This study employs LLM-based multi-agent simulation to investigate how fairness-related behavior evolves in repeated resource allocation. Thirty agents with five idealized value orientations participate in a survival-production cycle under equal, needs-based, contribution-based, and negotiation-based allocation mechanisms. Results show that negotiation achieves the highest productive return and subjective satisfaction with the strongest consensus in our main setting, and its subjective advantage holds across eight LLMs, suggesting that deliberative allocation can help navigate the fairness–efficiency tradeoff. Agents also exhibit systematic misalignment between subjective satisfaction and objective resource outcomes, shaped by value orientations and interaction conditions. Over repeated interaction, agents’ reflection-derived fairness positions shift away from their initial orientations toward more hybrid combinations of equality-, merit-, and needs-related reasoning. To provide human-grounded structural comparison, we further benchmark GPT-4o against published human choice distributions in 16 controlled allocation scenarios, finding moderate rank-level correspondence but substantially more concentrated LLM choices. These findings characterize value-conditioned reasoning and semantic change in LLM societies while remaining computational hypotheses rather than direct evidence of human fairness behavior.