Suppose we wanted our epistemology to apply not just to individuals, but to “group agents” like committees, governments, companies, and so on. Such agents may be made up of members who disagree. Levi (1986, 1999) has argued that representation of such conflict is better handled with sets of probabilities than with precise probabilities. There is a rich literature on combining or aggregating the (probabilistic) opinions of members of groups (Genest and Zidek 1986) but the outcome of such aggregation does not adequately represent the disagreement among the group. Some forms of aggregation also fail to respect plausible constraints on group belief. For example, if every member of the group agrees that \(X\) and \(Y\) are probabilistically independent, then it seems plausible to require that the group belief respects this unanimity. It is, however, well known that linear pooling—a simple and popular form of aggregation—does not respect this desideratum. Consider two probability functions \(p, q\) such that \(p(X) = p(Y) = 1/3\) and \(p(X\mid Y)=p(X)\) while \(q(X) = q(Y) = 2/3\) and \(q(X\mid Y)=q(X)\). Consider aggregating these two probabilities by taking an unweighted average of them: \(r = p/2 + q/2\). Now, calculation shows that \(r(X\cap Y) = 5/18\) while \(r(X)r(Y) = 1/4\), thus demonstrating that \(r\) does not consider \(X\) and \(Y\) to be independent. So such an aggregation method does not satisfy the above desideratum (Kyburg and Pittarelli 1992; Cozman 2012). For more on judgement aggregation in groups, see List and Pettit (2011), especially chapter 2.