Defining Decentralization: An Ontological Perspective

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jakub Kacper Szeląg, Aydin Abadi, Mohammad Naseri
• Venue: arXiv: cs.DC
• Published: 2026-08-10
• Collected: 2026-08-11
• URL: http://arxiv.org/abs/2608.09748v1
• PDF: https://arxiv.org/pdf/2608.09748v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

This research paper addresses the lack of a universal definition for decentralization in computer science and its related fields like security, AI, cloud infrastructure, IoT, etc. The authors propose a graph-based ontology to define decentralization as a relational and subject-specific property of computer communication systems. They introduce two new metrics, Void Tolerance and Imperviousness, for evaluating the degree of decentralization in various systems. The paper also includes a browser-based implementation that classifies and computes the metrics of any system.

What the Document Says

• The paper identifies a lack of universal definition for decentralization in computer science.
• It proposes a graph-based ontology to define decentralization as a relational and subject-specific property.
• Two new metrics, Void Tolerance and Imperviousness, are introduced for evaluating the degree of decentralization.
• A browser-based implementation is provided for automated classification and metric computation.

Key Concepts Mentioned

• decentralization
• computer science
• graph-based ontology
• Void Tolerance
• Imperviousness

Claims Made by the Document

• There is no universally accepted definition of decentralization applicable across computer communication systems.
• The proposed ontology can formally distinguish decentralization from distribution.

Evidence Used

• Decentralization as a concept in computer science has existed for over half a century but there remains no universally accepted definition...
• We define this research gap as the Decentralization Problem...

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.