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 sub- optimal 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.
Original language | English |
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Journal | Management Science |
Volume | 0 |
Issue number | 0 |
Early online date | Apr 2024 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- AI Advice
- Algorithmic recommendation
- human decision
- Adjustment cost
- delegation
Fields of Science and Technology Classification 2012
- 502 Economics