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Bias-Variance Games

Published: 13 July 2022 Publication History

Abstract

Firms engaged in electronic commerce increasingly rely on predictive analytics via machine-learning algorithms to drive a wide array of managerial decisions. The tuning of many standard machine learning algorithms can be understood as trading off bias (i.e., accuracy) with variance (i.e., precision) in the algorithm's predictions. The goal of this paper is to understand how competition between firms affects their strategic choice of such algorithms. To this end, we model the interaction of two firms choosing learning algorithms as a game and analyze its equilibria. Absent competition, players care only about the magnitude of predictive error and not its source. In contrast, our main result is that with competition, players prefer to incur error due to variance rather than due to bias, even at the cost of higher total error. In addition, we show that competition can have counterintuitive implications---for example, reducing the error incurred by a firm's algorithm can be harmful to that firm---but we provide conditions under which such phenomena do not occur. In addition to our theoretical analysis, we also validate our insights by applying our metrics to a publicly available data set.

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cover image ACM Conferences
EC '22: Proceedings of the 23rd ACM Conference on Economics and Computation
July 2022
1269 pages
ISBN:9781450391504
DOI:10.1145/3490486
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2022

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  1. bias-variance tradeoff
  2. competition
  3. machine learning

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  • Extended-abstract

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EC '22
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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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EC '25
The 25th ACM Conference on Economics and Computation
July 7 - 11, 2025
Stanford , CA , USA

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