  If $A$ and $B$ are sets such that $A \subset B$, generalisation may be
understood as the inference from $A$ of a hypothesis sufficient to construct
$B$. One might infer any number of hypotheses from $A$, yet only some of those
may generalise to $B$. How can one know which are likely to generalise? One
strategy is to choose the shortest, equating the ability to compress
information with the ability to generalise (a proxy for intelligence). We
examine this in the context of a mathematical formalism of enactive cognition.
We show that compression is neither necessary nor sufficient to maximise
performance (measured in terms of the probability of a hypothesis
generalising). We formulate a proxy unrelated to length or simplicity, called
weakness. We show that if tasks are uniformly distributed, then there is no
choice of proxy that performs at least as well as weakness maximisation in all
tasks while performing strictly better in at least one. In experiments
comparing maximum weakness and minimum description length in the context of
binary arithmetic, the former generalised at between $1.1$ and $5$ times the
rate of the latter. We argue this demonstrates that weakness is a far better
proxy, and explains why Deepmind's Apperception Engine is able to generalise
effectively.
