Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

Daniel Garcia, Juha Tolvanen and Alexander Wagner

No 10849, CESifo Working Paper Series from CESifo

Abstract: 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.

Keywords: advice; algorithmic recommendations; human decisions; adjustment cost; delegation (search for similar items in EconPapers)
JEL-codes: D22 D83 L13 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ain, nep-com and nep-ind
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10849

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