  When designing a robot's internal system, one often makes assumptions about
the structure of the intended environment of the robot. One may even assign
meaning to various internal components of the robot in terms of expected
environmental correlates. In this paper we want to make the distinction between
robot's internal and external worlds clear-cut. Can the robot learn about its
environment, relying only on internally available information, including the
sensor data? Are there mathematical conditions on the internal robot system
which can be internally verified and make the robot's internal system mirror
the structure of the environment? We prove that sufficiency is such a
mathematical principle, and mathematically describe the emergence of the
robot's internal structure isomorphic or bisimulation equivalent to that of the
environment. A connection to the free-energy principle is established, when
sufficiency is interpreted as a limit case of surprise minimization. As such,
we show that surprise minimization leads to having an internal model isomorphic
to the environment. This also parallels the Good Regulator Principle which
states that controlling a system sufficiently well means having a model of it.
Unlike the mentioned theories, ours is discrete, and non-probabilistic.
