Competitive Model Selection in Algorithmic Targeting

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