 It is crucial to highlight that this semantics does not capture the non-monotonic interpretation of counterfactual antecedents. For example, \({\llbracket}\mathsf{A}\land \mathsf{B}{\rrbracket}^R_v\) is a subset of \({\llbracket}\mathsf{A}{\rrbracket}\), and this means that any time \({{\medsquare}}(\mathsf{A\supset C})\) is true, so is \({{\medsquare}}(\mathsf{(A\land B)\supset C})\). After all, if all \(\mathsf{A}\)-worlds are in the red quadrant of  Figure 4,  so are all of the \(\mathsf{A\land B}\)-worlds, since the \(\mathsf{A\land B}\)-worlds are just a subset of the \(\mathsf{A}\)-worlds. A crucial point here is that on this semantics the domain of worlds quantified over by a counterfactual is constant across counterfactuals with different antecedents. As will be discussed in  §2.2,  advocates of strict conditional analyses aim to instead capture the non-monotonic behavior of antecedents pragmatically by incorporating it into a model of their context-sensitivity. The most important difference between strict analyses and similarity analyses is that similarity analyses capture this non-monotonicity semantically.