Competitive Model Selection in Algorithmic Targeting
Ganesh V. Iyer, T. Tony Ke · National Bureau of Economic Research · 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.