Must, knowledge, and (in)directness

Natural Language Semantics 24 (2):117-163 (2016)
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Abstract

This paper presents corpus and experimental data that problematize the traditional analysis of must as a strong necessity modal, as recently revived and defended by von Fintel and Gillies :351–383, 2010). I provide naturalistic examples showing that must p can be used alongside an explicit denial of knowledge of p or certainty in p, and that it can be conjoined with an expression indicating that p is not certain or that not-p is possible. I also report the results of an experiment involving lotteries, where most participants endorsed a sentence of the form must not-p despite being instructed that p is a possibility. Crucially, endorsement was much higher for must in this context than for matched sentences with knowledge or certainty expressions. These results indicate that the requirements for felicitous use of must are weaker than for know and certain rather than being at least as strong, as the epistemic necessity theory would predict. However, it is possible to account for these data while retaining the key insights of von Fintel and Gillies’ analysis of the evidential component of must. I discuss several existing accounts that could be construed in this way and explain why none is completely satisfactory. I then propose a new model that embeds an existing scalar theory into a probabilistic model of informational dynamics structured around questions and answers.

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Daniel Lassiter
Stanford University

Citations of this work

Assertion, Evidence, and the Future.Dilip Ninan - 2022 - Philosophical Review 131 (4):405-451.
The Dynamics of Argumentative Discourse.Carlotta Pavese & Alexander W. Kocurek - 2022 - Journal of Philosophical Logic 51 (2):413-456.
What ‘must’ adds.Matthew Mandelkern - 2019 - Linguistics and Philosophy 42 (3):225-266.
Generalized Update Semantics.Simon Goldstein - 2019 - Mind 128 (511):795-835.

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References found in this work

Knowledge and its limits.Timothy Williamson - 2000 - New York: Oxford University Press.
Knowledge and lotteries.John Hawthorne - 2004 - New York: Oxford University Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
Elusive knowledge.David Lewis - 1996 - Australasian Journal of Philosophy 74 (4):549 – 567.
Knowledge and Its Limits.Timothy Williamson - 2000 - Philosophy 76 (297):460-464.

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