学习信任对象：社会学习中的决策生成可信度
Learning Whom to Trust : Decision-Generated Credibility in Social Learning
中文总结 AI 辅助
该研究提出决策生成的社会信息可信度机制，通过强化学习智能体的漂移扩散过程建模，发现中等传播加速纠错、强传播锁定错误共识，为社会学习的错误放大问题提供了可检验的预测框架。
中文摘要 AI 辅助
社会互动可提升集体学习能力，但也会放大早期错误。我们研究当社会信息的可信度由发送者自身决策过程而非预先确定时的这种张力。强化学习智能体通过漂移扩散过程做出二元选择，该过程共同决定选择、决策时间和置信度；决策置信度随后通过加权预期影响和回顾性社会学习成为社会可信度。在均衡的社区曝光下，预期场存在精确的商表示。其局部雅可比矩阵是标量决策灵敏度项乘以社区耦合矩阵，这产生了共模放大阈值，以及跨社区渗透率在抑制相对社区差异中的分析作用。蒙特卡洛实验显示出相应的非单调性能模式：中等程度的传播加速错误修正，而强传播会将群体锁定在错误共识中；低渗透率则维持分歧。消融研究揭示了置信度的双重作用：对可信度敏感的传播会放大社会错误，而依赖置信度的个体学习则会稳定错误。该模型产生可检验的预测，将发送者的置信度与接收者的行为（以准确性为条件）关联起来。
英文摘要
Social interaction can improve collective learning but also amplify early mistakes. We study this tension when the credibility of social information is generated by the sender's own decision process rather than fixed ex ante. Reinforcement-learning agents make binary choices through a drift--diffusion process that jointly determines choice, decision time, and confidence; decision confidence then becomes social credibility by weighting anticipatory influence and retrospective social learning. Under balanced community exposure, the anticipatory field admits an exact quotient representation. Its local Jacobian is a scalar decision-sensitivity term multiplying the community-coupling matrix, which yields a common-mode amplification threshold and an analytical role for cross-community permeability in damping relative community differences. Monte Carlo experiments show the corresponding non-monotone performance pattern: moderate transmission accelerates correction, whereas strong transmission can lock populations into wrong consensus; low permeability instead sustains disagreement. Ablations reveal a dual role for confidence: credibility-sensitive transmission amplifies social error, while confidence-dependent private learning stabilises it. The model yields testable predictions linking sender confidence to receiver behaviour conditional on accuracy.
