用模拟实验揭示人机团队中信任如何随可靠性变化，指导安全部署。
Agent-Based Simulation of Trust Development in Human-Robot Teams: An Empirically-Validated Framework
- 基于实证数据构建可模拟的人机信任模型，支持多角色协作测试。
- 机器人可靠性影响最大，解释45.4%的信任差异，且显著提升任务成功率。
- 发现信任与绩效可能脱钩，适合人机交互设计与安全评估研究者参考。
本文提出一个基于实证的代理模型，用于模拟人机团队中的信任动态、工作负荷分配及协作表现。模型在NetLogo 6.4.0中实现，模拟2–10个代理完成不同复杂度任务。通过与Hancock等（2021）元分析对比，8个信任前因类别中有4个达到区间有效性，等级有效性达Spearman ρ=0.833。采用OFAT与全因子设计（每条件50次重复）的敏感性分析显示，机器人可靠性对信任影响最强（η²=0.35），并主导任务成功（η²=0.93）和生产率（η²=0.89），与元分析一致。信任不对称比值范围为0.07–0.55，低于元分析基准1.50，表明事件级不对称不必然导致累积不对称，因信任修复机制持续作用。情景分析揭示信任-绩效解耦：在信任恢复场景中，尽管信任最低（38.2），生产力最高（4.29）；在不可靠机器人场景中，信任最高（73.2），但任务成功率最低（33.4%），说明校准误差是独立于信任强度的关键诊断指标。方差分析确认可靠性、透明度、沟通与协作均有显著主效应（p<.001），共解释45.4%的信任变异。开源实现为部署前识别过度信任与不足信任提供了证据基础。
原文摘要 · Abstract (English)
This paper presents an empirically grounded agent-based model capturing trust dynamics, workload distribution, and collaborative performance in human-robot teams. The model, implemented in NetLogo 6.4.0, simulates teams of 2--10 agents performing tasks of varying complexity. We validate against Hancock et al.'s (2021) meta-analysis, achieving interval validity for 4 of 8 trust antecedent categories and strong ordinal validity (Spearman \r{ho}=0.833ρ= 0.833 \r{ho}=0.833). Sensitivity analysis using OFAT and full factorial designs (n=50n = 50 n=50 replications per condition) reveals robot reliability exhibits the strongest effect on trust (η2=0.35η^2 = 0.35 η2=0.35) and dominates task success (η2=0.93η^2 = 0.93 η2=0.93) and productivity (η2=0.89η^2 = 0.89 η2=0.89), consistent with meta-analytic findings. Trust asymmetry ratios ranged from 0.07 to 0.55 -- below the meta-analytic benchmark of 1.50 -- revealing that per-event asymmetry does not guarantee cumulative asymmetry when trust repair mechanisms remain active. Scenario analysis uncovered trust-performance decoupling: the Trust Recovery scenario achieved the highest productivity (4.29) despite the lowest trust (38.2), while the Unreliable Robot scenario produced the highest trust (73.2) despite the lowest task success (33.4\%), establishing calibration error as a critical diagnostic distinct from trust magnitude. Factorial ANOVA confirmed significant main effects for reliability, transparency, communication, and collaboration (p<.001p < .001 p<.001), explaining 45.4\% of trust variance. The open-source implementation provides an evidence-based tool for identifying overtrust and undertrust conditions prior to deployment.
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