Iyer, Ganesh. <br/><br/>
Competitive Model Selection in Algorithmic Targeting / 
Ganesh Iyer, T. Tony Ke. 
 - Cambridge, Mass.  National Bureau of Economic Research  2023. 
 - 1 online resource:  illustrations (black and white); 
 - NBER working paper series  no. w31002 .
 - Working Paper Series (National Bureau of Economic Research)  no. w31002. .
<br/><br/> March 2023. 
<br/><br/> This paper studies how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face the general trade-off between bias and variance when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm then appoints a data analyst that uses the chosen algorithm to estimate demand for multiple consumer segments, based on which, it devises a targeting policy to maximize estimated profit. We show that competition may induce firms to strategically choose simpler algorithms which involve more bias. This implies that more complex/flexible algorithms tend to have higher value for firms with greater monopoly power. 
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<br/><br/><label>Subjects--Topical Terms: </label><br/>Oligopoly and Other Forms of Market Imperfection<br/>Oligopoly and Other Imperfect Markets<br/>Advertising