 The interest of computer scientists for aggregation methods is witnessed by the fact that judgment aggregation is now among the topics of computational social choice, an interdisciplinary discipline that promotes exchanges and interactions between computer science and social choice theory. Computational issues of aggregation rules are among the interests of computational social choice. The intuition behind the application of complexity theory to aggregation rules is to assess the acceptability of an aggregation rule on the basis of pragmatic considerations, that is, the algorithmic feasibility of applying that rule. So, an aggregation rule is acceptable when its outcome is ‘easy’ to compute, that is, it can be solved by an algorithm in time which grows – at worst – polynomially with the size of the input (only in some pathological cases we can imagine desiring a rule that will not be able to return an outcome in a foreseeable future). On the other hand, if an aggregation rule is manipulable, then it is acceptable when it is ‘hard’ for an individual to manipulate it. Thus, the study of the computational complexity of aggregation rules may reveal that, even though a rule is manipulable, it is actually hard for an individual to act on that. The computational complexity of the distance-based procedures has been studied (Endriss et al. 2012; Endriss and de Haan 2015). The high computational complexity of Hamming rule in judgment aggregation mirrors a parallel result that the Kemeny rule in preference aggregation is also highly computational complex, as first shown in Bartholdi et al. (1989) and Hudry (1989). A new rule has been proposed to overcome the high computational complexity of distance-based procedures. The average-voter rule (Grandi 2012) selects the judgment set submitted by the individuals that minimizes the sum of the distances. Hence, the outcome has to be one of the submitted judgment sets. This allows reducing the computational complexity and, at the same time, selects the most representative individual.