The Gatekeeper: A Cybernetic architecture for Socially Calibrated Behaviour

Abstract

Social dependency changes the control problem faced by an animal. In a complex social environment, the consequences of action depend not only on material conditions and immediate motivation but also on the organism's changing position within a relational organisation generated by many other adaptive agents. The resulting state space is too large and dynamic to be reconstructed exhaustively for each behavioural decision. This paper proposes the Gatekeeper as a functional cybernetic architecture for making that problem tractable. The model combines state abstraction, continuously updated relational state representation, action selection within a socially acquired behavioural repertoire, socially legible behavioural configurations, and closed-loop recalibration. Its component operations are not claimed as new discoveries: cybernetics, reinforcement learning, action-selection theory, comparative cognition, computational ethology, social-learning research, signalling theory and relational-model approaches each establish neighbouring mechanisms or formal tools. The proposed contribution is their integration around a specific evolutionary control problem: rapid participation in a social organisation whose relevant state is external, relational and continuously changing. The model predicts reusable Self- and Other-related variables, cross-context propagation when relational state changes, recurrent coordinated behavioural states, rapid receiver responses to socially legible configurations, and persistence when behaviour restricts corrective feedback. Gatekeeper is therefore offered as a testable functional architecture rather than a discrete neural module or a claim of computational inevitability.