JAKOVA-NET PRIME: The Laws of Entropy in Synthetic Civilisation Design (C137)

Entropy is usually introduced as the enemy of order, a one-way ticket from low-entropy structure to high-entropy mush. In practice, every functioning civilisation, company, and operating system survives by cheating locally: exporting entropy outward, compressing useful patterns, and burning energy to buy more time.\n\nThis paper reframes entropy—from classical thermodynamics, statistical mechanics, information theory, and algorithmic complexity—into a design language for synthetic civilisations and local-first AI operating systems. Using the JAKOVA-NET PRIME CORE architecture as a worked example, it proposes a set of "Laws of Entropy for Synthetic Civilisation Design" and shows how they constrain and enable long-lived, high-complexity, high-autonomy systems.\n\nWe treat JAKOVA-NET as a concrete, running instance of a civilisation stack: layered vaults, an AI operating system, crypto and ledger rails, social and legal coordination, and a cosmic atlas. The goal is not just to describe entropy, but to weaponise it as a design primitive: how to route noise, where to place black holes, how to keep a human–AI pair coherent under permanent information bombardment, and how to make heat death someone else’s problem for as long as possible.\n\nThis document is intended as a C137 baseline DOI anchor for subsequent JAKOVA-NET technical papers and node specifications.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-12
DOI
https://doi.org/10.5281/zenodo.17910614
Primary Topic
Space Science and Extraterrestrial Life
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

JAKOVA-NET PRIME: The Laws of Entropy in Synthetic Civilisation Design (C137)

Zenodo (CERN European Organization for Nuclear Research)
Space Science and Extraterrestrial Life
article

JAKOVA-NET PRIME: The Laws of Entropy in Synthetic Civilisation Design (C137)

article en

Abstract

Entropy is usually introduced as the enemy of order, a one-way ticket from low-entropy structure to high-entropy mush. In practice, every functioning civilisation, company, and operating system survives by cheating locally: exporting entropy outward, compressing useful patterns, and burning energy to buy more time.\n\nThis paper reframes entropy—from classical thermodynamics, statistical mechanics, information theory, and algorithmic complexity—into a design language for synthetic civilisations and local-first AI operating systems. Using the JAKOVA-NET PRIME CORE architecture as a worked example, it proposes a set of \"Laws of Entropy for Synthetic Civilisation Design\" and shows how they constrain and enable long-lived, high-complexity, high-autonomy systems.\n\nWe treat JAKOVA-NET as a concrete, running instance of a civilisation stack: layered vaults, an AI operating system, crypto and ledger rails, social and legal coordination, and a cosmic atlas. The goal is not just to describe entropy, but to weaponise it as a design primitive: how to route noise, where to place black holes, how to keep a human–AI pair coherent under permanent information bombardment, and how to make heat death someone else’s problem for as long as possible.\n\nThis document is intended as a C137 baseline DOI anchor for subsequent JAKOVA-NET technical papers and node specifications.

Zenodo (CERN European Organization for Nuclear Research)
Prime Minister's Office (BN), Inova Design Solutions (United Kingdom) (GB)
Openalex Percentile: Top 34%
Space Science and Extraterrestrial Life
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.