 Similar quantitative considerations play a role in the Maximum Entropy Approach in  Goldzsmidt et al. (1993)  which is based on \(\epsilon\)-semantics. The basic idea is similar to Lexicographic Closure: \(\phi \nc \psi\) is entailed by \(\mathbf{K}\) if \(\min(\{\kappa(M) \mid M \models \phi \wedge \psi\}) &lt; \min (\{\kappa(M) \mid M \models \phi \wedge \neg \psi\})\), where \(\kappa(M)\) outputs a weigthed sum of violations in \(M\) and where weights are attributed to violations in view of specificity considerations. Similar to Lexicographic Closure the violation of more specific conditionals weigh heavier than violations of more general conditionals. As a consequence, just like in Lexicographic Closure, \(l \nc f\) is entailed in our example and so the drowning problem is avoided. A difference to Lexicographic Closure is, for instance, that \(l \wedge \neg f \nc c\) is not entailed according to the maximal entropy approach. The reason is that the violation of several less specific assertions may in sum counter-balance the violation of a more specific assertion. For instance, while model \(M_7\) is preferred over model \(M_8\) in the Lexicographic approach, in the Maximum Entropy both models turn out to be equally good and the best models of \(l \wedge \neg f\). 