 There are infinitely many candidates for \(m\), and hence \(c\), even for very simple languages. Carnap argues for his favored measure “\(m^*\)” by insisting that the only thing that significantly distinguishes individuals from one another is some qualitative difference, not just a difference in labeling. Call a structure description a maximal set of state descriptions, each of which can be obtained from another by some permutation of the individual names. \(m^*\) assigns each structure description equal measure, which in turn is divided equally among their constituent state descriptions. It gives greater weight to homogenous state descriptions than to heterogeneous ones, thus ‘rewarding’ uniformity among the individuals in accordance with putatively reasonable inductive practice. The induced \(c^*\) allows inductive learning from experience.