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Competitive Model Selection in Algorithmic Targeting

Author

Listed:
  • Ganesh Iyer
  • T. Tony Ke

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.

Suggested Citation

  • Ganesh Iyer & T. Tony Ke, 2023. "Competitive Model Selection in Algorithmic Targeting," NBER Working Papers 31002, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:31002
    Note: IO
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    More about this item

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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