The Power and Perils of MDL

We point out a potential weakness in the application of the celebrated minimum description length (MDL) principle for model selection. Specifically, it is shown that (although the index of the model class which actually minimizes a two-part code has many desirable properties) a model which has a shorter two- part code-length than another is not necessarily better (unless of course it achieves the global minimum). This is illustrated by an application to infer a grammar (DFA) from positive examples. We also analyze computability issues, and robustness under receding of the data. Generally, the classical approach is inadequate to express the goodness-of-fit of individual models for individual data sets. In practice however, this is precisely what we are interested in: both to express the goodness of a procedure and where and how it can fail. To achieve this practical goal, we paradoxically have to use the, supposedly impractical, vehicle of Kolmogorov complexity.