Silicopathy: Artificial Empathy through Cognitive and Affective Development of Pain

This position paper introduces the concept of *silicopathy*—a developmental and embodied framework for constructing artificial moral agency grounded in pain-based empathic learning. Extending prior work on artificial empathy, silicopathy integrates sensory discrimination, self–other mapping, and memory-based prediction using two computational models: Deep Modality Blending Networks (DMBN) and Oscillator-Driven Reservoir Computing (ODRC). These enable short-term pain learning and internal simulation. We propose future integration with large language models (LLMs) to construct socially interpretable long-term pain memory. While not implying biological sentience, we argue that such systems may exhibit morally intelligible behavior through synthetic pain modeling. Ethical implications and interdisciplinary challenges are discussed. Silicopathy thus outlines a concrete path toward affectively grounded, morally responsive machines.