# Is there any Need to Mention Induction?

- Chris Thornton,
*University of Sussex*

## Abstract

Induction is the process by which seen data becomes the
basis for predicting unseen data. There has long been
a desire to explain the procedure in a context-free way.
But Hume's circularity problem and the no-free-lunch
theorems seem to suggest the impossibility
of any context-free mechanism. Machine Learning takes
the position no such mechanism exists. But an
alternative comes from Epistemology. Popper's
falsificationist theory holds there is a general
mechanism, but that it does not perform induction.
Inductive effects arise implicitly, through pursuit of a
non-inductive goal. Less plausibly, the mechanism is
taken to be uninformed exploration of hypotheses. But as
the paper shows, Popper's solution can be reworked using
information theory. Increasing the informational
efficiency with which representations predict seen data
can be shown to produce inductive effects. With
representation optimization taking the place of
hypothesis-search in the argument, it becomes
possible to explain induction in a context-free way.

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