Human decision-making is strongly influenced by cognitive biases, particularly under conditions of uncertainty and risk. While prior work has examined bias in single-step decisions with immediate outcomes and in human interaction with a single autonomous agent, comparatively little attention has been paid to decision-making under delayed outcomes involving multiple AI agents, where decisions at each step affect subsequent states. In this work, we study how delayed outcomes shape decision-making and responsibility attribution in a multi-agent human-AI task. Using a controlled game-based experiment, we analyze how participants adjust their behavior following positive and negative outcomes. We observe asymmetric responses to gains and losses, with stronger corrective adjustments after negative outcomes. Importantly, participants often fail to correctly identify the actions that caused failure and misattribute responsibility across AI agents, leading to systematic revisions of decisions that are weakly related to the underlying causes of poor performance. We refer to this phenomenon as a form of attribution bias, manifested as biased error attribution under delayed feedback. Our findings highlight how cognitive biases can be amplified in human-AI systems with delayed outcomes and multiple autonomous agents, underscoring the need for decision-support systems that better support causal understanding and learning over time.

翻译：人类决策深受认知偏差的影响，尤其在不确定性和风险条件下。先前研究探讨了即时结果下单步决策中的偏差，以及人类与单个自主智能体交互中的偏差，但相对而言，很少关注涉及多个AI智能体且决策每一步都影响后续状态的延迟结果下的决策过程。在本工作中，我们研究了延迟结果如何塑造多智能体人机任务中的决策与责任归因。通过一项受控的基于游戏的实验，我们分析了参与者在面对正负结果时如何调整其行为。我们观察到对收益和损失的不对称反应，即在负结果后会有更强的修正性调整。重要的是，参与者常常无法正确识别导致失败的行动，并在AI智能体间错误地归因责任，从而导致系统地修订那些与导致绩效差的根本原因关联较弱的决策。我们将此现象称为一种归因偏差的表现，即在延迟反馈下表现为有偏的错误归因。我们的发现强调了认知偏差如何在具有延迟结果和多个自主智能体的人机系统中被放大，凸显了需要更好地支持因果理解和随时间学习的决策支持系统。