Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing
利用酒店客房定价数据，研究算法建议与人类决策的互动，发现调整成本导致人类决策者与算法顾问的利益冲突，算法建议存在战略性偏差，造成次优定价。
We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice with costly price adjustments shows that, in equilibrium, algorithmic price recommendations are strategically biased and lead to suboptimal pricing by human decision makers. We quantify the losses from the strategic bias in recommendations using as structural model and estimate the potential benefits that would result from a shift to fully automated algorithmic pricing. This paper was accepted by Axel Ockenfels, Special Issue on the Human-Algorithm Connection. Funding: D. Garcia gratefully acknowledges that this research was funded in part by the Austrian Science Fund [Grant FWF-FG6]. A. K. Wagner gratefully acknowledges financial support from the Anniversary Fund of the Oesterreichische Nationalbank [Project 18878]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03740 .