  Quantifying the inconsistency of a database is motivated by various goals
including reliability estimation for new datasets and progress indication in
data cleaning. Another goal is to attribute to individual tuples a level of
responsibility to the overall inconsistency, and thereby prioritize tuples in
the explanation or inspection of dirt. Therefore, inconsistency quantification
and attribution have been a subject of much research in Knowledge
Representation and, more recently, in Databases. As in many other fields, a
conventional responsibility sharing mechanism is the Shapley value from
cooperative game theory. In this paper, we carry out a systematic investigation
of the complexity of the Shapley value in common inconsistency measures for
functional-dependency (FD) violations. For several measures we establish a full
classification of the FD sets into tractable and intractable classes with
respect to Shapley-value computation. We also study the complexity of
approximation in intractable cases.
