Inference principles are postulated within statistics, they are not usually derived from any underlying physical constraints on real world observers. An exception to this rule is that in the context of partially observable information engines decision making can be based solely on physical arguments. An inference principle can be derived from minimization of the lower bound on average dissipation [Phys. Rev. Lett., 124(5), 050601], which is achievable with a quasi-static process. Thermodynamically rational decision strategies can be computed algorithmically with the resulting approach. Here, we use this to study an example of binary decision making under uncertainty that is very simple, yet just interesting enough to be non-trivial: observations are either entirely uninformative, or they carry complete certainty about the variable that needs to be known for successful energy harvesting. Solutions found algorithmically can be expressed in terms of parameterized soft partitions of the observable space. This allows for their interpretation, as well as for the analytical calculation of all quantities that characterize the decision problem and the thermodynamically rational strategies.

翻译：统计中的推理原理通常源于假设，而非基于真实世界中观测者的物理约束。然而，在部分可观测信息引擎中，决策可完全依据物理论证。通过最小化平均耗散的下界[Phys. Rev. Lett., 124(5), 050601]（其可通过准静态过程实现），可推导出推理原理。基于此方法可算法化地计算热力学理性决策策略。本文运用该原理研究一个极简却具非平凡性的二元不确定性决策案例：观测值要么完全不提供信息，要么完全确定变量（该变量为成功能量收集所必需）。算法求解的策略可表示为可观测空间的参数化软划分，这不仅便于解释，还能解析计算表征决策问题及热力学理性策略的所有量。