Aggregating information coming from multiple sources is a longstanding problem in both knowledge representation and multiagent systems (see, e.g., [28]). Depending on the chosen representation for the incoming pieces of knowledge or information, a number of competing approaches has seen the light in these literatures. Belief merging [21-23] studies the problem of aggregating propositional formulas coming from different agents into a set of models, subject to some integrity constraint. Judgment and binary aggregation [11, 12, 17] asks individual agents to report yes/no opinions on a set of logically-related binary issues - the agenda - in order to take a collective decision. Social welfare functions, the cornerstone problem in social choice theory (see, e.g., [2]), can also be viewed as mechanisms to merge conflicting information, namely the individual preferences of voters expressed in the form of linear orders over a set of alternatives. Other examples include graph aggregation [13], multi-agent argumentation [6-8], ontology merging [26], and clustering aggregation [15].