Competitive Model Selection in Algorithmic Targeting
研究市场竞争如何影响企业在目标定位中选择算法复杂度，发现竞争促使企业选择更简单（高偏差低方差）的算法，而垄断力强的企业更适合复杂算法。
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. History: Anthony Dukes served as the senior editor for this article. Funding: This work was supported by Hong Kong Research Grants Council [project number 14503122].