All AI Models are Wrong, but Some are Optimal
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910779720073216 |
|---|---|
| author | Anand, Akhil S Sawant, Shambhuraj Reinhardt, Dirk Gros, Sebastien |
| author_facet | Anand, Akhil S Sawant, Shambhuraj Reinhardt, Dirk Gros, Sebastien |
| contents | AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models often results in suboptimal performance. This is primarily because AI models are typically constructed to best fit the data, and hence to predict the most likely future rather than to enable high-performance decision-making. The hope that such prediction enables high-performance decisions is neither guaranteed in theory nor established in practice. In fact, there is increasing empirical evidence that predictive models must be tailored to decision-making objectives for performance. In this paper, we establish formal (necessary and sufficient) conditions that a predictive model (AI-based or not) must satisfy for a decision-making policy established using that model to be optimal. We then discuss their implications for building predictive AI models for sequential decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | All AI Models are Wrong, but Some are Optimal Anand, Akhil S Sawant, Shambhuraj Reinhardt, Dirk Gros, Sebastien Artificial Intelligence Machine Learning AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models often results in suboptimal performance. This is primarily because AI models are typically constructed to best fit the data, and hence to predict the most likely future rather than to enable high-performance decision-making. The hope that such prediction enables high-performance decisions is neither guaranteed in theory nor established in practice. In fact, there is increasing empirical evidence that predictive models must be tailored to decision-making objectives for performance. In this paper, we establish formal (necessary and sufficient) conditions that a predictive model (AI-based or not) must satisfy for a decision-making policy established using that model to be optimal. We then discuss their implications for building predictive AI models for sequential decision-making. |
| title | All AI Models are Wrong, but Some are Optimal |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.06086 |