We study how managers allocate resources in response to algorithmic recommendations that are programmed with specific levels of risk aversion. Using the anchoring and adjustment heuristic, we derive our predictions and test them in a series of multi-item newsvendor experiments. We find that highly risk-averse algorithmic recommendations have a strong and persistent influence on order decisions, even after the recommendations are no longer available. Furthermore, we show that these effects are similar regardless of factors such as source of advice (i.e., human vs. algorithm) and decision autonomy (i.e., whether the algorithm is externally assigned or chosen by the subjects themselves). Finally, we disentangle the effect of risk attitude from that of anchor distance and find that subjects selectively adjust their order decisions by relying more on algorithmic advice that contrasts with their inherent risk preferences. Our findings suggest that organizations can strategically utilize risk-averse algorithmic tools to improve inventory decisions while preserving managerial autonomy.

中文翻译：

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规避风险的算法支持和库存管理


我们研究管理者如何分配资源以响应具有特定级别风险厌恶的算法建议。使用锚定和调整启发式方法，我们得出预测并在一系列多项新闻供应商实验中对其进行测试。我们发现，高度规避风险的算法建议对订单决策具有强大而持久的影响，即使在这些建议不再可用之后也是如此。此外，我们表明，无论建议来源（即人类与算法）和决策自主性（即算法是外部分配还是由受试者自己选择）等因素，这些效果都是相似的。最后，我们将风险态度的影响与锚定距离的影响分开，发现受试者更多地依赖与其固有风险偏好形成鲜明对比的算法建议，从而选择性地调整他们的订单决策。我们的研究结果表明，组织可以战略性地利用规避风险的算法工具来改进库存决策，同时保持管理自主权。