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The Integrated Complexity Index of Consciousness (ICI): Theory, Models, and Applications
Paperback
General ComputersGeneral ScienceHistory & Philosophy of Science
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ISBN13: 9798180493309
Publisher: Independently Published
Published: Jun 7 2026
Pages: 362
Weight: 0.96
Height: 0.90 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Published: Jun 7 2026
Pages: 362
Weight: 0.96
Height: 0.90 Width: 6.00 Depth: 9.00
Language: English
This book develops the Integrated Complexity Index (ICI) as a quantitative framework for measuring organized complexity across biological, historical, economic, ecological, and artificial systems. Beginning with the cell as the minimal autonomous information-processing system, it argues that complexity is not determined by scale alone, but by the relation between two structural dimensions: DCS, which captures component diversity, total quantity, and concurrent processing throughput; and FWM, which captures closed feedback loops, effective interaction frequency, and memory depth.
At the center of the book is the claim that the lipid membrane marks a fundamental boundary in the history of organized existence. Before such a boundary appears, systems may possess vast scale, energy, and structure, but their feedback-integration capacity remains near zero. After the membrane establishes an inside/outside distinction, autonomous information processing becomes physically possible, allowing FWM to emerge and accumulate. On this basis, the book proposes that life, cognition, social institutions, markets, ecosystems, and artificial intelligence can be compared through a shared cybernetic and thermodynamic language.
The ICI formula is derived from information geometry, Landauer's principle, and stability theory, rather than introduced as a fitted index. The book further introduces the dynamic response function (R(t)), which measures the balance between feedback-integration capacity and scale-complexity pressure. This function provides a unified way to analyze collapse and early warning across domains: imperial decline, financial crises, ecological tipping points, and the structural limits of current AI systems are interpreted as different expressions of the same underlying imbalance between FWM and DCS.
Across its application chapters, the book uses ICI to reinterpret the evolution of life as a sequence of threshold transitions rather than linear accumulation; to classify imperial collapse into scale overload, memory dissipation, and topological rupture; to explain modern financial fragility through the divergence between transaction speed and effective regulatory feedback; to analyze ecosystem collapse through the breakdown of food-web loops and ecological memory; and to evaluate artificial intelligence as a high-DCS but low-FWM architecture. It argues that current AI systems achieve impressive task performance largely through scale, while lacking the endogenous feedback, oscillatory dynamics, and persistent memory required for high integration depth.
The final chapters examine the upper limit of human ICI, possible paths beyond it through brain-computer interfaces and synthetic biology, and the falsification conditions under which the entire framework would need to be revised or abandoned. The book therefore presents ICI not as a closed doctrine, but as an open measurement system: one that seeks to make complexity comparable, computable, and falsifiable across domains.
Its broader philosophical implication is that consciousness and organized existence need not be treated either as cosmic accidents or as teleological endpoints. They may instead be understood as lawful outcomes of open systems operating under thermodynamic constraints. In this sense, the book offers a quantitative language for asking where, and by what mechanisms, the universe begins to accumulate the capacity to understand itself.
The ICI formula is derived from information geometry, Landauer's principle, and stability theory, rather than introduced as a fitted index. The book further introduces the dynamic response function (R(t)), which measures the balance between feedback-integration capacity and scale-complexity pressure. This function provides a unified way to analyze collapse and early warning across domains: imperial decline, financial crises, ecological tipping points, and the structural limits of current AI systems are interpreted as different expressions of the same underlying imbalance between FWM and DCS.
Across its application chapters, the book uses ICI to reinterpret the evolution of life as a sequence of threshold transitions rather than linear accumulation; to classify imperial collapse into scale overload, memory dissipation, and topological rupture; to explain modern financial fragility through the divergence between transaction speed and effective regulatory feedback; to analyze ecosystem collapse through the breakdown of food-web loops and ecological memory; and to evaluate artificial intelligence as a high-DCS but low-FWM architecture. It argues that current AI systems achieve impressive task performance largely through scale, while lacking the endogenous feedback, oscillatory dynamics, and persistent memory required for high integration depth.
The final chapters examine the upper limit of human ICI, possible paths beyond it through brain-computer interfaces and synthetic biology, and the falsification conditions under which the entire framework would need to be revised or abandoned. The book therefore presents ICI not as a closed doctrine, but as an open measurement system: one that seeks to make complexity comparable, computable, and falsifiable across domains.
Its broader philosophical implication is that consciousness and organized existence need not be treated either as cosmic accidents or as teleological endpoints. They may instead be understood as lawful outcomes of open systems operating under thermodynamic constraints. In this sense, the book offers a quantitative language for asking where, and by what mechanisms, the universe begins to accumulate the capacity to understand itself.
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