God and the auditor
A book about philosophical machines, BOIS, and reproducible action under incomplete knowledge
Current edition: 0.00
Access: free public edition for reading and download
Canonical text: Russian
English translation: available
License: Creative Commons Attribution 4.0 International
Who this book is for
● AI builders
● Researchers
● People working with complex decisions
The idea
"God and the auditor" is a book about philosophical machines, the philomic approach, and reproducible action under incomplete knowledge.
It introduces a practical way to read, design, and operate complex systems. It starts from a common working condition: people, organizations, and AI-supported systems often need to act while knowledge is still incomplete.
The book shows how to work in that condition by making the unknown visible, structured, and testable.
It follows a clear operating path: choose what you are looking at, define its boundary and scale, identify what is missing, turn the important unknown into a question, test it through a protocol, execute a candidate solution, record what actually happened, filter experience through feedback, and turn verified results into instructions that can work again.
This is the BOIS cycle.
The introduction explains this path through two recurring examples: a workshop that organizes real work, and a bumblebee life cycle reconstructed from biological evidence. Together, they show two uses of the same discipline: building an artificial organization and understanding an existing process.
A central idea of the book is status discipline.
Facts, messages, hypotheses, conclusions, decisions, and open questions each have their own role. Keeping these statuses distinct helps a system preserve evidence, judgment, responsibility, and the exact place where further knowledge is needed.
This is why the book calls such systems philosophical machines.
A philosophical machine is not just a tool that performs tasks. It is a working architecture that makes fundamental distinctions operational: what exists in the calculation, what counts as knowledge, who acts, who accepts results, what risks guide action, what counts as closure, and how experience changes future work.
The book leads from attention to action, and from action back to attention.
It asks how meaning can become a reproducible way of acting, learning, repairing errors, and continuing work.
It introduces a practical way to read, design, and operate complex systems. It starts from a common working condition: people, organizations, and AI-supported systems often need to act while knowledge is still incomplete.
The book shows how to work in that condition by making the unknown visible, structured, and testable.
It follows a clear operating path: choose what you are looking at, define its boundary and scale, identify what is missing, turn the important unknown into a question, test it through a protocol, execute a candidate solution, record what actually happened, filter experience through feedback, and turn verified results into instructions that can work again.
This is the BOIS cycle.
The introduction explains this path through two recurring examples: a workshop that organizes real work, and a bumblebee life cycle reconstructed from biological evidence. Together, they show two uses of the same discipline: building an artificial organization and understanding an existing process.
A central idea of the book is status discipline.
Facts, messages, hypotheses, conclusions, decisions, and open questions each have their own role. Keeping these statuses distinct helps a system preserve evidence, judgment, responsibility, and the exact place where further knowledge is needed.
This is why the book calls such systems philosophical machines.
A philosophical machine is not just a tool that performs tasks. It is a working architecture that makes fundamental distinctions operational: what exists in the calculation, what counts as knowledge, who acts, who accepts results, what risks guide action, what counts as closure, and how experience changes future work.
The book leads from attention to action, and from action back to attention.
It asks how meaning can become a reproducible way of acting, learning, repairing errors, and continuing work.
Free public edition.
Read the Russian canonical edition or the English translation.
What is inside
● An introduction to the filomic approach
● The BOIS cycle
● The Pravda project
● M1/M3 as reasoning machines for AI-and-memory substrates
● Tables, procedures, and project structures for practical use
Continue with BOIS
BOIS is a cycle for disciplined reasoning and action under incomplete knowledge.
Attention → Knowledge Gap Area → Valuable Gap → Valuable Question → Protocol → Next Step → Execution → Experience → Feedback and Selection → Verified Result → Instruction → Physiology → New Attention.
The cycle keeps facts, messages, hypotheses, conclusions, decisions, and open questions in their own places. It turns uncertainty into a working form: questions, tests, results, and next actions.
Attention → Knowledge Gap Area → Valuable Gap → Valuable Question → Protocol → Next Step → Execution → Experience → Feedback and Selection → Verified Result → Instruction → Physiology → New Attention.
The cycle keeps facts, messages, hypotheses, conclusions, decisions, and open questions in their own places. It turns uncertainty into a working form: questions, tests, results, and next actions.
