It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910149834178560 |
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| author | Fowlie, Andrew |
| author_facet | Fowlie, Andrew |
| contents | Prompted by misconceptions in the recent literature, we review the justifications for naturalness arguments and Occam's razor found in Bayesian statistics. We discuss the automatic Occam's razor that emerges in Bayesian formalism, bringing together points of view from diverse fields, including statistics, social sciences, physics and machine learning. In pedagogical calculations, we demonstrate that this automatic razor disfavors unnatural models in which predictions must be fine-tuned to agree with observation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18656 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty Fowlie, Andrew History and Philosophy of Physics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability Prompted by misconceptions in the recent literature, we review the justifications for naturalness arguments and Occam's razor found in Bayesian statistics. We discuss the automatic Occam's razor that emerges in Bayesian formalism, bringing together points of view from diverse fields, including statistics, social sciences, physics and machine learning. In pedagogical calculations, we demonstrate that this automatic razor disfavors unnatural models in which predictions must be fine-tuned to agree with observation. |
| title | It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty |
| topic | History and Philosophy of Physics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2604.18656 |