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Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

Author

Listed:
  • 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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    References listed on IDEAS

    as
    1. Maurice E. Schweitzer & Gérard P. Cachon, 2000. "Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence," Management Science, INFORMS, vol. 46(3), pages 404-420, March.
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    More about this item

    Keywords

    advice; algorithmic recommendations; human decisions; adjustment cost; delegation;
    All these keywords.

    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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