 In this probabilistic rather than binary setting, each individual \(i \in N\) has an opinion function \(Pr_i\) on a given agenda \(X\) (which is here typically an algebra of propositions), where, for each \(p \in X\), \(Pr_i(p)\) is the subjective probability or degree of belief (a real number in the interval [0,1]) that individual \(i\) assigns to \(p\). The function \(Pr_i\) is normally assumed to be probabilistically  coherent.[15]  The aim is to find an opinion pooling rule, again denoted \(F\), which assigns to each admissible profile \(\langle Pr_1, Pr_2,\ldots, Pr_n\rangle\) of individual opinion functions a collective opinion function \(Pr\), which should ideally also be probabilistically coherent. For each \(p \in X, Pr(p)\) is interpreted as the subjective probability or degree of belief that is collectively assigned to \(p\). A structurally similar aggregation problem can be found in the literature on peer disagreement, where an individual epistemic agent seeks to reconcile several potentially conflicting assignments of probabilities to some propositions—typically the agent’s own probability assignment and those of some epistemic peers—so as to arrive at an all-things-considered probability assignment (for an overview, see, e.g., Christensen and Lackey 2013).