Microfoundation Inference for Strategic Prediction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910908693872640 |
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| author | Bracale, Daniele Maity, Subha Polo, Felipe Maia Somerstep, Seamus Banerjee, Moulinath Sun, Yuekai |
| author_facet | Bracale, Daniele Maity, Subha Polo, Felipe Maia Somerstep, Seamus Banerjee, Moulinath Sun, Yuekai |
| contents | Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread adaptation of performative prediction in machine learning is that practitioners are generally unaware of the social impacts of their predictions. To address this gap, we propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents' responses as a cost-adjusted utility maximization problem and propose estimates for said cost. Our approach leverages optimal transport to align pre-model exposure (ex ante) and post-model exposure (ex post) distributions. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Microfoundation Inference for Strategic Prediction Bracale, Daniele Maity, Subha Polo, Felipe Maia Somerstep, Seamus Banerjee, Moulinath Sun, Yuekai Machine Learning Methodology Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread adaptation of performative prediction in machine learning is that practitioners are generally unaware of the social impacts of their predictions. To address this gap, we propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents' responses as a cost-adjusted utility maximization problem and propose estimates for said cost. Our approach leverages optimal transport to align pre-model exposure (ex ante) and post-model exposure (ex post) distributions. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset. |
| title | Microfoundation Inference for Strategic Prediction |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2411.08998 |