Probabilistic modeling of rational communication with conditionals

Britta Grusdt*, Daniel Lassiter , Michael Franke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

While a large body of work has scrutinized the meaning of conditional sentences, considerably less attention has been paid to formal models of their pragmatic use and interpretation. Here, we take a probabilistic approach to pragmatic reasoning about indicative conditionals which flexibly integrates gradient beliefs about richly structured world states. We model listeners’ update of their prior beliefs about the causal structure of the world and the joint probabilities of the consequent and antecedent based on assumptions about the speaker’s utterance production protocol. We show that, when supplied with natural contextual assumptions, our model uniformly explains a number of inferences attested in the literature, including epistemic inferences, conditional perfection and the dependency between antecedent and consequent of a conditional. We argue that this approach also helps explain three puzzles introduced by Douven (2012) about updating with conditionals: depending on the utterance context, the listener’s belief in the antecedent may increase, decrease or remain unchanged.
Original languageEnglish
Article number13
Number of pages53
JournalSemantics and Pragmatics
Early online date12 Oct 2022
Publication statusE-pub ahead of print - 12 Oct 2022

Keywords / Materials (for Non-textual outputs)

  • indicative conditionals
  • Rational-Speech-Act model
  • computational pragmatics
  • causal Bayes nets
  • inferentialism


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