LLM agents are widely used in social simulation, yet fluent social reasoning does not guarantee valid decision mechanisms. We test whether models implement socially embedded utility, where actions depend on peer-weighted references rather than isolated private preference. We introduce a theory-guided benchmark from network utility theory covering utility recovery, social-comparison gating, and structural transfer across two out-of-distribution decision forms. Across open-source and proprietary models, we find a gap between cue recognition and peer aggregation, spurious social activation when comparison should be inactive, and declining accuracy as social dependence rises. We propose theory-supervised mechanism alignment trained on latent utility states. Fine-tuning yields +8.3 and +13.9 utility-recovery points on Llama-3.1-8B and Qwen3-8B, raises gating above 95%, and improves transfer to unseen structures. Multi-agent simulations show that repairing local utility shifts macro outcomes, including stronger financial amplification and more stable labor stratification, which indicates that realistic macro-level simulations require adjustments at the individual level. Data and code are available at https://github.com/saaranLiu/SEDM-alignment.