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Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events

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  • Juan Manuel Sánchez-Cartas

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

  • Alberto Tejero

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

  • Gonzalo León

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

Abstract

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

Suggested Citation

  • Juan Manuel Sánchez-Cartas & Alberto Tejero & Gonzalo León, 2021. "Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events," Sustainability, MDPI, vol. 13(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2542-:d:506458
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    References listed on IDEAS

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