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Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

Author

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  • Daniel Garcia
  • Juha Tolvanen
  • Alexander K. Wagner

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.

Suggested Citation

  • Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2023. "Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing," CESifo Working Paper Series 10849, CESifo.
    • Handle: RePEc:ces:ceswps:_10849
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    Keywords

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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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