选择(遗传算法)
计算机科学
选型
产业组织
经济
人工智能
作者
Ganesh Iyer,T. Tony Ke
出处
期刊:Marketing Science
[Institute for Operations Research and the Management Sciences]
日期:2024-06-25
标识
DOI:10.1287/mksc.2023.0175
摘要
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].
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