 A second approach gives “semantic values” to conditionals as follows: \(1 (=\) true) if \(A \amp B\); \(0 (=\) false) if \(A \amp{\sim}B\); \(\mathbf{p}_A (B)\) if \({\sim}A\). See van Fraassen (1976), McGee (1989), Jeffrey (1991), Stalnaker and Jeffrey (1994), Sanfilippo et al. (2020). Thus we have a belief-relative three-valued entity. Its probability is its “expected value”. For instance, I’m to pick a ball from a bag. 50% of the balls are red. 80% of the red balls have black spots. Consider “If I pick a red ball \((R)\) it will have a black spot \((B)\)”. \(\mathbf{p}_R (B) = 80\)%. If \(R \amp B\), the conditional gets semantic value 1, if \(R \amp{\sim}B\), it gets semantic value 0. What does it get if \({\sim}R\)? One way of motivating this approach is to treat it as a refinement of Stalnaker’s truth conditions. Is the nearest \(R\)-world a \(B\)-world or not? Well, if I actually don’t pick a red ball, there isn’t any difference, in nearness to the actual world, between the worlds in which I do; but 80% of them are \(B\)-worlds. Select an \(R\)-world at random; then it’s 80% likely that it is a \(B\)-world. So “If \(R, B\)” gets 80% if \({\sim}R\). You don’t divide the \({\sim}R\)-worlds into those in which “If \(R, B\)” is true and those in which it is false. Instead the conditional gets value 80% in all of them. The expected value of “If \(R, B\)” is