Delegate Pricing Decisions to an Algorithm? Experimental Evidence

Hans-Theo Normann, Nina Ruli\'e, Olaf Stypa and Tobias Werner

Papers from arXiv.org

Abstract: We analyze the delegation of pricing by participants, representing firms, to a collusive, self-learning algorithm in a repeated Bertrand experiment. In the baseline treatment, participants set prices themselves. In the other treatments, participants can either delegate pricing to the algorithm at the beginning of each supergame or receive algorithmic recommendations that they can override. Participants delegate more when they can override the algorithm's decisions. In both algorithmic treatments, prices are lower than in the baseline. Our results indicate that while self-learning pricing algorithms can be collusive, they can foster competition rather than collusion with humans-in-the-loop.

Date: 2025-10
New Economics Papers: this item is included in nep-ain, nep-com, nep-exp and nep-reg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2510.27636 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2510.27636

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().