Societal Neural Networks: Governance-Aware Adaptive Coordination among Human, AI, and Institutional Actors

The metaphor of society as a neural network is not new. Prior research has modeled human society using neural-network and Boltzmann-machine analogies, while newer work on society-in-the-loop governance, institutional AI, human-AI coordination, and AI-orchestrated organizations has moved toward socio-technical systems in which artificial agents participate in collective action. This paper therefore does not claim priority for the metaphor. It defines a narrower and testable architecture: the Societal Neural Network (SocNN), a heterogeneous coordination network in which human actors, AI agents, and institutional actors can receive, delegate, execute, constrain, and learn from work under explicit authority and governance conditions. The minimum operational mechanism is termed a Governed Adaptive Coordination Network (GACN). A GACN couples capability representation, authority-aware routing, execution provenance, outcome evidence, adaptive routing, and governance constraints. Its key design rule is non-self-escalation: performance feedback may change capability confidence and routing preferences, but it cannot by itself expand an actor's authority. The paper formalizes SocNN as a stateful coordination system, specifies necessary conditions that distinguish it from social-network metaphors, conventional multi-agent systems, workflow orchestration, and governance frameworks, and derives six falsifiable propositions. It then proposes a replay-and-field-test design comparing manual, rule-based, capability-only, and governance-aware adaptive routing. The intended contribution is a research object that connects organization theory, multi-agent systems, AI governance, and computational social systems without collapsing governance into optimization or treating institutions as passive context.