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Competitive Model Selection in Algorithmic Targeting
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OA
AI
DOI：10.1287/mksc.2023.0175.png)
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Abstract
En 中文
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.
Keywords:
algorithmic competition
model selection
algorithmic bias
data analytics
targeting
economics of AI
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10.1
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