Probabilistic modeling of rational communication with conditionals
This work addresses a gap in formal models for the pragmatic use of conditionals in linguistics and cognitive science, offering a flexible framework that is incremental in building on existing probabilistic methods.
The paper tackles the problem of modeling pragmatic reasoning about indicative conditionals by developing a probabilistic approach that integrates beliefs about world states and speaker production. The result is a model that uniformly explains various attested inferences, such as epistemic inferences and conditional perfection, and addresses puzzles about belief updates in different contexts.
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.