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

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.

Artificial Intelligence, Algorithmic Competition and Market Structures

“…To facilitate comparisons with other works, we adopt the baseline parametrization of [6], [11]. The main reason to In the case of Q-learning, it requires a finite action space.…”

“…In the US, some Amazon sellers were fined for price gouging. 4 Algorithms may get trapped in price gouging solutions, which may even drive up the prices of non-algorithmic firms [11]. Another challenge that firms may face is whether they should develop their own algorithms or outsource them.…”

“…Firms using algorithmic pricing compete in three different market structures: Logit, Hotelling, and Linear demand. The Logit model has become a standard in the algorithmic pricing literature [6], [10], [11]. The Hotelling and the linear demand model are well-known in operations research and economic modeling [12], [13].…”