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publications

Partially Observable Szilard Engines

Published in New J. Phys. 24 073031, 2022

This papers provides the first example of a Partially Observable Szilard Engine. The partially observable engine is obtained from a standard Szilard Engine by inserting the divider at an angle instead of horizontally. The resulting family of engines (parameterized by the angle) is analyzed in great detail within the framework of Generalized Partially Observable Information Engines. Interestingly, optimal data encodings for the partially observable engines are probabilistic in general and we provide a scheme to construct these probabilistic data representations in a simple way. Download paper here

Recommended citation: Still, S., & Daimer, D. (2022). Partially Observable Szilard Engines. New J. Phys. 24 073031.

The physical observer in a Szilard engine with uncertainty

Published in arXiv preprint arXiv:2309.10580, 2023

We use the fact that an algorithm for computing optimal strategies can be directly derived from maximizing overall engine work output in generalized partially observable information engines. For a stylizedly simple 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. We analyze and compare optimal strategies for three different observer classes: (1) optimal observers, (2) observers limited to the parameterized soft partitionings introduced here and (3) observers limited to coarse graining. While coarse graining based observers are outperformed by the other two types of observers, there is no difference in performance between unconstrained, optimal observers and those limited to soft partitionings. The parameterized soft partitioning strategies allow us to compute key quantities of the decision problem analytically. Download paper here

Recommended citation: Daimer, D. & Still, S. (2023). The physical observer in a Szilard engine with uncertainty. arXiv preprint arXiv:2309.10580.

Thermodynamically rational decision making under uncertainty

Published in arXiv preprint arXiv:2309.10476, 2023

We use the framework of partially observable information engines to obtain an analytical characterization of thermodynamically rational agent behaviour for a simple, yet non–trivial example of a Maxwells demon operating under uncertainty. Our results provide the first complete detailed physical understanding of a decision problem under uncertainty. Download paper here

Recommended citation: Daimer, D. & Still, S. (2023). Thermodynamically rational decision making under uncertainty. arXiv preprint arXiv:2309.10476.

Physical Observers and Quantum Reconstructions

Published in arXiv preprint arXiv:2506.01561, 2025

There is a multitude of interpretations of quantum mechanics, but foundational principles are lacking. Relational quantum mechanics views the observer as a physical system, which allows for an unambiguous interpretation as all axioms are purely operational, describing how observers acquire information. The approach, however, is based on the premise that the observer retains only predictive information about the observed system. Here, we justify this premise using the following principle: Physically embedded observers choose information processing strategies that provide them with the option to approach physical limits to the greatest possible extent. Download paper here

Recommended citation: Daimer, D. & Still, S. (2025). Physical Observers and Quantum Reconstructions. arXiv preprint arXiv:2506.01561.

talks

Observable partitioning under uncertainty in information engines with physically implemented observer memories

Published: March 09, 2023

This poster presentation at the APS March Meeting 2023 explores a simple model class of information engines with uncertainty: Observations either carry no uncertainty or maximum uncertainty about the relevant quantity. Within the framework of generalized, partially observable information engines the optimal data representation strategies are found and a concrete physical model to realize these encodings is presented. Two different approximations of the optimal encodings, coarse graining and soft partitioning, are analyzed for comparison.

teaching

Mathematics Course for Physics Freshmen

Undergraduate course, Humboldt Universität zu Berlin, Physics, 2013

This course was organized by the Physics student body. It consisted of lectures as well as practice sessions and was taught voluntarily by more advanced Physics students. The aim of the course was to introduce the Physics Freshmen to the necessary concepts from Mathematics required for their first year of Physics classes.

Dorian Daimer