Competitive Model Selection in Algorithmic Targeting

Ganesh Iyer () and T. Tony Ke ()
Additional contact information
Ganesh Iyer: University of California at Berkeley, Berkeley, California 94720
T. Tony Ke: Chinese University of Hong Kong, Hong Kong

Marketing Science, 2024, vol. 43, issue 6, 1226-1241

Abstract: We study how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face a general bias-variance trade-off when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm has a data analyst who uses the chosen algorithm to estimate demand for multiple consumer segments, based on which it devises a targeting policy to maximize estimated profits. We show that competition induces firms to strategically choose simpler algorithms that involve more bias but lower variance. Therefore, more complex/flexible algorithms may have higher value for firms with greater monopoly power.

Keywords: algorithmic competition; model selection; algorithmic bias; data analytics; targeting; economics of AI (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.2023.0175 (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:inm:ormksc:v:43:y:2024:i:6:p:1226-1241

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().