最佳决策并非最佳建议：做出具有依从性的推荐
The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations
AI总结：
本文提出一种考虑依从性的优化框架，分析部分依从性对最优推荐的影响，并提供工具以生成免疫于人类偏差的推荐策略。
摘要：
许多高风险决策采用专家在环结构，即人类操作员接收算法推荐但最终由其做决定。因此，算法的推荐可能与实际实施的决策不同。然而，大多数算法推荐是通过解决一个假设推荐将完美实施的优化问题得到的。我们提出了一种具有依从性的优化框架，以捕捉推荐与实施政策之间的二元性，并分析部分依从性对最优推荐的影响。我们显示，忽视部分依从性现象，如目前大多数推荐引擎所做，可能导致与当前人类基准性能和推荐算法预期相比严重性能下降。我们的框架还提供了有用的工具来分析结构并计算最优推荐策略，这些策略天然免疫于此类人类偏差，并保证比基准策略有所改进。
英文摘要：
Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm's recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations are obtained by solving an optimization problem that assumes recommendations will be perfectly implemented. We propose an adherence-aware optimization framework to capture the dichotomy between the recommended and the implemented policy and analyze the impact of partial adherence on the optimal recommendation. We show that overlooking the partial adherence phenomenon, as is currently being done by most recommendation engines, can lead to arbitrarily severe performance deterioration, compared with both the current human baseline performance and what is expected by the recommendation algorithm. Our framework also provides useful tools to analyze the structure and to compute optimal recommendation policies that are naturally immune against such human deviations, and are guaranteed to improve upon the baseline policy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
