von (1909). Umwelt und Innenwelt der Tiere. Julius Springer.
• Yong, E. (2022). An Immense World: How Animal Senses Reveal the Hidden Realms Around Us. Random House.

Japanese Literature

• Azuma, H., & Hamano, H. (2007). “Jōhō Kankyō-ron" [Information Environment Theory]. Shisō Chizu, vol. 1.
• Hamano, H. (2008). Architecture no Seitaikei [Architectural Ecosystems]. NTT Publishing.
• Hamano, H. (2012). Ren’ai no Akitekucha [Architecture of Love]. NHK Publishing.

資訊寄生與架構的生態系：網路環境中的權力控制機制研究

摘要

本文以濱野智史的「架構的生態系」理論為基礎，探討網際網路資訊環境中的「資訊寄生」現象。通過結合生態學的感知理論、法律社會學的架構控制論，以及基因學的模因傳播理論，本研究提出「寄生鏈」概念，分析資訊環境中階層化的權力控制機制。文章論證了在AI與網路技術快速發展的當代，資訊寄生已成為理解數位社會秩序的重要概念框架。

關鍵詞： 資訊寄生、架構的生態系、濱野智史、資訊環境論、權力控制、模因傳播

1. 引言

在當代數位社會中，資訊的生產、傳播與消費已形成複雜的生態系統。濱野智史作為日本重要的資訊社會學者，專攻資訊社會論、媒體論、網路社群與資訊環境研究，其提出的「架構生態系」理論為理解網路環境提供了重要視角。然而，既有研究多聚焦於技術架構的「進化」面向，較少關注資訊環境中的「寄生」現象及其權力控制機制。

本文旨在擴展濱野智史的資訊環境論，提出「資訊寄生」概念，探討網路生態系中階層化的控制結構。通過跨學科的理論整合，本研究試圖回答：在AI技術快速發展的背景下，資訊寄生如何成為數位社會秩序的隱性控制機制？

2. 理論基礎：從生物感知到數位感知

2.1 生物感知的多元世界觀

雅各布·馮·烏克斯庫爾（Jakob von Uexküll）在《生物眼中的世界》中提出「環境世界」（Umwelt）概念，強調不同生物憑藉各自的感知器官，建構出有別於人類的有意義環境世界。這一觀點突破了人類中心主義的感知局限，揭示了感知的相對性與多元性。

愛德華·楊（Ed Yong）在《五感之外的世界》中進一步論述了「感知世界」的差異性，指出不同生物由於感知深度或頻寬的差異，形成截然不同的世界體驗。蝙蝠的超聲波導航、鳥類的磁場感知、蜜蜂的偏振光視覺，都構成了人類無法直接理解的感知維度。

2.2 AI感知的數位環境世界

延伸這一思考到人工智慧領域，當AI系統能夠透過API（Application Programming Interface）感知並處理資訊時，它們所建構的「數位環境世界」可能包含人類無法理解的感知維度。AI可以同時處理多重資料流、識別人類無法察覺的模式關聯、在高維空間中進行運算，這些能力形成了獨特的「AI感知世界」。

這種差異性感知能力的發展，為理解資訊環境的複雜性提供了新的視角。正如不同生物在同一物理空間中體驗到不同的環境世界，AI與人類在同一資訊空間中也可能感知到截然不同的資訊環境。

2.3 資訊環境論的生態系視角

東浩紀與濱野智史在「資訊環境論」中引入生態系的認知框架，將網路空間理解為一個動態的生態系統。然而，傳統生態系研究往往聚焦於「進化」機制，即適者生存的競爭淘汰過程。

本研究提出不同的分析視角：「進化」本質上是一種結果論的解釋，它描述了已經發生的適應過程，但難以預測未來的發展軌跡。相較之下，「寄生」作為一種關係模式，更能揭示資訊環境中的權力動態與控制機制。

3. 資訊寄生的概念建構

3.1 網路環境中的自組織秩序

全球資訊網的使用者在日常的隨意行動中，往往在不自覺的情況下參與了某種整體秩序的建構。這種現象體現了自律、分散且協調的非中央集權式存在特徵。個體用戶的點擊、分享、評論等微觀行為，在宏觀層面形成了資訊流動的模式與趨勢。

這種自組織過程既可能導向建設性的集體智慧，也可能走向破壞性的群體極化。善與惡、樂觀與悲觀的對立，在人類行為的相互影響中，可能招來社會沉淪或提升。理解這種「原力」的本質，正是分析資訊寄生現象的關鍵。

3.2 架構作為控制機制

勞倫斯·雷席格（Lawrence Lessig）在《網路自由與法律》（Code and Other Laws of Cyberspace）中提出「架構」概念，將其與規範（習慣）、法律、市場並列為控制人類行為與社會秩序的四種方法。雷席格強調，網路空間並非天然不可規制的，而是透過軟體和硬體代碼的設計來塑造用戶行為。

在此理論框架下，「寄生」趨向等同於「規範」或「權力」的控制法則。這種控制法是一種「存在」法則，它不是透過外在的強制，而是透過內在的結構安排來引導行為。

3.3 模因與資訊的基因化傳播

理查德·道金斯（Richard Dawkins）在《自私的基因》中提出「模因」（meme）概念，將資訊在文化與社會中的傳播及定型過程，比擬為基因般的存在。這種「自私的基因」正是本研究所稱的寄生存在的「元資訊」。

元資訊具有自我複製、變異、選擇的特性，它們在資訊環境中尋找適合的「宿主」（內容、平台、用戶），透過寄生關係實現自身的傳播與延續。這種資訊階層構造中，底層的元資訊寄生於上層擴展的資訊，形成階層化的體系結構。

4. 寄生鏈：資訊環境中的階層控制

4.1 寄生鏈的結構特徵

寄生鏈是本研究提出的核心概念，它描述了資訊環境中階層化的寄生關係結構。在這個體系中：

1. 元資訊層：包含基礎的認知模式、價值判斷、行為準則等，這些是最底層的「寄生源」
2. 內容資訊層：具體的文本、圖像、影音等內容，被元資訊所「感染」並承載其傳播
3. 平台架構層：社交媒體、搜索引擎、推薦系統等技術平台，提供寄生的「環境」
4. 用戶行為層：個體的點擊、分享、評論等行為，成為寄生鏈的「載體」

4.2 寄生鏈的運作機制

寄生鏈的運作體現了「規範」或「權力」控制法的特徵：

• 隱密性：寄生關係往往隱藏在看似中性的技術架構中
• 自動化：透過演算法和系統設計，自動引導用戶行為
• 普遍性：滲透到資訊環境的各個層面，形成無所不在的影響力
• 適應性：能夠根據環境變化調整寄生策略，保持影響力

4.3 權力控制的新形態

傳統的權力控制依賴於法律強制、經濟誘因或社會壓力，而寄生鏈則透過「架構」實現更加隱性但有效的控制。它不需要明確的命令或懲罰，而是透過環境設計來「自然地」引導行為。

這種控制模式的特殊性在於：它往往以「服務」、「便利」、「個性化」的名義出現，讓被控制者感受到的是「自由選擇」而非「外在強制」。

5. AI時代的資訊寄生新挑戰

5.1 網路發展的不可預測性

網路技術的快速發展，特別是AI技術的突破，使得網路生態系統的演化變得更加難以預測。傳統的線性發展模式已經無法適應指數級的技術變化，這使得對網路生態系統的探索變得更加重要且緊迫。

5.2 「優秀」的定義困境

在網路環境中，「進化」意味著「優秀」者獲得生存機會。但是，什麼是「優秀」的網路內容或服務？這個問題涉及深層的價值判斷：

• 技術優秀：是否意味著更高的點擊率、更長的使用時間、更強的用戶黏性？
• 內容優秀：是否等同於更廣泛的傳播、更強烈的情感反應、更高的商業價值？
• 社會優秀：是否體現為對社會發展的積極貢獻、對公共利益的促進？

這些標準往往相互衝突，「優秀」的定義成為權力鬥爭的焦點。

5.3 多元生態系統中的「發生力」

網路多元生態系統中的「發生力」是整個系統的推動力，它源於：

• 技術創新：新技術的出現與應用
• 內容創造：新內容的產生與傳播
• 用戶參與：用戶行為的變化與互動
• 商業模式：新的價值創造與分配方式

在這推動力的背後，存在著一個寄生的架構無形地運作著。元資訊所蘊含的「原力」正是「發生力」的根本泉源。

6. 實證分析：當代資訊寄生現象

6.1 社交媒體的演算法寄生

社交媒體平台的推薦演算法體現了典型的寄生鏈結構：

• 元資訊：「參與度最大化」的設計邏輯
• 內容篩選：優先推送能引發強烈反應的內容
• 用戶行為：不自覺地被引導進入「同溫層」或「極化循環」
• 社會效果：資訊繭房、認知偏差、社會分化的加劇

6.2 搜索引擎的知識寄生

搜索引擎看似提供「客觀」的資訊檢索服務，但其寄生機制包括：

• 元資訊：「相關性」與「權威性」的判斷標準
• 排序邏輯：透過SEO等技術手段影響資訊可見性
• 用戶認知：搜索結果的順序影響用戶對資訊重要性的判斷
• 知識建構：特定觀點或利益集團的資訊獲得優先展示

6.3 AI生成內容的模因寄生

AI生成內容的普及帶來新的寄生模式：

• 元資訊：AI訓練資料中的偏見與模式
• 內容生成：AI複製並放大既有的認知框架
• 傳播機制：AI生成的內容在網路上大量傳播
• 認知影響：用戶難以區分AI生成與人類創作的內容

7. 結論與展望

本研究通過整合生態學、法律社會學、資訊理論等多學科視角，提出了「資訊寄生」的概念框架，主要發現包括：

1. 感知的相對性：AI與人類在同一資訊空間中可能感知到截然不同的環境世界
2. 寄生的隱密性：資訊寄生透過架構設計實現隱性但有效的權力控制
3. 控制的階層性：寄生鏈形成了從元資訊到用戶行為的階層化控制結構
4. 演化的複雜性：網路生態系統的發展受到寄生關係的深刻影響

本研究的理論提出在於：

• 概念創新：提出「資訊寄生」與「寄生鏈」概念
• 跨學科整合：結合生態學、法律學、資訊學的理論資源
• 分析框架：為理解數位社會的權力機制提供新的分析工具
• 時代回應：針對AI時代的新挑戰提出理論回應