  Maximum-entropy ensembles are key primitives in statistical mechanics from
which thermodynamic properties can be derived. Over the decades, several
approaches have been put forward in order to justify from minimal assumptions
the use of these ensembles in statistical descriptions. However, there is still
no full consensus on the precise reasoning justifying the use of such
ensembles. In this work, we provide a new approach to derive maximum-entropy
ensembles taking a strictly operational perspective. We investigate the set of
possible transitions that a system can undergo together with an environment,
when one only has partial information about both the system and its
environment. The set of all these allowed transitions encodes thermodynamic
laws and limitations on thermodynamic tasks as particular cases. Our main
result is that the set of allowed transitions coincides with the one possible
if both system and environment were assigned the maximum entropy state
compatible with the partial information. This justifies the overwhelming
success of such ensembles and provides a derivation without relying on
considerations of typicality or information-theoretic measures.
