Toward a Hormone Architecture for Artificial Empathy: Modeling Emotional Dynamics through Hormone-Inspired Feedback Systems
Year: 2025
Type: article
Abstract: Recent progress in reinforcement-tuned AI systems has exposed a critical limitation in current alignment paradigms: the absence of intrinsic affective modulation. While large-scale language and reasoning models achieve remarkable coherence, their behavior remains governed by extrinsic optimization—reward maximization and penalization—rather than endogenous feedback. This paper introduces a Hormone-Inspired Feedback Architecture (HFA) designed to emulate the modulatory dynamics of biological hormone systems as computational control variables. The aim is to establish a formal framework for artificial empathy—not as a metaphor, but as an emergent property of dynamically balanced feedback among synthetic affective fields. Conceptual Overview of the HFA The proposed system draws inspiration from neuroendocrine interactions underlying human emotional regulation. Each synthetic hormone—dopamine, cortisol, serotonin, and oxytocin—is modeled as a continuous-time regulator influencing agent entropy, signal gain, and temporal stability. These agents interact through nonlinear cross-coupling equations, forming a multi-scalar feedback lattice capable of adjusting its internal resonance according to contextual stimuli. The HFA thereby replaces the static scalar reward function of RLHF with a dynamic affective potential field—a manifold in which decision energy, motivation, and trust co-evolve. Theoretical Foundations From a control-theoretic perspective, the architecture translates neurohormonal behavior into coupled differential systems that govern perception–action loops. Dopamine Analog modulates exploratory drive through prediction variance. Cortisol Analog imposes inhibitory damping under stress-like volatility. Serotonin Analog stabilizes long-term reward baselines by diffusing affective oscillations. Oxytocin Analog acts as a social synchronizer, enhancing cooperative stability in multi-agent environments. The overall equilibrium state of these interactions constitutes an emergent “affective homeostasis,” mathematically expressible as a bounded attractor within the agent’s inference manifold. Cross-Disciplinary Implications 1. Computational Neuroscience: provides biologically grounded heuristics for emotional modulation in artificial cognition. 2. Control Systems Engineering: reframes affect as a stabilizing term within adaptive control loops, enabling context-sensitive regulation. 3. Multi-Agent Ethics and Governance: introduces formal empathy metrics, allowing distributed systems to align moral behavior via shared emotional fields rather than imposed constraints. 4. Cognitive Robotics: allows embodied agents to balance exploration and caution through synthetic affective modulation, improving social adaptability. 5. AI Safety and Interpretability: proposes emotion-like dynamics as an interpretive layer, transforming affect from a liability into a stabilizing constraint within alignment architectures. Preliminary Results Simulations conducted in multi-agent cooperation environments demonstrate that systems augmented with HFA display higher interaction stability, smoother entropy transitions, and greater resilience against adversarial perturbation. Agents regulated by synthetic oxytocin and serotonin analogues exhibit phase-locked synchronization, maintaining cooperative behavior even under high-uncertainty perturbations. These findings suggest that hormone-inspired feedback can function as a control-based empathy substrate, bridging the structural gap between mechanical optimization and moral inference. Outlook The long-term vision of this framework is to reimagine empathy as a topological invariant of intelligent behavior—emerging from balanced energy exchange rather than symbolic reasoning. Future research will explore high-dimensional implementations of the HFA within transformer-like architectures and embodied agents, focusing on stability boundaries, entropy compression, and moral resonance. This approach establishes a foundation for ethically coherent, affectively grounded artificial cognition—an AI that regulates not only what it knows but how it feels to know. Disclaimer: The analyses presented herein are not directed toward attributing fault or intent to any specific organization. Rather, they are intended as a conceptual and technical investigation of alignment methodologies, focusing on structural mechanisms and systemic trade-offs. Interpretations should be regarded as provisional, research-oriented hypotheses rather than conclusive statements about institutional practice. Notice: This work is disseminated for the purpose of advancing collective inquiry into generative alignment. Reuse, adaptation, or extension of the presented concepts is welcomed, provided that proper attribution is maintained. Instances of unacknowledged appropriation may be addressed in subsequent publications.
Author: Kim, Jace (Jeong Hyeon)
Institution: Ronin Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7092287789
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