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On the semantics and pragmatics of epistemic vocabulary

Paper · 2015 · Sarah Moss

Summary

Moss tackles how to give a meaning to epistemic words such as might, must, probably and indicative if, especially when they are stacked inside each other ("probably a possible hire"), placed under disjunction, or used inside classically valid argument forms. Instead of treating a sentence's meaning as a set of possible worlds, she assigns each sentence a set of probability measures: the credal states that "accept" it. Epistemic operators carry indices tied to contextually supplied partitions of logical space. An operator evaluates the measure on each cell of the partition. This makes nested modals express opinions about which of several candidate opinions is right.

The paper first collects linguistic data and turns it into desiderata. It then builds the semantics, adds a pragmatics of assertion, and proves formal results in appendices. The main payoff is one theory that explains several phenomena:

  • why nested modals signal unsettled opinion,
  • why Yalcin-style "not p and might p" stays bad under supposition,
  • why modus tollens and constructive dilemma fail with epistemic vocabulary while remaining "quasi-valid."

Key points

  • Semantic values are sets of probability measures, not sets of worlds. Epistemic vocabulary is indexed to context-given partitions (g_c(i)) of possibilities.
  • The data include nested modals. Example (5), for instance, pairs the claim that hiring Alice is likely with the claim that it might be likely that Bob gets hired. These are backed by attested examples from Austen 1818 and Washington's 1776 address.
  • The paper engages with Yalcin 2007, Schroeder 2012, Kolodny & MacFarlane 2010, Yalcin 2012a and Rothschild 2012 (imprecise credences).
  • The appendices use a die model (low/high, odd/even) to prove that modus tollens and constructive dilemma are invalid with epistemic premises.
  • Both forms are proved quasi-valid, meaning they hold whenever every partition cell gets positive probability. "Chancy modus ponens" (probably P, if P then Q, so probably Q) is proved fully valid.

Interesting ideas

  • I1. Nested modals can be read as uncertainty over which information source to trust. "Probably might" means most of several sources allow it, which gives a concrete, checkable model of layered hedging.
  • I2. Meanings are constraints on belief states rather than on facts. This explains why "it isn't John, but it might be" clashes even inside a supposition.
  • I3. Epistemic operators are relativized to a contextually chosen partition (the question under discussion). The same sentence can then come out different under different questions.
  • I4. Validity is split into full validity and quasi-validity (validity when no live alternative has zero probability). This pins down exactly when an inference rule is safe.
  • I5. Each claimed invalidity is backed by an explicit small countermodel, the die example. This is a cheap and transparent way to test inference rules.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): Store hedged claims as a distribution over source-opinions instead of a single flag, so stacked modals keep distinct strengths.
  • I2 (frames): The speech-act layer could check whether an utterance's modal content is coherent with the speaker's asserted beliefs and flag Yalcin-style clashes as anomalies.
  • I3 (world): Attach a "question partition" to each modal frame so explanations can say which alternatives a "probably" was computed over.
  • I4 (answer): A reasoning step that chains modal premises via modus tollens or dilemma should be labelled conditional or defeasible, while chancy modus ponens can be applied freely.
  • I5 (learn): Build tiny toy-world countermodels as negative controls when evaluating whether learned inference or repair rules wrongly license epistemic conclusions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of MichiganAnn Arbor · United States36 works · 12,181 citations

📚 Shelves 5 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Philosophy · 211 works

Cite

Cite as Sarah Moss. 2015. On the semantics and pragmatics of epistemic vocabulary. Semantics and Pragmatics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3765/sp.8.5

@article{moss2015semantics,
  title = {{On the semantics and pragmatics of epistemic vocabulary}},
  author = {Sarah Moss},
  journal = {Semantics and Pragmatics},
  year = {2015},
  doi = {10.3765/sp.8.5},
  url = {https://doi.org/10.3765/sp.8.5}
}

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