Algorithmic Targeting and the Precision-Recall Tradeoff
研究了竞争环境下企业如何权衡算法目标定位的精确率和召回率，发现竞争企业比垄断企业更偏好高精确率低召回率的策略，且算法相关性越高目标消费者越少。
We examine the implications of competitive algorithmic targeting when outcomes of targeting algorithms are the individual consumer-level predicted probabilities of conversion. In these situations, firms implicitly face the well-known precision-recall tradeoff while choosing their targeting strategies. They can choose to target a smaller set of consumers with a high probability of conversion (precision) but miss out on many consumers who might still be interested in their product. Conversely, firms can target a larger set of consumers (recall), but this results in a greater probability that their targeting is wasted on uninterested consumers. We analyze this precision-recall tradeoff under competition between firms that strategically choose their algorithmic targeting policies. We show that competing firms favor a targeting policy that has higher precision but lower recall compared with a monopoly. Firms target fewer consumers when their algorithms are more correlated. They also have the incentive to strategically decrease the precision of their targeting policies in order to reduce competition. If firms endogenously choose their algorithmic correlation, then there is an equilibrium incentive to decrease the correlation. History: Anthony Dukes served as the senior editor for this article. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2024.0930 .