DOI: 10.5281/zenodo.17041692
title: Dynamic Global Alignment Model (DGAM–V2): A Causal, Multi-Agent Decision-Intelligence Architecture for Sovereign Strategic Statecraft in a Multipolar World Final Ultimate Edition (2025)

description:  



🧾 Canonical Zenodo Record Description

Final Ultimate Edition (2025)

Dynamic Global Alignment Model (DGAM–V2):

A Causal, Multi-Agent Decision-Intelligence Architecture for Sovereign Strategic Statecraft in a Multipolar World



This Zenodo record archives the authoritative Final Ultimate Edition (2025) of the Dynamic Global Alignment Model (DGAM–V2) as a canonical, cryptographically verifiable scholarly artifact.The work is published under a persistent DOI and is intended for long-term archival, citation, institutional reference, and rigorous peer review.

DGAM–V2 is released as a single unified research object, composed of two tightly coupled volumes that are cryptographically bound to ensure immutability, auditability, and citation safety.



📘 Repository Contents

Root-1: Main Research Volume

File: DGAM-V2_Final-Ultimate-Edition_Mazumdar-2025.pdf

The Main Research Volume presents the core conceptual, mathematical, and strategic architecture of DGAM–V2, including:



Formal model structure and theoretical foundations

Causal, risk-aware decision-intelligence design

Strategic reasoning under multipolar geopolitical uncertainty

Explicit governance constraints, ethical boundaries, and sovereignty safeguards

Structured cross-references to all formal proofs, algorithms, and validation material contained in the Annex Compendium


This volume is written for academic researchers, doctoral scholars, senior policy analysts, think-tank directors, ministers, and journal reviewers.



Root-2: Annex Compendium (Technical & Audit Authority)

File: DGAM-V2_Annex-Compendium_Final-Ultimate-Edition_Mazumdar_2025.pdf

The Annex Compendium serves as the definitive technical, mathematical, and governance reference, containing:



Complete mathematical proofs and formal derivations

Full Python reference implementations

Reproducibility protocols and documented boundary conditions

Audit-grade governance logic, security isolation, and deployment constraints

Validation experiments, stress testing, and explicit limitation analysis


This volume is intended for reviewers, auditors, engineers, security analysts, and advanced researchers.



🧠 Framework Overview

DGAM–V2 is a research-grade decision-intelligence and governance architecture for analyzing strategic state alignment and policy choice under geopolitical uncertainty in a multipolar world.

The framework integrates:



Multi-Agent Reinforcement Learning (MARL) for strategic multi-actor interaction

Risk-sensitive optimization, including GeoVaR and CVaR

Structural Causal Models (SCM) with intervention and counterfactual reasoning

Tail-risk and systemic-shock analysis under non-stationary and regime-switching dynamics

Governance-by-design, including human-in-the-loop control, auditability, security isolation, and version integrity


DGAM–V2 reframes alignment not as a static bloc-membership or correlational forecasting problem, but as a dynamic, causal, and risk-bounded decision process.



🔐 Cryptographic Integrity & Immutability

The two volumes are cryptographically bound using a Merkle construction to ensure immutability, citation safety, and long-term archival integrity.

Merkle Root (SHA-256):9b6764b538bb8872a6cc18debc3ab92e5b96fde0cee75e0f8426ef455b489fcd

This Merkle root irreversibly binds the Main Research Volume and the Annex Compendium into one immutable scholarly object.Any modification invalidates the root.



⚖️ Scope, Boundaries & Ethical Posture

DGAM–V2 is not:



a predictive oracle

an autonomous decision-making system

an operational command, escalation, or targeting tool


It is designed strictly as a decision-support and analytical architecture, preserving:



sovereign human authority

legal accountability

democratic and institutional oversight


All claims are explicitly bounded, auditable, and reproducible within documented assumptions, data limits, and governance constraints.



🎯 Intended Use



Doctoral and post-doctoral research

Flagship think-tank and strategic-studies programs

Government and national-security decision support (non-operational)

AI governance, auditability, and sovereign deployment research

Long-term archival reference and peer review




📜 Author

Dr. B. MazumdarIndependent Researcher–Scholar

AI Governance • Cybersecurity • Post-Quantum Cryptography • Digital Statecraft

ORCID: https://orcid.org/0009-0007-5615-3558