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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.10580 |
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| _version_ | 1866910524556443648 |
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| author | Daimer, Dorian Still, Susanne |
| author_facet | Daimer, Dorian Still, Susanne |
| contents | Information engines model ``Maxwell's demon" mechanistically. However, the demon's strategy is pre-described by an external experimenter, and information engines are conveniently designed such that observables contain complete information about variables pertinent to work extraction. In real world scenarios, it is more realistic to encounter partial observability, which forces the physical observer, an integral part of the information engine, to make inferences from incomplete knowledge. Here, we use the fact that an algorithm for computing optimal strategies can be directly derived from maximizing overall engine work output. For a simple binary decision problem, we discover interesting optimal strategies that differ notably from naive coarse graining. They inspire a model class of simple, yet compelling, parameterized soft partitionings of the observable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10580 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The physical observer in a Szilard engine with uncertainty Daimer, Dorian Still, Susanne Statistical Mechanics Information engines model ``Maxwell's demon" mechanistically. However, the demon's strategy is pre-described by an external experimenter, and information engines are conveniently designed such that observables contain complete information about variables pertinent to work extraction. In real world scenarios, it is more realistic to encounter partial observability, which forces the physical observer, an integral part of the information engine, to make inferences from incomplete knowledge. Here, we use the fact that an algorithm for computing optimal strategies can be directly derived from maximizing overall engine work output. For a simple binary decision problem, we discover interesting optimal strategies that differ notably from naive coarse graining. They inspire a model class of simple, yet compelling, parameterized soft partitionings of the observable. |
| title | The physical observer in a Szilard engine with uncertainty |
| topic | Statistical Mechanics |
| url | https://arxiv.org/abs/2309.10580 |