Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

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.