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

By:

Iyer, Ganesh

Contributor(s):

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w31002.Publication details: Cambridge, Mass. National Bureau of Economic Research 2023.Description: 1 online resource: illustrations (black and white)Subject(s):

Other classification:

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w31002 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

Collection: Colección NBER Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	nber w3100 The McKibbin-Sachs Global Model: Theory and Specifications /	nber w31000 Human Capital and Climate Change /	nber w31001 Multidimensional Human Capital and the Wage Structure /	nber w31002 Competitive Model Selection in Algorithmic Targeting /	nber w31003 Asymmetries in Federal Reserve Objectives /	nber w31004 The Global Dollar Cycle /	nber w31005 Partial Identification of Treatment-Effect Distributions with Count-Valued Outcomes /	Next

March 2023.

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.