We investigate the impacts of the decision support system's recommendations on decision makers' psychology and decision behaviors under uncertain contexts where optimal solutions exist. As a representative of such contexts, the newsvendor problem is studied by using the method of laboratory experiments. Through providing an elaborately designed decision support system in Experiment I, we validate that the optimal recommendations help to alleviate human newsvendors' Pull-to-Center bias, i.e., the actual orders fall in the range between mean demand and optimal order that maximizes the expected profit theoretically, and decrease the bias asymmetry under two profit conditions (high or low). We also reveal that optimal recommendations can't eliminate the bias, as decision makers exhibit two competing psychological factors simultaneously when using the decision support system: algorithm aversion and regret aversion. Algorithm aversion persistently impedes them from following the superior recommendations, while regret aversion sometimes pulls them to approach to the recommendations driven by the feeling of experienced regret. Further, we redesign the decision support system in Experiment II and find that, although the conservative system recommendations are valueless compared with the optimal one, the well-designed radical system recommendations may eliminate the Pull-to-Center bias under the high-profit condition, through the interaction of the dominant regret aversion, dominated algorithm aversion, and the anchoring effect.

在存在最佳解决方案的不确定环境下，我们研究了决策支持系统的建议对决策者的心理和决策行为的影响。作为这种情况的代表，通过使用实验室实验的方法来研究新闻卖主问题。通过在实验I中提供精心设计的决策支持系统，我们验证了最佳建议有助于减轻人类新闻卖主的“以中心为中心”的偏见，即实际订单落在平均需求和使预期最大化的最佳订单之间的范围内。从理论上获利，并在两个获利条件（高或低）下降低偏差不对称性。我们还发现，最佳建议无法消除偏见，因为决策者在使用决策支持系统时会同时表现出两个相互竞争的心理因素：算法规避和后悔规避。厌恶算法会持续阻碍他们遵循高级建议，而后悔厌恶有时会使他们靠经验丰富的后悔感而逼近建议。此外，我们在实验II中重新设计了决策支持系统，发现尽管保守的系统建议与最优的系统建议相比没有价值，但精心设计的基本系统建议可能会消除高利润条件下的“以中心为中心”的偏见，通过优势后悔厌恶，主导算法厌恶和锚定效应之间的相互作用。厌恶算法会持续阻碍他们遵循高级建议，而后悔厌恶有时会使他们受经验丰富的后悔之感驱使接近建议。此外，我们在实验II中重新设计了决策支持系统，发现尽管保守的系统建议与最优的系统建议相比没有价值，但精心设计的基本系统建议可能会消除高利润条件下的“以中心为中心”的偏见，通过优势后悔厌恶，主导算法厌恶和锚定效应之间的相互作用。厌恶算法会持续阻碍他们遵循高级建议，而后悔厌恶有时会使他们受经验丰富的后悔之感驱使接近建议。此外，我们在实验II中重新设计了决策支持系统，发现尽管保守的系统建议与最优的系统建议相比没有价值，但精心设计的基本系统建议可能会消除高利润条件下的“以中心为中心”的偏见，通过优势后悔厌恶，主导算法厌恶和锚定效应之间的相互作用。