Daniel Garcia

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 .

In markets, such as those for airline tickets and hotel accommodations, firms sell time-dated products and have private information about unsold capacities. We show that competition under private information may explain observed phenomena, such as increased price dispersion and higher expected prices toward the deadline. We also show that private information severely limits the market power of firms and that information exchange about capacity increases firms’ profits. Finally, we inquire into the incentives to unilaterally disclose information or to engage in espionage about rival’s capacity and show that they increase firms’ profits compared with the private information setting. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was partially funded by the Austrian Science Foundation FWF under [Project FG 6]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4613 .