【致思篇】UTD24/FT50中人工智能的应用与影响

摘要：Consumers increasingly use digital advice when making purchasing decisions. How do such tools change consumer behavior and what types of consumers are likely to use them? We examine these questions with a randomized controlled trial of digital expert advice in the context of prescription drug insurance. The intervention we study was effective at changing consumer choices. We propose that, conceptually, expert advice can affect consumer choices through two distinct channels: by updating consumer beliefs about product features (learning) and by influencing how much consumers value product features (interpretation). Using our trial data to estimate a model of consumer demand, we find that both channels are quantitatively important. Digital expert advice tools not only provide consumers with information, but also alter how consumers value product features. For example, consumers are willing to pay 14% less for a plan with the most popular brand and 37% less for an extra star rating when they incorporate digital expert advice on plan choice relative to only having information about product features. Further, we document substantial selection into the use of digital advice on two margins. Consumers who are inherently less active shoppers and those who we predict would have responded to advice more were less likely to demand it. Our results raise concerns regarding the ability of digital advice to alter consumer preferences as well as the distributional implications of greater access to digital expert advice.

消费者在做出购买决策时，越来越依赖数字化建议工具。然而，这些工具如何改变消费者行为，以及哪些类型的消费者更可能使用它们，尚不清楚。我们通过在处方药保险的背景下进行随机对照试验来研究这些问题。研究结果表明，干预措施有效地改变了消费者的选择。我们提出，专家建议从概念上讲可以通过两个不同的渠道影响消费者选择：一是通过更新消费者对产品特征的信念 (学习)，二是通过影响消费者对产品特征的重视程度 (解释)。利用试验数据，我们估计了消费者需求模型，发现这两个渠道在数量上都对消费者行为产生了显著影响。具体而言，数字专家建议工具不仅提供了信息，还改变了消费者对产品特征的重视程度。例如，相较于仅有关于产品特征的信息的情况下，消费者在结合数字专家建议进行选择时，愿意为最受欢迎品牌的计划支付少14%，而对于额外星级评分的计划，则愿意支付少37%。此外，我们还发现，消费者在使用数字建议方面存在显著的选择偏好。天生不太活跃的购物者，以及更可能对建议做出反应的消费者，实际上更不可能选择这种建议。我们的结果引发了对数字建议在改变消费者偏好能力，及其对更广泛获取数字专家建议的分配效应的担忧。

https://doi.org/10.1287/mnsc.2020.02453

摘要：We document an unprecedented brain drain of Artificial Intelligence (AI) professors from universities from 2004 to 2018. We find that students from the affected universities establish fewer AI startups and raise less funding. The brain‐drain effect is significant for tenured professors, professors from top universities, and deep‐learning professors. Additional evidence suggests that unobserved city‐ and university‐level shocks are unlikely to drive our results. We consider several economic channels for the findings. The most consistent explanation is that professors' departures reduce startup founders' AI knowledge, which we find is an important factor for successful startup formation and fundraising.

我们记录了 2004 年至 2018 年期间，大学人工智能 (AI) 教授前所未有的人才流失现象。研究发现，受影响大学的学生创办的AI初创企业数量显著减少，且这些初创企业筹集的资金也显著降低。人才流失效应在终身教授、顶尖大学教授和深度学习领域的教授中尤为显著。进一步分析表明，未观察到的城市和大学层面的冲击不太可能解释我们的结果。我们考虑了研究结果的几个经济渠道。其中最一致的解释是，教授的离职导致了AI知识的流失，进而影响了初创公司的成功创立和筹集资金。

https://doi.org/10.1111/jofi.13302