Competitive Model Selection in Algorithmic Targeting / Ganesh Iyer, T. Tony Ke.

Format:

Book

Author/Creator:

Iyer, Ganesh.

Contributor:

National Bureau of Economic Research.

Ke, T. Tony.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31002.

NBER working paper series no. w31002

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

This paper studies how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face the general trade-off between bias and variance when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm then appoints a data analyst that uses the chosen algorithm to estimate demand for multiple consumer segments, based on which, it devises a targeting policy to maximize estimated profit. We show that competition may induce firms to strategically choose simpler algorithms which involve more bias. This implies that more complex/flexible algorithms tend to have higher value for firms with greater monopoly power.

Notes:

Print version record

March 2023.