Probabilistic semantics for epistemic modals: Normality assumptions, conditional epistemic spaces and the strength of must and might

The epistemic modal auxiliaries must and might are vehicles for expressing the force with which a proposition follows from some body of evidence or information. Standard approaches model these operators using quantificational logic, but probabilistic becoming increasingly influential. According to traditional view, is maximally strong operator bare possibility one. A competing account—popular amongst proponents probabilisitic turn—says that, given evidence, $$\phi $$ entails that $$Pr(\phi )$$ high non-maximal significantly greater than 0. Drawing on several observations concerning behavior must, similar in evidential contexts, deductive inferences, downplaying retractions scenarios, expressions tension, I argue those two influential accounts have systematic descriptive shortcomings. To better make sense their complex behavior, propose instead broadly Kratzerian account according ) = 1$$ > 0$$ , set normality assumptions about world. From this perspective, common mode reasoning whereby we draw inferences specific bits against rich background assumptions—some represent as defeasible—which capture our general expectations will show predictions can be substantially refined once it combined yet independently motivated ‘grammatical’ approach computation scalar implicatures. Finally, discuss implications results more discussions empirical theoretical motivation adopt semantic framework.