Markets for Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912637018701824 |
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| author | Dasaratha, Krishna Ortner, Juan Zhu, Chengyang |
| author_facet | Dasaratha, Krishna Ortner, Juan Zhu, Chengyang |
| contents | Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be expressed in terms of the \emph{bias-variance decompositions} of the models that firms sell. We give conditions when symmetric firms will choose different modeling techniques, e.g., each using only a subset of available covariates. We also show firms can choose inefficiently biased models or inefficiently costly models to deter entry by competitors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Markets for Models Dasaratha, Krishna Ortner, Juan Zhu, Chengyang Theoretical Economics Machine Learning Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be expressed in terms of the \emph{bias-variance decompositions} of the models that firms sell. We give conditions when symmetric firms will choose different modeling techniques, e.g., each using only a subset of available covariates. We also show firms can choose inefficiently biased models or inefficiently costly models to deter entry by competitors. |
| title | Markets for Models |
| topic | Theoretical Economics Machine Learning |
| url | https://arxiv.org/abs/2503.02946 |