Proceedings of the Annual Meeting of the Cognitive Science Society

Traditionally, regularity in a data set is assessed by fitting a model to the data and examining the extent to which the variance accounted for by the model is large compared to the overall variance in the system. Such approaches, however, do not address the complementary question of how much regularity is present in the data, in the first place, and how much work is expected to be required to capture a particular amount of regularity. In this work we use the notion of Kolmogorov complexity to derive a measure of system regularity independent of any particular model. Thus, in our framework, the explanatory adequacy of a model can be readily quantified, so that one can examine the extent to which the model is satisfactory, or whether additional mechanisms need be postulated.