  What does it take for a system, biological or not, to have goals? Here, this
question is approached in the context of in silico artificial evolution. By
examining the informational and causal properties of artificial organisms
('animats') controlled by small, adaptive neural networks (Markov Brains), this
essay discusses necessary requirements for intrinsic information, autonomy, and
meaning. The focus lies on comparing two types of Markov Brains that evolved in
the same simple environment: one with purely feedforward connections between
its elements, the other with an integrated set of elements that causally
constrain each other. While both types of brains 'process' information about
their environment and are equally fit, only the integrated one forms a causally
autonomous entity above a background of external influences. This suggests that
to assess whether goals are meaningful for a system itself, it is important to
understand what the system is, rather than what it does.
