The physical observer in a Szilard engine with uncertainty

TL;DR

This paper investigates how a physical observer in a Szilard engine makes optimal decisions under partial observability, revealing strategies that outperform naive approaches and inspiring new soft partitioning models.

Contribution

It introduces a framework for deriving optimal strategies for physical observers in information engines with incomplete information, highlighting novel decision strategies.

Findings

Optimal strategies differ from naive coarse graining.

Soft partitioning models improve decision-making.

Strategies maximize overall engine work output.

Abstract

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.