用智能体模拟情绪误判如何破坏社交信任与秩序

The ability of humans to detect and respond to others' emotions is fundamental to understanding social behavior. Here, agents are instantiated with emotion classifiers of varying accuracy to study the impact of perceptual accuracy on emergent emotional and spatial behavior. Agents are visually represented with face photos from the KDEF database and endowed with one of three classifiers trained on the JAFFE (poor), CK+ (medium), or KDEF (high) datasets. Agents communicate locally on a 2D toroidal lattice, perceiving neighbors' emotional state based on their classifier and responding with movement toward perceived positive emotions and away from perceived negative emotions. Note that the agents respond to perceived, instead of ground-truth, emotions, introducing systematic misperception and frustration. A battery of experiments is carried out on homogeneous and heterogeneous populations and scenarios with repeated emotional shocks. Results show that low-accuracy classifiers on the part of the agent reliably result in diminished trust, emotional disintegration into sadness, and disordered social organization. By contrast, the agent that develops high accuracy develops hardy emotional clusters and resilience to emotional disruptions. Even in emotionally neutral scenarios, misperception is enough to generate segregation and disintegration of cohesion. These findings underscore the fact that biases or imprecision in emotion recognition may significantly warp social processes and disrupt emotional integration.