Competitive Model Selection in Algorithmic Targeting

Ganesh Iyer and T. Tony Ke

No 31002, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D43 L13 M37 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-cmp, nep-com, nep-ind, nep-mic and nep-reg
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