Redefining Empathy in AI as Predictive Misalignment Tolerance

Traditional theories of empathy, especially in artificial systems, often focus on "resonance" – mirroring another's immediate emotional or cognitive state. This synchronic view defines empathic AI by its ability to recognize and align with affect. However, this paper argues that for extended dialogues, where understanding evolves through prediction, divergence, and repair, this definition is insufficient. The researchers propose a new framework: empathy as "predictive misalignment tolerance." This concept emphasizes an agent's capacity to anticipate and actively regulate divergence over time, rather than simply collapsing it. They formalize this as Interpretive Error Tolerance (IET), a dynamic-threshold heuristic that models empathy as maintaining a viable band of divergence between interacting agents. Computational experiments using controlled noise revealed that while the IET update rule didn't outperform fixed baselines, a robust regime-dependent structure emerged. Dialogue repair was found to trade discriminative fidelity for the preservation of gist meaning. At low noise levels, repair degraded retrieval accuracy, but at high noise, it effectively preserved the core message. This suggests that empathy in prolonged interactions isn't about eliminating differences but rather about dynamically managing interpretive distance.