  Unexpectedness is a central concept in Simplicity Theory, a theory of
cognition relating various inferential processes to the computation of
Kolmogorov complexities, rather than probabilities. Its predictive power has
been confirmed by several experiments with human subjects, yet its theoretical
basis remains largely unexplored: why does it work? This paper lays the
groundwork for three theoretical conjectures. First, unexpectedness can be seen
as a generalization of Bayes' rule. Second, the frequentist core of
unexpectedness can be connected to the function of tracking ergodic properties
of the world. Third, unexpectedness can be seen as constituent of various
measures of divergence between the entropy of the world (environment) and the
variety of the observer (system). The resulting framework hints to research
directions that go beyond the division between probabilistic and logical
approaches, potentially bringing new insights into the extraction of causal
relations, and into the role of descriptive mechanisms in learning.
