AI 中文总结
本研究将AI过度依赖建模为复杂适应系统，通过人群过程建模揭示社会学习、社会证明等机制会引发集体过度依赖，提出可通过调整反馈设计防止崩溃，为AI依赖研究提供新视角。
AI 中文摘要
AI辅助对人群的帮助或危害，更多取决于人们是否恰当依赖它（正确时信任、错误时核查），而非模型准确率。但现有研究通常单次分析单个用户，本文将其建模为人群过程：智能体反复单独完成任务、接受AI答案或核查答案，更新关于AI质量的贝叶斯信念；当智能体联网时，还会向同伴学习。四项结果构成完整逻辑：环境设定基准，任务难度与AI质量共同决定过度依赖和校准遗憾；社会学习形成共识而非过度依赖，经2×2拓扑标记设计验证的均值保持定理表明，仅当信念可传递影响时，网络连接才会改变群体状态；社会证明将依赖转化为反馈级联，可见的未核查使用会抑制核查，使群体陷入集体过度依赖；反馈设计可防止崩溃，让核查可见或削弱社会证明即可扭转该趋势。综上，研究将AI依赖视为计算社会动力学问题，个体学习、同伴观察与反馈暴露共同决定人群是否保持校准状态。
英文摘要
Whether AI assistance helps or harms a population depends less on the model's accuracy than on whether people rely on it appropriately trusting it when it is right and checking it when it is not. Yet reliance is usually studied one user at a time. We model it as a population process: agents repeatedly solve a task alone, accept an AI answer, or verify it, updating a Bayesian belief about AI quality and, when networked, learning from peers. Four results form one story. The environment sets the baseline: task difficulty and AI quality fix both overreliance and calibration regret. Social learning creates consensus, not overreliance: a mean-preservation theorem, confirmed by a 2*2 topology*tagging design, shows connectivity moves the aggregate only when influence transmits beliefs. Social proof turns reliance into a feedback cascade: visible unverified use suppresses verification and tips the population into collective overreliance. Feedback design can prevent collapse: making verification visible or dampening social proof reverses it. Together, the results frame AI reliance as a computational social dynamics problem, where individual learning, peer observation, and feedback exposure jointly shape whether a population remains calibrated.
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