Sugarscape-Based Swarm Governance Model for Sensing Ecosystems: Environment-First Anomaly Detection and Quantum-Assisted Multi-Modal Confirmation

Emerging drone and AI-enabled threats are increasingly embedded within complex environmental systems. Traditional detection paradigms rely on object-centric identification approaches, which require prior recognition of the target before anomaly detection becomes possible. This creates structural delays in environments where signals are weak, intermittent, or intentionally obscured.

This paper proposes a Sugarscape-inspired swarm governance model for sensing ecosystems, where anomaly detection begins not with the identification of objects but with environmental deviations. The model builds upon the tradition of complex adaptive systems modeling and integrates principles from ecological sensing, swarm intelligence, and distributed governance.

A key innovation of the proposed architecture is the Environment-First Detection Chain, in which quantum photon confirmation serves as the initial trigger enabling rapid system readiness, while subsequent multi-modal confirmations (acoustic, RF, radar, optical) refine environmental context and scenario generation.

The architecture also introduces a human-authority governance layer, where artificial intelligence is limited to scenario generation while final decision authority remains with human operators. This governance approach addresses a critical concern in contemporary defense and security ecosystems regarding the role of AI in lethal or strategic decision-making.

The concept builds upon previous work on Same-Modality Identification of Drone Threats (SMIDT) and was further developed following the IAP Build for Ukraine MIT hackathon, contributing to the to the broader ecosystem of the NATO Innovation Fund and the North Atlantic Alliance in general.

Conceptual Origin:
The analytical frameworks exploring sensing ecosystems, swarm governance and dual-use security architectures described in this document, including the DRONEDOME developed and advanced Same-Modality Identification of Drone Threats (SMIDT) Diagrams, constitute pre-existing research developed within DRONEDOME Center prior to any collaboration or institutional engagement described herein. Their inclusion in academic, research, or collaborative contexts does not imply transfer of intellectual property or institutional ownership. Attribution of individual scholarly contribution may be made in academic settings, while institutional intellectual property remains with institutional intellectual property held by DRONEDOME as the originating research entity and individual scholarly contributions attributed to Oleh Deineka, Volodymyr Khomenko, and Sergiy Skidanov.